Does ChatGPT-Enhanced Collaborative Learning Foster Critical Thinkingin Education? A Bloom’s Taxonomy Perspective

Does ChatGPT-Enhanced Collaborative Learning Foster Critical Thinkingin Education? 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A Bloom’s Taxonomy Perspective Ixora Javanisa Eunike, Yithro Serang, Andri Dayarana K. Silalahi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6307782/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines how ChatGPT-supported collaborative learning influences critical thinking in education using Bloom’s Taxonomy. Purposive sampling was used to collect data from 665 Indonesian pharmacy students through an online survey. PLS-SEM assessed the direct effects of cognitive processes on critical thinking. NCA identified essential cognitive conditions, while fsQCA explored different cognitive pathways leading to high or low critical thinking. Collaborative learning significantly enhances understanding, applying, and remembering. Understanding has the strongest effect on critical thinking, while applying and remembering have moderate effects. These findings suggest that deep comprehension drives analytical reasoning, whereas applying and remembering serve complementary roles. NCA confirms that understanding and applying are necessary for fostering critical thinking, while remembering plays a supporting role. fsQCA results indicate that students who combine deep understanding with memory retention exhibit strong critical thinking. In contrast, students who rely solely on remembering without comprehension or application struggle to develop higher-order reasoning. This study reveals that ChatGPT does not inherently enhance critical thinking but must be integrated into structured collaborative learning. Effective AI-assisted education requires active discussion, application, and critical evaluation of AI-generated insights. These findings offer a framework for optimizing AI-driven pharmacy education to support both knowledge acquisition and analytical reasoning in clinical decision-making. ChatGPT-Assisted Learning Collaborative Learning Critical Thinking Bloom’s Taxonomy Pharmacy Education Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Pharmacy education requires more than just theoretical knowledge. Students must develop cognitive skills that help them analyze pharmaceutical information and apply it in clinical decision-making (Das et al., 2022 ). Lower-order thinking skills (LOTS)—remembering, understanding, and applying—form the foundation for higher-order reasoning (Bloom, 1956 ). However, transitioning from LOTS to critical thinking requires structured learning strategies that promote active engagement with knowledge. Collaborative learning supports this process by encouraging students to discuss concepts, integrate diverse perspectives, and strengthen analytical reasoning (Rajiah, 2025 ; Carstensen et al., 2020 ). With the rise of artificial intelligence (AI), particularly ChatGPT, pharmacy education has access to new tools that provide instant information, personalized feedback, and clinical case simulations (Zhang et al., 2024b ). However, a key challenge remains: ensuring that these technologies do more than just provide information. AI must actively support cognitive development and help students strengthen critical thinking for real-world decision-making. Despite the growing adoption of AI in pharmacy education, a significant gap remains in understanding how AI truly shapes students’ cognitive development. Most research has focused on AI’s ability to improve information accessibility and support independent learning (Wang et al., 2023 ; Wang et al., 2024 ). However, few studies have empirically investigated how AI-assisted collaborative learning contributes to the transition from LOTS to critical thinking. Heung, & Chiu, ( 2025 ) and Gurnani, & Kaur, ( 2024 ) found that while collaborative learning increases student engagement, its direct influence on analytical reasoning remains unclear. Additionally, many discussions surrounding AI assume that its integration will inherently enhance cognitive skills, despite the fact that, without the right pedagogical structure, AI may reinforce passive learning and memorization rather than deep understanding (Morrell et al., 2021 ). Further complicating this issue is the lack of faculty readiness in implementing AI-based instruction grounded in Bloom’s Taxonomy and concerns regarding algorithmic bias and unequal access to AI tools. Therefore, a more comprehensive investigation is needed to determine the conditions under which AI-assisted collaborative learning can truly promote critical thinking rather than simply functioning as an advanced information retrieval system. This study aims to bridge this gap by investigating how ChatGPT-supported collaborative learning supports cognitive development in pharmacy students, especially in transitioning from LOTS to critical thinking during clinical decision-making. This cognitive transition has been difficult to bridge through traditional instructional methods, which often reinforce surface-level knowledge retention instead of emphasizing deep analytical reasoning. It remains unclear whether ChatGPT-enabled collaborative learning promotes more than just improved recall and comprehension through AI-assisted learning. Therefore, this study asks: How does ChatGPT-based collaborative learning influence pharmacy students’ cognitive processes in developing critical thinking for clinical decision-making? In this question, we examine whether AI-enhanced interactions aid conceptual understanding, strengthen students' abilities to apply knowledge in real-life scenarios, and support structured reasoning. Beyond understanding AI’s direct influence on cognitive processes, it is equally important to explore the conditions under which AI-assisted learning effectively cultivates critical thinking. Cognitive skill development is not a uniform process; different students may require distinct learning pathways to transition from LOTS to higher-order reasoning. While some students may rely on deep comprehension to analyze AI-generated clinical recommendations, others may develop critical thinking by actively applying AI-generated knowledge in decision-making tasks. Identifying these distinct cognitive pathways is essential for designing AI-based learning environments that accommodate diverse learning styles. To address this, the second research question asks: What cognitive conditions are necessary for fostering critical thinking, and how do different cognitive pathways lead to high or low reasoning in AI-based learning? By mapping these pathways, this study seeks to establish a clearer framework for structuring AI-driven collaborative learning that effectively supports students in developing independent analytical reasoning. This study investigates how AI-assisted collaborative learning facilitates the progression from LOTS to critical thinking in pharmacy education. To achieve this, SEM evaluates the extent to which collaborative learning enhances remembering, understanding, and applying, and how these cognitive processes contribute to critical thinking. Necessary Condition Analysis (NCA) identifies the essential cognitive skills required for students to transition from basic knowledge acquisition to deeper analytical reasoning. To translate these insights into actionable strategies, fuzzy set Qualitative Comparative Analysis (fsQCA) maps different cognitive pathways leading to high or low critical thinking, offering practical solutions for AI-enhanced pharmacy education. By integrating these methods, this study provides a comprehensive perspective on AI’s role in learning, ensuring that AI tools are leveraged to strengthen critical thinking rather than merely delivering information. The findings of this study contribute both theoretically and practically to pharmacy education. Theoretically, this study challenges the conventional view that remembering, understanding, and applying function as sequential cognitive stages; instead, it highlights their interconnected role in developing critical thinking (Nkhoma et al., 2017 ; Arien-Zakay, 2024 ). Practically, this research offers actionable insights for educators on how AI-assisted collaborative learning can be structured to not only facilitate content delivery but also support engagement, critical assessment, and clinical application of knowledge. By defining the conditions under which AI enhances cognitive development, this study provides an evidence-based framework for designing AI-integrated pharmacy curricula that cultivate students’ analytical reasoning skills while maintaining their cognitive independence. Ultimately, these insights can guide institutions in implementing AI-driven learning environments that balance technological advancements with pedagogical strategies, ensuring students develop the competencies required for complex clinical decision-making. 2. Literature Review 2.1. Previous studies and gaps Previous research has demonstrated the importance of collaborative learning shown in Table 1 , Bloom's Taxonomy, and critical thinking in pharmacy education for improving student engagement and problem-solving skills. Collaborative learning has been recognized as a valuable pedagogical tool in fostering critical thinking, especially in interprofessional and interdisciplinary contexts where students apply theoretical knowledge to practical, real-world situations (Abdulhalim et al., 2011 ; Englin & Cillessen, 2023). Bloom’s Taxonomy offers a structured framework for cognitive development, guiding learners from foundational tasks such as remembering and understanding to advanced skills like analyzing and creating (Arien-Zakay, 2024 ). For example, Kulikovskikh et al. ( 2017 ) demonstrated how clicker-based collaborative learning enhances higher-order thinking and reduces guessing during assessments. However, their approach was limited to clicker usage, lacking broader applicability. Similarly, Nkhoma et al. ( 2017 ) explored case-based learning integrated with Bloom’s Taxonomy, showing improvements in cognitive skills and practical knowledge application. However, their study did not address the progression from lower-order to higher-order thinking comprehensively. Despite these advancements, significant gaps persist in integrating lower-order thinking skills within collaborative learning frameworks. Many curricula focus heavily on higher-order skills, such as evaluation and creation, while neglecting foundational stages like remembering and understanding, which are essential for long-term knowledge retention and application (Duncan-Hewitt & Austin, 2005 ). For instance, Cheng et al. ( 2021 ) examined collaborative learning in flipped classrooms, emphasizing its impact on learning satisfaction and critical thinking. However, their research did not directly assess cognitive progression, leaving gaps in understanding how foundational skills contribute to critical thinking development. Addressing these challenges, this study introduces an AI-enhanced collaborative learning framework that integrates Bloom’s lower-order thinking skills to support critical thinking. By leveraging AI tools such as ChatGPT, this research aims to analyze how structured collaborative learning fosters the progression from foundational knowledge to advanced cognitive tasks, bridging the gaps identified in prior studies while equipping pharmacy students with essential skills for academic and professional success. Table 1 Previous Studies and Gaps Reference Context Objective Contribution Identified Gaps This Study’s Contribution Kulikovskikh et al. ( 2017 ) Using clickers to facilitate collaborative learning Improving higher-order thinking while minimizing guessing during assessments Facilitates critical thinking by managing tasks effectively Restricted to clickers and lacks wider applicability Broadens the application of collaborative learning across multiple areas in pharmacy education Nkhoma et al. ( 2017 ) Case-based learning integrated with Bloom’s Taxonomy Assessing the development of cognitive skills through organized case analysis Improves higher-order thinking and the practical application of knowledge Does not emphasize the progression from lower-order to higher-order thinking Analyze the progression from lower- to higher-order thinking through organized collaborative learning Cheng et al. ( 2021 ) Collaborative learning within flipped classroom settings Examining learning satisfaction in relation to personality traits and collaborative efforts Emphasizes the impact of collaborative learning on satisfaction and the development of critical thinking skills Fails to directly evaluate cognitive progression Examines the direct impact on Bloom’s Taxonomy and the development of critical thinking This Study AI-enhanced pharmacy education Redefining critical thinking via AI-integrated Bloom’s Taxonomy (lower Order thinking) Integrates AI into Bloom’s Taxonomy (lower order thinking) to foster critical thinking Limited AI-enhanced collaborative learning research Develops an AI-based framework for collaborative learning and critical thinking 2.2. ChatGPT in pharmacy Education The integration of ChatGPT into pharmacy education is reshaping how students engage with learning materials. Acting as a virtual teaching assistant, ChatGPT enhances comprehension by simplifying complex pharmaceutical concepts and offering immediate feedback (Zhang et al., 2024a ). Instructors use it to generate assessments, including multiple-choice questions, ensuring consistency in evaluation while reducing manual workload (Ahmed et al., 2024 ). However, concerns about content accuracy persist, as AI-generated explanations may require expert validation to avoid misinformation (Sridharan & Sequeira, 2024 ). Additionally, while many students rely on ChatGPT for assignments and studying, excessive dependence on AI can weaken critical thinking skills (Orok et al., 2024 ). A structured integration within pharmacy curricula, guided by human oversight, is necessary to maximize ChatGPT’s potential while mitigating risks (Abu Hammour et al., 2023 ; Li et al., 2023 ). Beyond its role in content delivery, ChatGPT contributes to skill development in pharmacy education, particularly in clinical reasoning and patient counseling. By simulating patient interactions, it allows students to practice real-world decision-making, enhancing their ability to assess symptoms and provide tailored medication advice (Zhang et al., 2024a ). This interactive learning approach fosters deeper engagement and supports students in applying theoretical knowledge to practical scenarios (Ahmed et al., 2024 ). Additionally, AI-driven chatbots have demonstrated effectiveness in improving medication adherence and patient education, extending their utility beyond academic settings (Abu Hammour et al., 2023 ). However, ethical concerns, including data privacy and the risk of bias in AI-generated content, highlight the need for careful regulation and instructor guidance (Li et al., 2023 ). As pharmacy education continues evolving with AI, balancing technology integration with traditional pedagogical methods will be crucial in ensuring effective learning outcomes. 2.3. Critical Thinking Critical thinking remains a core skill in education, allowing students to analyze, interpret, and assess information to solve problems effectively (Vincent-Lancrin, 2023 ). In pharmacy education, it plays a vital role in shaping clinical reasoning, enabling future pharmacists to evaluate drug interactions, assess patient conditions, and make sound treatment decisions (Aktoprak & Hursen, 2022 ). However, despite its importance, many pharmacy programs have yet to fully develop students' critical thinking abilities, as rote memorization often takes precedence over deep analysis (Araújo et al., 2024 ). Educators have introduced structured approaches such as problem-based learning, case studies, and simulations to encourage analytical thinking, but their success depends largely on how well they are integrated into the curriculum and how actively students engage with them (Prokop-Dorner et al., 2024 ). To equip students for real-world clinical decision-making, pharmacy education must adopt teaching strategies that not only impart knowledge but also push students to question, evaluate, and apply their learning critically. AI-driven tools like ChatGPT are increasingly integrated into pharmacy education, offering students interactive learning experiences that enhance knowledge retention and application (Zhang et al., 2024b ). These tools assist in analyzing pharmaceutical case studies, evaluating prescription guidelines, and simulating patient interactions, thereby strengthening diagnostic reasoning (Reffhaug & Lysgaard, 2024 ). AI can support inquiry-based learning by prompting students to justify treatment decisions, explore alternative solutions, and refine their understanding of clinical concepts (García-Moro et al., 2021 ). However, without structured oversight, there is a risk that students may become overly dependent on AI-generated content, passively accepting responses rather than engaging in critical analysis (Abu Hammour et al., 2023 ). To counter this, pharmacy curricula should be designed to balance AI integration with activities that encourage independent problem-solving and metacognitive reflection (Li et al., 2023 ). While AI enhances learning efficiency, it cannot replace the cognitive depth required for critical thinking in pharmacy education (Aktoprak & Hursen, 2022 ). Over-reliance on AI-generated recommendations may weaken students’ ability to assess clinical data independently, reducing their confidence in making complex decisions (Kerruish, 2023). Additionally, AI systems may introduce biases or inaccuracies, reinforcing the need for students to develop strong evaluative skills to verify information critically (Huang & Sang, 2023 ). Ethical considerations, such as patient safety and the responsible use of AI in healthcare, must also be incorporated into pharmacy training to ensure that students understand both the benefits and limitations of AI assistance (García-Moro et al., 2021 ). Ultimately, AI should function as a complementary tool that reinforces, rather than replaces, critical thinking, ensuring pharmacy graduates possess both technological proficiency and independent analytical skills (Li et al., 2023 ). 2.4. Lower Order Thinking in Bloom Taxonomy Bloom’s Taxonomy categorizes lower-order thinking skills—Remembering, Understanding, and Applying—as the building blocks of cognitive development and knowledge retention. At its core, Remembering requires students to recall facts, recognize key concepts, and retrieve previously learned information, ensuring they have a solid foundation for further learning (Vincent-Lancrin, 2023 ). Once these basics are established, students move to Understanding, where they interpret, process, and articulate concepts in their own words, allowing for deeper engagement with the material (Aktoprak & Hursen, 2022 ). Applying marks the transition from theoretical knowledge to practical use, as students begin implementing learned concepts in real-world situations, reinforcing their ability to apply information effectively (Araújo et al., 2024 ). These cognitive skills are essential for progression to higher-order thinking, where students analyze, evaluate, and create new knowledge through critical reasoning and problem-solving (Prokop-Dorner et al., 2024 ). The structured approach of Bloom’s Taxonomy helps educators design curricula that support student learning progression. By prioritizing lower-order thinking, instructors ensure that students first establish a solid conceptual framework before advancing to complex cognitive tasks (García-Moro et al., 2021 ). The emphasis on remembering key facts, understanding core principles, and applying knowledge in different contexts enhances comprehension and retention, making learning more meaningful (Huang & Sang, 2023 ). Additionally, these skills are particularly relevant in pharmacy education, where accurate recall of medical information, conceptual understanding of drug interactions, and the ability to apply knowledge in clinical settings are critical for professional competence (Abu Hammour et al., 2023 ). Mastery of lower-order thinking allows students to develop confidence in their learning process, ultimately preparing them to engage with more advanced problem-solving and decision-making tasks (Li et al., 2023 ). 2.5. Collaborative learning Collaborative learning strengthens pharmacy education by fostering teamwork, enhancing student engagement, and deepening conceptual understanding. Unlike passive instructional methods, this approach actively involves students in discussions, case analyses, and problem-solving exercises, reinforcing their ability to navigate real-world pharmaceutical challenges (Cox et al. , 2012). Interprofessional education (IPE) initiatives, where pharmacy students collaborate with peers from other healthcare fields, further refine their decision-making and patient care competencies (Fusco et al., 2024 ). The use of collaborative testing, in which students first complete exams individually before retaking them in groups, has demonstrated improvements in academic performance and knowledge retention, supporting the argument that learning is most effective when students engage with and challenge each other’s reasoning (Prescott et al., 2024 ). However, effective implementation requires structured facilitation to ensure that collaboration remains purposeful, preventing imbalanced participation and ensuring equal cognitive engagement across teams (Abdulhalim et al., 2011 ). The integration of Bloom’s Taxonomy into collaborative learning amplifies its impact by structuring cognitive development across progressive levels. Educators can design group activities that transition students from recalling basic pharmaceutical facts to critically evaluating treatment options and formulating evidence-based recommendations (Kim et al. , 2012). For instance, in pharmacy curricula, collaborative exercises such as case-based discussions encourage students to analyze patient scenarios, assess therapeutic alternatives, and justify clinical decisions—tasks aligned with the higher tiers of Bloom’s hierarchy (Valcke et al., 2009 ). Writing Across the Curriculum (WAC) programs have also demonstrated the value of collaborative learning, engaging students in discussions that stimulate advanced cognitive processing (Randolph, 2000 ). Nevertheless, the alignment of collaborative learning with Bloom’s framework must be consistent across diverse educational settings to maximize its benefits, requiring ongoing refinement and interdisciplinary cooperation (Owen et al., 2011 ). 2.6. Research focus area and theoretical framework This study’s conceptual framework emphasizes the foundational role of lower-order thinking within Bloom’s Taxonomy—Remember, Understand, and Apply—in collaborative learning settings for pharmacy education. These cognitive stages are critical for building a solid knowledge base that enables students to effectively progress toward higher-order skills. Collaborative learning enhances this process by encouraging students to recall essential facts, interpret core concepts, and apply knowledge in practical, real-world scenarios. The structured integration of Bloom’s lower-order levels ensures that students acquire the foundational skills needed to approach more advanced cognitive tasks with confidence (Krathwohl, 2002 ; Shaikh et al., 2021 ). As illustrated in Fig. 1 , the focus on lower-order thinking addresses a significant gap in pharmacy education, where students often rely on memorization rather than developing a deeper understanding. Collaborative activities, such as group-based discussions and applied problem-solving tasks, allow students to process and contextualize learned material, reinforcing comprehension and retention (Zhang et al., 2024b ; Cain & Rajan, 2024 ). By engaging with these lower cognitive levels, students are equipped to bridge the gap between theoretical knowledge and practical application, ensuring their readiness for the complexities of clinical practice. Embedding collaborative learning within Bloom’s foundational tiers not only enhances educational outcomes but also ensures a robust platform for future cognitive and professional growth (Hamid et al., 2023 ; Chhatrala & Chaudhari, 2024 ). This provides a more engaging learning experience for students while cultivating the analytical agility essential for effective clinical reasoning in real-world healthcare settings as shown in Fig. 2 . 3. Hypothesis 3.1. Collaborative Learning and Lower Order Thinking Collaborative learning fosters an interactive learning environment where students engage in shared problem-solving, enhancing their cognitive abilities through peer discussions and knowledge exchange (Zheng et al., 2024 ). This approach is particularly beneficial in pharmacy education, where students must integrate theoretical knowledge with clinical applications. The incorporation of ChatGPT further optimizes collaborative learning by serving as a virtual teaching assistant, facilitating structured dialogue, and supporting interdisciplinary learning (Fui-Hoon Nah et al., 2023 ). By offering instant explanations, structured prompts, and personalized learning pathways, ChatGPT enhances engagement and information retention. However, while AI-driven collaboration improves lower-order thinking skills, excessive reliance on AI-generated content may weaken independent reasoning, necessitating strategic integration within pharmacy curricula (Gonsalves, 2024 ). With ChatGPT, students are able to remember pharmaceutical knowledge more effectively by using peer interaction in conjunction with AI-generated support. Remembering, as the first level of Bloom’s Taxonomy, involves recalling key concepts such as drug classifications, mechanisms of action, and clinical guidelines. In a collaborative setting, students strengthen their memory by discussing and explaining concepts to each other, reinforcing their understanding through active engagement. ChatGPT enhances this process by providing mnemonic aids, interactive flashcards, and AI-generated summaries, making it easier for students to retrieve and retain information (Ruiz-Rojas et al., 2024 ). Additionally, AI-driven explanations allow students to check their recall accuracy instantly, helping them refine their understanding (Zhang et al., 2024b ). However, if students rely too much on AI-generated content, they may remember information only at a surface level without deeper processing. To prevent this, collaborative learning with ChatGPT should be structured to encourage active discussion, self-explanation, and problem-solving, ensuring that students develop strong and lasting recall abilities (Al Shloul et al., 2024 ). Thus, this study proposes: H1: Collaborative learning supported by ChatGPT enhances students' ability to remember pharmaceutical knowledge Understanding involves more than just remembering facts; it requires students to interpret, explain, and apply concepts in different contexts. Collaborative learning helps develop this skill by encouraging students to engage in discussions where they clarify ideas, challenge assumptions, and refine their understanding through peer interaction (Hui, 2024 ). This interactive process ensures that students do not just memorize information but also learn to connect and use it effectively. ChatGPT further supports this learning approach by providing clear explanations, structured summaries, and real-world examples, helping students view information from different perspectives and reinforcing comprehension (Tu & Hwang, 2024 ). However, true understanding requires active participation, and relying too much on AI-generated content without critical engagement may limit deep learning. To maximize the benefits, students must interact with both their peers and AI, analyzing, questioning, and applying the information rather than just accepting it as presented (Al-Dujaili et al., 2023 ). In this way, collaborative learning, combined with AI-driven support, strengthens students' ability to understand complex concepts by promoting active engagement and deeper cognitive processing. Thus, this study proposes: H2: Collaborative learning supported by ChatGPT enhances students' understand of complex pharmaceutical concepts. Students work together to tackle clinical cases that ChatGPT generates, encouraging them to use what they’ve learned and apply it to practical situations. This group learning process allows students to discuss, question, and challenge each other’s ideas, creating a space where everyone can engage deeply with the material. As they work through these clinical scenarios together, they refine their understanding and problem-solving skills (Hamid et al., 2023 ). ChatGPT supports this by providing immediate, personalized feedback that helps students think critically and adjust their approaches. By combining collaboration with AI-driven guidance, students gain a stronger grasp of how theory translates into practice, ultimately bridging the gap between classroom learning and real-world decision-making (Roosan et al., 2024 ). This collaborative approach is key in helping students not only learn but also actively apply their knowledge, making the learning experience richer and more practical. Thus, this study proposes: H3: Collaborative learning with ChatGPT enhances students' ability to apply theoretical knowledge in pharmacy practice. 3.2. Bloom Taxonomy and Critical Thinking Skills Lower-order thinking skills, like remembering, understanding, and applying, lay the groundwork for developing critical thinking. These skills help students recall important information, grasp key concepts, and apply them in different situations. Without mastering these basic steps, students would struggle to tackle more complex tasks like analyzing and evaluating problems. Tools like ChatGPT can be helpful in this process by providing interactive and personalized learning experiences, which make it easier for students to retain and understand the material (Sharma, 2024 ). However, while AI can strengthen these foundational skills, relying too heavily on it might reduce the cognitive effort needed for deeper, independent thinking. To truly foster critical thinking, it’s important that ChatGPT be used in a way that promotes active participation and encourages students to think critically, not just absorb information (Almazrou et al., 2024 ). Remembering, as the first stage in Bloom's Taxonomy, plays a key role in fostering critical thinking, and using ChatGPT can significantly enhance this process. By helping students recall essential facts and concepts, ChatGPT provides a structured way for them to engage with the material, reinforcing their memory through mnemonics and adaptive recall exercises (Herrmann-Werner et al., 2024 ). This active recall, especially when integrated into collaborative learning, encourages students to not just remember, but to apply what they’ve learned, setting the stage for deeper analysis and evaluation. As students retrieve information with the help of ChatGPT, they are forced to engage more critically with the material, making connections between concepts and strengthening their understanding. While remembering may seem basic, when done actively with AI support, it becomes the foundation for developing the higher-order thinking skills needed to analyze and evaluate complex scenarios, ultimately boosting critical thinking (Taesotikul et al., 2024 ). Thus, this study proposes: H4: Remember, enhanced through ChatGPT, positively influences critical thinking. Understanding plays a critical role in bridging the gap between simple knowledge recall and more complex critical thinking. When students use ChatGPT to enhance their understanding, they gain the opportunity to explore concepts through multiple perspectives and explanations, which helps them connect and contextualize theoretical knowledge. By presenting different ways of interpreting information and offering case-based scenarios, ChatGPT enables students to see patterns and relationships that are key to critical thinking (Choi, 2023 ). However, while AI-generated content can improve comprehension, it is not enough to passively absorb information. For understanding to truly lead to critical thinking, students must engage actively with the material, questioning and reflecting on the AI-generated explanations. This engagement encourages independent thought and deeper analysis, ensuring that students do not simply accept AI-driven insights, but critically assess and apply them in meaningful ways (Anderson et al., 2024 ; Bharatha et al., 2024 ). Thus, this study proposes: H5: Understand, enhanced through ChatGPT, positively influences critical thinking . Applying knowledge is a crucial step in developing critical thinking, as it involves using what students have learned to solve real-world problems and make decisions. ChatGPT enhances this process by simulating real-life cases, offering practice scenarios, and providing immediate feedback on problem-solving in pharmaceutical contexts (Suriano et al., 2025 ). Through these AI-driven exercises, students are able to actively engage in decision-making, testing their ability to apply knowledge in various situations (Gonsalves, 2024 ). However, while ChatGPT helps guide the application of knowledge, it cannot replace the depth of human judgment. Therefore, it is important for students to critically evaluate the AI-generated solutions, ensuring they develop independent reasoning and decision-making skills (Taesotikul et al., 2024 ). In summary, ChatGPT can support the application of knowledge, but its impact on critical thinking depends on how actively students engage and reflect on their decisions. Thus, this study proposes: H6: Apply, enhanced through ChatGPT, positively influences critical thinking. 4. Methods 4.1. Research Design and Process Exploring how lower-order thinking skills (LOTS) in Bloom’s Taxonomy contribute to critical thinking is key to understanding the role of ChatGPT in pharmacy education. While ChatGPT helps students remember, understand, and apply knowledge, its ability to foster deeper analytical thinking is still unclear. A major concern is that students might rely too heavily on AI-generated content, which could lead to passive learning instead of active engagement. This brings up an important question: Can ChatGPT-driven LOTS naturally lead to critical thinking, or do students need additional learning strategies to make that transition? By addressing this, the study provides insights into how AI can be used more effectively to support meaningful cognitive development rather than just delivering information. Establishing a clear link between lower-order thinking skills (LOTS) and critical thinking requires a solid theoretical foundation. To achieve this, a systematic literature review was conducted using Google Scholar, Scopus, and Web of Science, focusing on AI in education, Bloom’s Taxonomy, and critical thinking development. While prior studies recognize ChatGPT’s role in improving knowledge recall and comprehension, they offer limited insights into whether AI-driven LOTS can effectively transition into higher-order cognitive skills (Hu et al., 2023 ). Additionally, much of the existing research prioritizes AI’s ability to organize and present information, rather than investigating whether it actively supports students in developing critical reasoning skills. These gaps highlight the need to evaluate ChatGPT’s effectiveness not just as a tool for reinforcing LOTS, but as a potential enabler for deeper intellectual engagement in pharmacy education. To address these gaps, this study explores whether ChatGPT-driven lower-order thinking skills (LOTS) can help students develop critical thinking in pharmacy education. It looks at how AI-assisted remember, understand, and apply of knowledge provide a foundation for deeper reasoning. Instead of treating ChatGPT as just a tool for retrieving information, this research considers how it can actively support students’ cognitive growth. By drawing on educational theories and AI-based learning models, the study aims to find practical ways to make ChatGPT more effective in strengthening critical thinking skills in pharmacy students. 4.2. Operationalization and Measures 4.2.1 Collaborative learning Collaborative learning is an active educational method where students work together in discussions, problem-solving, and shared knowledge creation, prioritizing teamwork and critical thinking over passive absorption of information (Cain & Rajan, 2024 ; Hamid et al., 2023 ). In this study, it involves pharmacy students collaborating to grasp concepts and address challenges, supported by ChatGPT as an AI tool that facilitates interaction and knowledge exchange. ChatGPT offers personalized assistance, delivers immediate feedback, and supports activities such as virtual tutoring, test preparation, and clinical scenario analysis. The effectiveness of collaborative learning in this context is measured through four dimensions: teamwork, problem-solving, knowledge sharing, and engagement. These metrics assess how ChatGPT enhances collaboration, deepens cognitive understanding, and promotes efficient learning, underscoring its contribution to a dynamic, technology-driven educational framework in pharmacy education. 4.2.2 Bloom Taxonomy Lower-order thinking skills (LOTS), as defined in Bloom's Taxonomy, encompass remembering, understanding, and applying, forming the foundation for cognitive development in pharmacy education. These skills are essential for students to establish a strong knowledge base that enables progression to higher-order thinking tasks such as analysis and evaluation. In pharmacy education, LOTS ensures students grasp fundamental concepts, facilitating their ability to navigate more complex clinical scenarios. By incorporating Bloom’s framework, educators can structure learning objectives to progressively develop students' cognitive abilities, emphasizing the importance of foundational skills for effective pharmacy practice (Ramirez, 2017 ; Hui, 2024 ). This study operationalizes each LOTS level to evaluate its role in building a comprehensive educational foundation for pharmacy students. Remember refers to the ability to recall and retrieve facts or basic concepts accurately. In pharmacy education, this involves memorizing drug names, mechanisms of action, classifications, and common side effects, which are vital for ensuring safe and effective patient care (Bharatha et al., 2024 ). In this study, remembering is measured through assessments that test students' ability to recall essential pharmaceutical knowledge, such as identifying drug interactions or naming medications in specific therapeutic classes. These measures evaluate the extent to which students can access foundational knowledge necessary for clinical decision-making. The emphasis on remembering ensures that students build a solid knowledge repository, enabling them to respond effectively in practice-oriented settings. Understand, the second LOTS level, involves interpreting, summarizing, and explaining concepts. In pharmacy education, understanding enables students to make connections between pharmacological data and its clinical implications, such as explaining the effects of drug interactions or summarizing therapeutic protocols (Taesotikul et al., 2024 ). In this study, understanding is operationalized through tasks that require students to explain drug mechanisms or interpret patient case studies. Assessments include written responses and group discussions where students demonstrate their comprehension by connecting theoretical knowledge to practical contexts. This level plays a critical role in bridging factual recall with deeper cognitive engagement, preparing students for informed clinical decision-making. Apply involves using acquired knowledge in practical situations, such as solving case studies or adjusting treatment plans based on specific patient needs. In pharmacy education, application tasks train students to translate theoretical concepts into actionable decisions, such as calculating dosages or tailoring therapies to patient-specific conditions (Roosan et al., 2024 ). In this study, applying is measured through simulations, role-playing scenarios, and problem-based learning exercises where students demonstrate their ability to implement pharmaceutical knowledge effectively. These assessments reflect real-world challenges, ensuring students develop the practical skills required for professional practice. By focusing on application, this study highlights the importance of translating foundational knowledge into actionable outcomes, a critical step toward professional competency in pharmacy. 4.2.3 Critical Thinking Critical thinking is defined as the ability of pharmacy students to actively engage with AI-generated information, assess its credibility, and apply logical reasoning in clinical decision-making (Altun & Yildirim, 2023 ). Rather than passively accepting AI outputs, students must critically evaluate and integrate information to develop well-reasoned conclusions. This study measures critical thinking through five key indicators: (1) interpreting complex pharmaceutical concepts with AI assistance, (2) integrating diverse perspectives to construct structured arguments, (3) assessing the reliability of AI-generated content, (4) applying logical reasoning to clinical decisions, and (5) identifying patterns and key insights from AI discussions. 4.3. Sampling Technique and Data Collection Procedure This study utilizes purposive sampling, targeting Indonesian pharmacy students in higher education who have used ChatGPT for personalized learning. To ensure relevance, participants were required to meet three criteria: prior ChatGPT usage for collaborative learning, current enrollment in a higher education institution, and a minimum age of 18. These conditions were clearly stated at the start of the survey to maintain data integrity. By selecting participants with specific experience, this approach strengthens the study’s ability to generate meaningful insights into AI-driven learning, Bloom’s Taxonomy, and critical thinking (Yusuf et al., 2024 ). Data collection took place online between November 2024 and January 2025, using a structured survey with four sections. The first secured informed consent, while the second screened for eligibility. The third gathered demographic data, and the final section measured key constructs using 28 Likert-scale items. To enhance validity, the instrument was reviewed by educational psychology and technology experts and pilot-tested with pharmacy students before full deployment. The study ultimately obtained 655 valid responses, providing a solid foundation for analyzing AI’s role in cognitive development. 4.4. Analysis Technique This study uses PLS-SEM with SmartPLS 4.0, a method widely recognized for handling complex theoretical frameworks and hypothesis testing (Hair et al., 2017 ). The analysis begins with the measurement model, where construct validity is assessed through convergent validity (measured by AVE and CR) and discriminant validity (evaluated using the Fornell-Larcker criterion and HTMT ratio) (Fornell & Larcker, 1981 ; Henseler et al., 2015 ). To address potential bias from common method variance (CMV), Harman’s Single Factor Test is conducted (Baumgartner et al., 2021 ). Moving to the structural model, R² values determine how well the independent variables explain the outcomes, while f² effect sizes indicate the strength of each predictor (Falk & Miller, 1992 ). Bootstrapping with 10,000 resamples provides a solid foundation for hypothesis testing by improving statistical reliability. Additionally, model fit is checked using SRMR, d_ULS, d_G, and NFI to ensure the framework is well-structured and meaningful (Hu & Bentler, 1998 ). By following this rigorous yet practical approach, the study delivers strong empirical insights into how AI-enhanced collaborative learning supports critical thinking development in pharmacy education through Bloom’s Taxonomy. To deepen the analysis, this study applies NCA and fsQCA. NCA identifies conditions that must be present for critical thinking to develop, where a factor is considered necessary if its consistency is above 0.90 and coverage exceeds 0.50 (Pappas & Woodside, 2021 ). This helps determine whether LOTS play a crucial role in shaping critical thinking. Meanwhile, fsQCA examines different combinations of conditions to identify which factors significantly contribute, which are irrelevant, and which may hinder the outcome. By analyzing consistency and coverage values, fsQCA provides practical insights into the most effective ways to enhance AI-supported learning. The analysis is conducted using fsQCA version 4.1 (Ragin, 2023 ), ensuring that findings go beyond statistical validation and offer practical recommendations for improving critical thinking development in pharmacy education. 5. Results 5.1. Sample Profile This study involved 665 pharmacy students from Indonesia, providing a robust foundation for examining the impact of collaborative learning on lower-order and critical thinking skills (see Table 2 ). Female participants signified the majority of the sample, accounting for 59% (n = 391), while male students made up 41% (n = 274), confirming a diverse range of perspectives. This higher proportion of female students likely reflects the growing trend of women pursuing pharmacy education in Indonesia, where healthcare fields often attract more female students. The largest age group was 18–25 years (55%, n = 368), displaying the typical age for higher education in Indonesia, followed by students aged 26–35 years (38%, n = 254). A smaller proportion of participants were aged 36–45 years (6%, n = 40), with only 0.4% (n = 3) above 45 years. Regarding marital status, 73% of participants were single (n = 487), which allies with the idea that younger students often prioritize education before marriage, while the 27% of married participants (n = 178) may consider older students returning to education for career advancement. Considering educational backgrounds: 46% held undergraduate degrees (n = 305), 32% had senior high school qualifications (n = 214), 17% held diplomas (n = 116), and 5% were postgraduates (n = 31). This diverse educational profile enhances the study's relevance, aligning with Bloom’s Taxonomy by capturing a broad spectrum of cognitive development stages. These varied demographic and educational characteristics enrich the study's findings and reinforce their applicability to pharmacy education, particularly in understanding cognitive skill progression within the framework of Bloom’s Taxonomy. Table 2 Sample Profile Measure Items Frequency Percentage Gender Male 274 0.41 Female 391 0.59 Age (Years Old) 18–25 368 0.55 26–35 254 0.38 36–45 40 0.06 > 45 3 0.004 Marital Status Married 178 0.73 Single 487 0.27 Educational Background Senior High school or equivalent 214 0.32 Diploma 116 0.17 Undergraduate 305 0.46 Postgraduate 31 0.05 5.2. Common Method Variance Identifying potential bias in the data is crucial for ensuring research validity, which is why this study applies Harman’s single-factor test, a widely recognized method for detecting Common Method Variance (CMV). This approach involves loading all measured items onto a single factor to determine whether a dominant variance source exists (Baumgartner et al., 2021 ). A CMV value below 50% signifies that response bias is minimal and that the collected data maintain consistency and validity. The results indicate that eigenvalues range from 0.210 to 8.492, which is significantly lower than the 50% threshold, confirming that CMV is not a concern in this study. Furthermore, an analysis of Variance Inflation Factor (VIF) values shows a range of 25.46 to 67.60, suggesting that the constructs are well-defined and do not suffer from problematic multicollinearity (Hair et al., 2017 ). These findings reinforce the credibility of the research model, ensuring that the observed relationships reflect genuine associations rather than statistical distortions caused by measurement bias. 5.3. Results from SEM 5.3.1 Validity and Reliability Assessment This study conducted a thorough validity and reliability assessment to confirm the robustness of the measurement model (Table 3 ). OL for all items exceeded the 0.70 threshold, indicating strong item involvements to their respective constructs (Hair et al., 2017 ). CA values ranged from 0.770 to 0.915, and CR values fell between 0.897 and 0.937, both exceeding the recommended minimum of 0.70. Furthermore, AVE values for all constructs were above 0.50, with scores such as 0.665 for Collaborative Learning and 0.748 for critical thinking, confirming acceptable convergent validity (Fornell & Larcker, 1981 ). These results demonstrate that the constructs are both reliable and valid, ensuring the measurement model captures the intended concepts accurately. Consequently, the study's findings are grounded in robust statistical foundations, free from concerns of measurement error or inconsistencies. Table 3 Convergent Validity and Reliability Assessment Construct Items OL CA CR AVE Collaborative Learning CL1 0.774 0.874 0.908 0.665 CL2 0.814 CL3 0.858 CL4 0.800 CL5 0.829 Remember RM1 0.893 0.775 0.899 0.816 RM3 0.914 Understand UD1 0.840 0.888 0.922 0.748 UD3 0.881 UD5 0.874 UD11 0.865 Apply AP1 0.897 0.770 0.897 0.813 AP3 0.906 Critical Thinking CT1 0.871 0.915 0.937 0.748 CT2 0.860 CT3 0.854 CT4 0.888 CT5 0.850 Notes: a. OL, Outer Loadings; CA, Cronbach’s Alpha; CR, Composite Reliability; AVE, Average Variance Extracted. b. CL, Collaborative Learning; RM, Remember; UD, Understand; AP, Apply; AN, Analyze; EV, Evaluate; CR, Create; CT, Critical Thinking. c. The threshold for OL > 0.70; CA > 0.70; CR > 0.70 and AVE > 0.50. Discriminant validity was assessed using the Fornell-Larcker criterion and the HTMT method to confirm the distinctiveness of the constructs in the study (Table 4 ). The square root of the AVE for each construct, highlighted in bold, exceeded the highest correlation between the constructs, thereby satisfying the Fornell-Larcker criterion (Fornell & Larcker, 1981 ). For instance, the square root of AVE for Collaborative Learning (0.815) was greater than its highest correlation with any other construct (0.902 with critical thinking). Similarly, critical thinking displayed a square root of AVE (0.865) that surpassed its intercorrelations with other constructs, such as 0.752 with Apply and 0.809 with collaborative learning. These results indicate that the constructs are empirically distinct and that the measurement model ensures strong discriminant validity across all constructs. Table 4 Discriminant Validity of Fornell-Larcker Criterion and HTMT Construct CL RM UD AP CT CL 0.815 0.752 0.918 0.880 0.902 RM 0.622 0.903 0.775 0.708 0.760 UD 0.812 0.645 0.865 0.910 0.941 AP 0.723 0.547 0.752 0.902 0.895 CT 0.809 0.642 0.850 0.752 0.865 Notes: a. The italic values indicates the HTMT with the threshold of < 0.90. b. The bolded values represent the square root of AVE. c. Other values represent intercorrelations between constructs for measuring the Fornell-Larcker criterion In addition, the HTMT values were below the threshold of 0.90, further confirming discriminant validity as proposed by Henseler et al. ( 2015 ). For example, the HTMT value between Collaborative Learning and Understand was 0.918, and between Understand and Apply, it was 0.910, both within acceptable ranges. This demonstrates that the constructs maintain their uniqueness without significant overlap. The combination of Fornell-Larcker and HTMT assessments highlights the integrity of the measurement model, ensuring it is robust and suitable for hypothesis testing. These findings provide a strong foundation for subsequent structural analyses and confirm that the constructs are sufficiently distinct to capture the targeted dimensions effectively. Discriminant validity was evaluated using the cross-loadings matrix, confirming that all indicators loaded more strongly on their respective constructs than on others (Table 5 ). For instance, AP1 and AP3 had higher loadings on Apply (0.897 and 0.906), while CL3 loaded strongly on Collaborative Learning (0.858). These findings affirm construct distinctiveness and measurement validity, supporting their reliability for hypothesis testing (Hair et al., 2017 ). Table 5 Discriminant Validity of Cross-Loadings Matrix AP CL CT RM UD AP1 0.897 0.634 0.666 0.497 0.668 AP3 0.906 0.669 0.690 0.490 0.689 CL1 0.555 0.774 0.585 0.446 0.596 CL2 0.586 0.814 0.630 0.540 0.640 CL3 0.635 0.858 0.754 0.538 0.748 CL4 0.573 0.800 0.641 0.482 0.620 CL5 0.592 0.829 0.676 0.523 0.693 CT1 0.690 0.695 0.871 0.598 0.755 CT2 0.611 0.720 0.860 0.520 0.741 CT3 0.630 0.669 0.854 0.574 0.730 CT4 0.661 0.712 0.888 0.583 0.758 CT5 0.659 0.703 0.850 0.498 0.687 RM1 0.495 0.533 0.548 0.893 0.552 RM3 0.494 0.588 0.610 0.914 0.611 UD1 0.667 0.697 0.690 0.568 0.840 UD11 0.625 0.669 0.710 0.534 0.865 UD3 0.679 0.740 0.766 0.588 0.881 UD5 0.632 0.701 0.770 0.542 0.874 Note: The values printed in italic are the outer loadings 5.3.2 Hypothesis Testing The outcomes defined in Table 6 , Fig. 3 , and Fig. 4 (a-d) display the fundamental role of collaborative learning (CL) in increasing lower-order cognitive processes, including remembering (RM), understanding (UD), and applying (AP). Hypotheses H1 to H3 receive strong support, with CL exerting significant effects on RM (β = 0.622, f² = 0.630, T = 11.184), UD (β = 0.812, f² = 1.936, T = 27.735), and AP (β = 0.723, f² = 1.093, T = 14.717). These results emphasize that collaborative learning activities promote critical foundational skills by engaging learners in group-based interactions that support memory retention, conceptual understanding, and practical application (Fornell & Larcker, 1981 ). Moreover, the substantial effect sizes for UD and AP highlight the effectiveness of collaborative learning in cultivating deeper comprehension and real-world problem-solving, aligning closely with the goals of Bloom's Taxonomy. Further analysis reveals that the transition from lower-order to higher-order cognitive processes is facilitated through these foundational skills. The path coefficients show that RM significantly influences CT (β = 0.098, f² = 0.025, T = 2.623), although with a small effect size, indicating that while memory plays a role in critical thinking, its impact is limited. Conversely, UD demonstrates a strong relationship with CT (β = 0.438, f² = 0.233, T = 6.065), with a moderate-to-large effect size, underscoring the importance of understanding in developing critical thinking abilities. These findings suggest that the ability to comprehend and contextualize information is a crucial precursor to advanced reasoning, consistent with prior studies emphasizing the role of comprehension in higher-order cognitive tasks (Hair et al., 2017 ). Table 6 Hypothesis Testing Results Hyphotesis β f 2 T-Value Bootstrapping CI 97.5% N = 10,000 Decision Min Max H1. CL ◊ RM 0.622*** 0.630 11.184 0.502 0.721 Supported H2. CL ◊ UD 0.812*** 1.936 27.735 0.746 0.861 Supported H3. CL ◊ AP 0.723*** 1.093 14.717 0.612 0.802 Supported H4. RM ◊ CT 0.098** 0.025 2.623 0.029 0.175 Supported H5. UD ◊ CT 0.438*** 0.233 6.065 0.282 0.563 Supported H6. AP ◊ CT 0.178** 0.058 3.066 0.070 0.299 Supported Notes: a. CL, Collaborative Learning; RM, Remember; UD, Understand; AP, Apply; AN, Analyze; EV, Evaluate; CR, Create; CT, Critical Thinking. b. β , Path Coefficient; CI, Confidence Interval. c. Significance level determined by p-value of ***P < 0.001; **P < 0.010; *P < 0.050. Lastly, applying (AP) also significantly impacts CT (β = 0.178, f² = 0.058, T = 3.066), reinforcing the idea that hands-on practice and real-world applications contribute meaningfully to critical thinking development. However, the effect size for AP remains moderate, indicating that while application is valuable, it requires integration with other cognitive skills for optimal impact on critical reasoning. Together, these results validate the structural framework of this study, illustrating how collaborative learning facilitates the progression from foundational cognitive processes to critical thinking. These findings provide empirical support for the integration of collaborative learning strategies into pharmacy education to enhance both lower-order and critical thinking skills. 5.4. Necessary Condition Analysis This study applies PLS-SEM and Necessary Condition Analysis (NCA) to examine how lower-order thinking skills (LOTS)—Remember, Understand, and Apply—contribute to critical thinking in pharmacy education. PLS-SEM explores the relationships between these cognitive processes and critical thinking, while NCA determines which skills are essential for its development. The findings show that understanding is the most crucial factor (d = 0.411, p = 0.000). This means that students must first comprehend concepts before they can critically engage with information. Without a strong foundation in understanding, students may struggle to process ideas effectively. This limitation makes it difficult for them to assess or question information, reducing their ability to think critically. The ability to apply knowledge is also significant (d = 0.409, p = 0.000). Students who actively use what they learn develop stronger critical thinking skills. This suggests that learning should move beyond memorization, as applying concepts in practical situations reinforces deeper cognitive engagement. On the other hand, remembering (d = 0.097, p = 0.000) does not appear to be a strict requirement for critical thinking. While recall is useful for retaining knowledge, simply remembering facts does not automatically lead to deeper reasoning. These findings suggest that AI-assisted learning should prioritize strengthening understanding and application. This approach ensures that students develop the cognitive foundation necessary to transition from LOTS to critical thinking in pharmacy education Table 7 Necessary Condition Analysis Critical Thinking Effect Size (d) (CE-FDH) P-value Collaborative Learning 0.356 0.000 Apply 0.409 0.000 Understand 0.411 0.000 Remember 0.097 0.000 Notes: Effect size of 0 < d < 0.1 is small effect size, 0.1 < d < 0.30 medium effect size. The results emphasize that critical thinking in pharmacy education is shaped by understanding and applying, while remembering plays a supporting role. Understanding enables students to analyze, evaluate, and adapt knowledge to complex scenarios, making it the strongest driver of higher-order reasoning. Applying reinforces this process by bridging theory with practice, allowing students to refine their decision-making skills in real-world contexts. Remembering, while necessary, has a limited effect since recall alone does not foster deep reasoning unless integrated with comprehension and practical use. These findings highlight the need for learning strategies that prioritize understanding and application, ensuring students develop critical thinking essential for clinical decision-making. 5.5. Model Robustness Testing To ensure the reliability of the research model, this study applies three evaluation methods: Goodness of Fit (GoF) measurement, model fit assessment through various statistical indices, and an analysis of explanatory power using the R-Square (R²) value. The GoF approach estimates overall model fit by calculating the square root of the mean R² value multiplied by the Average Variance Extracted (AVE). To interpret the results, this study follows Huang et al. ( 2024 ) and Phaosathianphan & Leelasantitham ( 2021 ), who classify model fit levels into four categories: poor ( 0.36). These benchmarks are consistent with the standards proposed by Tenenhaus et al. ( 2005 ) and Wetzels et al. ( 2009 ). Based on the findings, the GoF value of 0.668 indicates a strong model fit, suggesting that the proposed framework effectively represents the data and is well-equipped for hypothesis testing. GoF = \(\:\sqrt{\stackrel{-}{AVE}}\) \(\:\times\:\) \(\:\sqrt{\stackrel{-}{{R}^{2}}}\) Eq. 1 GoF = \(\:\sqrt{0.58775x0.758}\) GoF = 0.668 To further validate the research model, this study examines its explanatory power using the R-Square (R²) approach. This method assesses how well the independent variables account for variations in the dependent variables. According to Falk and Miller ( 1992 ), an R² value above 0.1 is the minimum requirement for a model to be considered reliable. If the value is lower, the model lacks sufficient explanatory power to support the hypotheses. The results indicate that collaborative learning significantly influences the cognitive stages of Bloom’s Taxonomy, with R² values of 0.387 for remember, 0.659 for understand, and 0.522 for apply. Furthermore, critical thinking has an R² value of 0.783, demonstrating that it is strongly shaped by remember, understand, and apply. Since all values surpass the required threshold, these findings confirm that the model is robust and effectively explains the relationships between the key variables. In order to ensure the model’s reliability, this study examines its fit indices, which help determine whether the model aligns well with the data. The analysis reveals fit values of SRMR 0.064, d_ULS 0.691, d_G 0.337, Chi-Square 713.94, and NFI 0.862. Based on the criteria outlined by Hair et al. ( 2017 ), a model is considered a good fit when the SRMR is below 0.080 and the NFI exceeds 0.70. Since both conditions are met, the results confirm that the model achieves an acceptable level of fit. This further strengthens the confidence in the model’s structure and its ability to support hypothesis testing. With all three robustness checks aligning with established standards, the research model is well-validated and ready for further analysis. 5.6. Results from fsQCA 5.6.1 Calibration selection and truth table construction The fsQCA analysis was initiated by calibrating the raw data to construct a truth table in Table 8 , as guided by Pappas and Woodside ( 2021 ). This process transformed the antecedents—CL, RM, UD, AP—into fuzzy set values ranging from “0” (non-membership) to “1” (full membership). Calibration ensured the logical combinations of antecedents aligned with the outcomes of high or low CT. Following calibration, the truth table was constructed to observe the raw consistency of different configurations. Consistency values greater than 0.90 indicate robust relationships between antecedents and outcomes. Table 8 presents these calibrated configurations, providing a framework for understanding how specific combinations of antecedents contribute to either high or low CT. This rigorous calibration process ensures that the analysis adheres to the fuzzy set logic, establishing reliability and validity for interpreting the findings. For high CT, the truth table identified five key configurations with raw consistency values exceeding 0.85, confirming their significance. The configuration CL = 1, RM = 1, UD = 1, AP = 1, which included 347 cases, achieved the highest consistency of 0.985, demonstrating that full membership across all antecedents leads to superior CT outcomes. Another notable configuration, CL = 0, RM = 1, UD = 1, AP = 1, involving four cases, achieved a consistency of 0.938, highlighting the critical role of memory, understanding, and application in promoting high CT, even when collaborative learning is not fully present. These findings affirm that fostering engagement in memory retention, comprehension, and practical application is pivotal for enhancing CT levels. The consistency values further validate the robustness of these configurations, illustrating their alignment with Bloom’s Taxonomy, which emphasizes progressive cognitive skill development. For low CT, six configurations were identified, with consistency values ranging from 0.805 to 0.978. The configuration CL = 0, RM = 1, UD = 0, AP = 0 had the highest consistency of 0.978 across seven cases, indicating that limited understanding and application significantly contribute to low CT. Conversely, the configuration CL = 1, RM = 1, UD = 1, AP = 1, which appeared in 347 cases, resulted in low CT outcomes with a consistency of 0.104, suggesting alternative factors may influence these outcomes despite high antecedent membership. These findings emphasize the necessity of focusing on understanding and application components to reduce low CT instances. The results highlight the importance of addressing weak antecedent engagement to effectively mitigate factors that hinder critical thinking development in educational contexts. Table 8 Truth Table for High and Low Critical Thinking Antecedents for High Critical Thinking (CT) Cases The outcome for High CT Raw Consistency CL RM UD AP 1 1 1 1 347 1 0.985 1 1 1 0 2 1 0.951 0 1 1 1 4 1 0.938 0 1 0 1 2 1 0.913 0 1 1 0 2 1 0.871 0 1 0 0 7 0 0.747 Antecedents for Low Critical Thinking (CT) Cases The outcome for Low CT Raw Consistency CL RM UD AP 0 1 0 0 7 1 0.978 0 1 1 0 2 1 0.939 0 1 0 1 2 1 0.935 0 1 1 1 4 1 0.805 1 1 1 0 2 0 0.781 1 1 1 1 347 0 0.104 5.6.2 fsQCA Analysis Table 9 presents the configurations for achieving high and low CT. For high CT, two configurations (P1 and P2) emerge with overall solution coverage of 0.963 and solution consistency of 0.956. Configuration P1 indicates that the presence of RM and UD, combined with a "don't care" condition for CL and Apply AP, leads to high CT. It achieves a raw coverage of 0.963 and unique coverage of 0.867, with a strong consistency value of 0.960. Configuration P2 highlights the importance of RM and AP in fostering high CT, with CL and UD being "don't care" conditions. While P2 demonstrates a slightly lower consistency of 0.903, its unique contribution is minimal (0.000), suggesting its supplementary role. These results validate that RM consistently contributes across both configurations, aligning with Bloom's Taxonomy, which emphasizes foundational cognitive skills as precursors to higher-order thinking. Conversely, low CT is explained by a single configuration (P1) with an overall solution coverage of 0.786 and consistency of 0.840. In this scenario, RM is the sole present condition, while CL, UD, and AP are absent. This configuration underscores that while memory retention is essential, the absence of deeper cognitive engagement and application limits the development of CT. The raw and unique coverage for low CT is identical (0.786), signifying the exclusivity of this configuration in explaining low CT outcomes. These findings highlight the necessity of integrating higher cognitive processes such as understanding and application into collaborative learning to prevent stagnation in critical thinking development. The distinctions between high and low CT configurations provide actionable insights for designing educational strategies that promote cognitive growth. Table 9 Configuration Analysis Favorable for High and Low Critical Thinking Configuration High Critical Thinking Low Critical Thinking P1 P2 P1 Collaborative Learning Ⓧ Ⓧ Remember ● ● ● Understand ● Apply ● Raw Coverage 0.963 0.096 0.786 Unique Coverage 0.867 0.000 0.786 Consistency 0.960 0.903 0.840 Overall solution coverage 0.963 0.786 Overall solution consistency 0.956 0.840 Notes: “●” indicates presence of conditions, “Ⓧ” indicates absence of conditions, and “blank space” indicates a “don't care” condition. This study highlights the complexity of developing critical thinking in pharmacy education, as shown in Table 9 . The analysis identifies two pathways (p1 and p2) that lead to high critical thinking, offering valuable insights into how different cognitive elements interact. P1 (Fig. 5 ) shows that high critical thinking can still be achieved with a don't care condition for collaborative learning and apply, as long as remember and understand are present. This configuration has a consistency of 0.960 and coverage of 0.963, indicating a strong relationship. P2 (Fig. 6 ) presents an alternative path where high critical thinking is supported by remember and apply, even when collaborative learning is in a don't care condition. This configuration has a consistency of 0.903 and coverage of 0.096, showing flexibility in how cognitive skills contribute to critical thinking. On the other hand, the analysis of low critical thinking (Fig. 7 ) reveals that even when remember is present, the absence of understanding and application limits students’ ability to think critically. This configuration has a consistency of 0.840 and coverage of 0.786, reinforcing the idea that critical thinking requires more than just recalling information. 6. Discussion This study investigates how ChatGPT-supported collaborative learning shapes critical thinking in pharmacy education using Bloom’s Taxonomy as a framework. By employing SEM, NCA, and fsQCA, this study provides a multi-dimensional perspective on how lower-order cognitive processes (Remembering, Understanding, Apply) contribute to higher-order reasoning. SEM explores how collaborative learning influences cognitive development and its impact on critical thinking. NCA determines which cognitive processes are essential for critical thinking to emerge, distinguishing between necessary and non-essential skills. fsQCA extends these insights by identifying different configurations of cognitive processes that lead to high or low critical thinking, providing practical solutions for enhancing learning strategies. Together, these findings offer a structured understanding of cognitive development and present evidence-based recommendations for designing AI-enhanced learning environments that effectively foster critical thinking in pharmacy education. SEM findings highlight that collaborative learning in ChatGPT-enhanced pharmacy education significantly strengthened understanding, application, and remembering, with understanding and applying having the greatest impact. Through discussion, students became more engaged in concepts by clarifying ideas, gaining insights into different perspectives, and refining their reasoning based on feedback. This process enhanced understanding because students actively processed information rather than passively consuming it. Collaborative learning also enhanced applying by helping students assess treatment options, justify pharmacological decisions, and relate theory to clinical practice. Remembering is also important, as retention improves with frequent talks, problem solving, and contextual learning rather than memory alone. Pires et al. ( 2020 ) emphasize the necessity of designing ChatGPT-assisted collaborative learning to improve comprehension, encourage real-world application, and increase retention through meaningful involvement. Developing critical thinking in ChatGPT-enhanced pharmacy education is primarily driven by understanding, while applying and remembering have a moderately significant impact. Understanding enables students to critically assess AI-generated clinical recommendations, evaluate treatment options, and make informed decisions. Without strong comprehension, they struggle to analyze ChatGPT-generated insights beyond surface-level recall. Applying further strengthens critical thinking by helping students connect theoretical knowledge to real-world scenarios, refine clinical reasoning, and make structured decisions. While applying and remembering are both moderately significant, their impact depends on how students actively process and engage with AI-generated content. Remembering builds a knowledge base but does not lead to deeper reasoning unless paired with comprehension and practical use. Zaidi et al. ( 2018 ) suggest that ChatGPT-assisted learning should focus on strengthening comprehension and application while ensuring memorization supports rather than replaces critical engagement with AI-generated information. The NCA findings highlight that understanding and applying are essential for fostering critical thinking, while remembering plays a supporting role. Understanding is the strongest driver, enabling students to interpret AI-generated responses, critically assess clinical recommendations, and differentiate between reliable and biased information. Without deep comprehension, students risk accepting AI outputs without scrutiny, limiting their ability to think critically. Applying further reinforces critical thinking by allowing students to test ChatGPT-generated knowledge in real-world problem-solving, ensuring that learning moves beyond passive consumption. When students actively apply insights in clinical scenarios, they refine their decision-making and analytical skills. Remembering, while necessary for knowledge retention, has a limited impact since recall alone does not promote deeper reasoning unless paired with comprehension and practical application. AI-assisted learning should focus on interactive engagement, ensuring students critically process ChatGPT’s outputs, as Almazrou et al. ( 2024 ) emphasize. The fsQCA findings reveal that different combinations of lower-order thinking skills influence how students develop critical thinking in ChatGPT-enhanced pharmacy education. Two pathways lead to high critical thinking, both requiring remembering but with either understanding or applying playing a key role. In the first configuration, students who comprehend and retain ChatGPT-generated pharmaceutical information can critically evaluate AI-generated treatment recommendations without necessarily applying them. This suggests that deep understanding helps learners assess the accuracy of AI-provided drug interactions and clinical guidelines instead of accepting them without question. In the second configuration, students who apply retained knowledge, even with limited understanding, can still develop critical thinking. This indicates that actively engaging with AI-generated case studies and using them in clinical decision-making helps refine reasoning, even when conceptual comprehension is incomplete. When only remembering is present, critical thinking remains weak because memorizing AI-generated pharmaceutical content does not lead to independent clinical judgment. As Taesotikul et al. ( 2024 ) highlight, AI-assisted learning should be designed to foster critical assessment and application of ChatGPT-generated insights. 7. Implication 7.1. Implication for Theory Development This study refines learning and cognitive development theories by showing how ChatGPT-assisted collaborative learning reshapes the role of lower-order cognitive processes in critical thinking. Traditional models often present understanding, applying, and remembering as separate stages, but these findings suggest they work together as interconnected processes in AI-assisted learning. Collaborative learning strengthens understanding by prompting students to engage actively with AI-generated content, clarify complex concepts, and refine their reasoning through discussion. Applying helps students connect theoretical knowledge to clinical practice, evaluate treatment options, and make informed decisions based on AI-generated insights. Remembering becomes more effective when students reinforce comprehension through meaningful interaction rather than passive memorization. These insights refine adaptive learning theories by emphasizing that AI-enhanced education must be designed to foster active engagement, ensuring students critically process and apply knowledge rather than simply recalling information (Pires et al., 2020 ; Zaidi et al., 2018 ). The fsQCA findings introduce an important shift in understanding how cognitive processes interact in different learning environments. Unlike traditional models that assume a linear progression from remembering to critical thinking, the results suggest multiple pathways leading to high or low reasoning skills. Some students develop strong critical thinking by comprehending and evaluating AI-generated content, even without applying it directly. Others refine their reasoning by actively applying retained knowledge, even with limited conceptual understanding. These insights challenge static cognitive models by highlighting that AI-assisted learning enables flexible cognitive pathways, where either deep comprehension or applied engagement can drive reasoning (Cain & Rajan, 2024 ). Conversely, students who rely solely on remembering struggle to critically assess AI-generated content, reinforcing that memorization without comprehension or application limits higher-order reasoning (Taesotikul et al., 2024 ). These findings call for a reassessment of how AI tools are integrated into pharmacy education, ensuring they support diverse learning trajectories. Beyond refining existing frameworks, these findings shape future AI-assisted learning models by emphasizing that ChatGPT’s role in cognitive development depends on how students engage with AI-generated outputs. Prior research has positioned AI as a content delivery tool, but this study reveals that learning effectiveness is determined by structured engagement, reflection, and application. Without intentional learning strategies, students may default to passively accepting AI-generated knowledge without deeper analysis, limiting the development of independent judgment. These findings contribute to adaptive learning theories, reinforcing that AI should not replace critical engagement but serve as a structured scaffold for developing reasoning skills (Almazrou et al., 2024 ). As ChatGPT and similar tools become more embedded in pharmacy education, these insights highlight the need for AI-driven learning designs that balance content delivery with analytical thinking, ensuring students develop the cognitive adaptability needed for complex clinical decision-making (Valcke et al., 2009 ). 7.2. Implication for Pharmacy Education Integrating AI-assisted collaborative learning into pharmacy education requires deliberate instructional strategies that foster active engagement and critical thinking. This study reveals that students do not develop higher-order reasoning simply by interacting with AI-generated content, but through structured processes of understanding, applying, and remembering. Educators must design learning experiences that push students beyond passive acceptance of AI-generated insights to maximize ChatGPT's impact. Without structured engagement, there is a risk that students will over-rely on AI outputs rather than critically evaluating and applying information in clinical decision-making. By aligning AI-enhanced learning with Bloom's Taxonomy, students develop analytical reasoning, refine their ability to assess, and apply complex pharmaceutical knowledge. The following implications provide practical recommendations for implementing these findings in pharmacy education. The SEM results show that collaborative learning enhances understanding, applying, and remembering, with understanding having the strongest influence on critical thinking. Applying and remembering also contribute, but their effects are moderately significant. These findings suggest that structured peer discussions and case-based learning help students process information deeply rather than passively accepting AI-generated content. Educators should use AI-assisted problem-solving tasks where students analyze clinical cases, justify treatment options, and integrate pharmacological knowledge into decision-making. To reinforce applying, simulated patient consultations and AI-driven diagnostic exercises can help students practice real-world clinical reasoning. Remembering should be strengthened through repeated engagement and reflection, ensuring students retain and use knowledge meaningfully rather than relying on memorization alone. The NCA findings highlight that understanding and applying are necessary conditions for critical thinking, while remembering plays a supporting role. Without a strong foundation in understanding, students may struggle to assess AI-generated recommendations critically, increasing the risk of overreliance on algorithmic outputs. Applying bridges theory and practice, helping students validate AI-generated clinical insights through experiential learning. Educators should design tasks that require students to challenge AI recommendations, cross-reference them with clinical guidelines, and apply them in case-based learning. Reflection checkpoints, where students justify their clinical decisions and discuss AI-generated responses, can reinforce deeper reasoning and analytical skills. The fsQCA results reveal that high critical thinking is achieved through different cognitive pathways. One configuration shows that students with strong understanding and remembering can critically evaluate AI-generated clinical insights, even without direct application. This suggests that pharmacy educators should emphasize deep comprehension of AI-driven recommendations, encouraging students to verify drug interactions and assess treatment accuracy before applying them. Another configuration shows that students who actively apply retained knowledge can still develop critical thinking, even with limited conceptual understanding. This highlights the value of hands-on clinical simulations, where students practice applying ChatGPT-generated knowledge in decision-making, refining reasoning through experience rather than theoretical mastery alone. In contrast, low critical thinking emerges when only remembering is present, without understanding or applying. Students who memorize AI-generated outputs without processing them critically are less likely to develop independent judgment. This reinforces the need for educators to go beyond content delivery and encourage interactive learning. Pharmacy programs should implement structured case discussions, problem-based learning, and AI-assisted role-play scenarios. It’s ensuring that students do not simply recall information, but actively engage in its analysis and application. 8. Conclusion, Limitation and Future Research Avenues This study demonstrates that ChatGPT-assisted collaborative learning enhances critical thinking in pharmacy education by strengthening lower-order cognitive processes—understanding, applying, and remembering. Using SEM, NCA, and fsQCA, the findings show that understanding plays the most significant role in fostering critical thinking, while applying and remembering provide essential support. Collaborative learning improves conceptual engagement, problem-solving, and knowledge retention, ensuring that AI-generated content is processed critically rather than passively accepted. The fsQCA results further reveal that students can develop high critical thinking through different cognitive pathways—either by deeply understanding and retaining AI-generated knowledge or by applying learned concepts in real-world scenarios. However, the findings also caution that low critical thinking occurs when students rely solely on remembering without integrating comprehension or application, reinforcing the need for structured, interactive learning strategies that encourage deeper reasoning and independent decision-making. Despite these contributions, this study has limitations that should be addressed in future research. First, the study focuses on pharmacy education, which may limit generalizability to other fields that require different cognitive demands. Future research should examine how AI-assisted collaborative learning influences critical thinking across various disciplines. Second, while the study uses a mixed-method analytical approach, it does not account for long-term cognitive development, which is crucial for assessing how AI integration shapes students’ reasoning skills over time. Future studies should incorporate longitudinal research designs to evaluate the sustained impact of AI-assisted learning (Zaidi et al., 2018 ). Third, the study does not assess how students' prior digital literacy affects their ability to critically engage with AI-generated insights, a factor that could influence learning outcomes (Almazrou et al., 2024 ). Addressing these limitations can further refine AI-enhanced learning models and ensure that AI tools like ChatGPT support rather than replace independent analytical thinking in education. References Abdulhalim, A. M., Sammarco, V., Jayasekera, J., & Ogbonna, E. (2011). Benefits of interdisciplinary learning between PharmD and PhD students. American journal of pharmaceutical education , 75 (7). 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Necessary Condition Analysis Plots\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6307782/v1/5bd2f3dbe9a7cff8770d1181.jpg"},{"id":79360498,"identity":"5a76c937-f97e-424c-8d06-de463ee99ece","added_by":"auto","created_at":"2025-03-27 12:16:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":13896,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration p1 predicting high critical thinking with consistency of 96%\u003c/p\u003e\n\u003cp\u003eNotes: Solutions have a consistency of 0.903 and coverage of 0.096. The normal ellipse represents the presence of conditions, and, no ellipse represents “do not care” conditions.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6307782/v1/4c57accff8061bdcc6c25c68.jpg"},{"id":79360499,"identity":"319f58e1-5b0f-4d65-a7f6-79efdb2a0961","added_by":"auto","created_at":"2025-03-27 12:16:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17704,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration p2 predicting high critical thinking with consistency of 90,3%.\u003c/p\u003e\n\u003cp\u003eNotes: Solutions have a consistency of 0.903 and coverage of 0.096. The normal ellipse represents the presence of conditions, the dotted ellipse represents absence conditions, and no ellipse represents “do not care” conditions.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6307782/v1/de15a2bc5a5e7a47310e814e.jpg"},{"id":79360500,"identity":"08c4b006-0e9e-4ba0-8d0e-ca8c0229c953","added_by":"auto","created_at":"2025-03-27 12:16:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":13634,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration p1 predicting low critical thinking with consistency of 84%.\u003c/p\u003e\n\u003cp\u003eNotes: Solutions have a consistency of 0.840 and coverage of 0.786. The normal ellipse represents the presence of conditions, the dotted ellipse represents absence conditions, and no ellipse represents “do not care” conditions.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6307782/v1/4676f363842938ae6c40436e.jpg"},{"id":79363172,"identity":"ad34253a-7c25-4452-92b6-001939239a53","added_by":"auto","created_at":"2025-03-27 12:48:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1993019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6307782/v1/98549054-24d3-408b-83aa-fb5c65b6befa.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDoes ChatGPT-Enhanced Collaborative Learning Foster Critical Thinkingin Education? A Bloom’s Taxonomy Perspective\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePharmacy education requires more than just theoretical knowledge. Students must develop cognitive skills that help them analyze pharmaceutical information and apply it in clinical decision-making (Das et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lower-order thinking skills (LOTS)\u0026mdash;remembering, understanding, and applying\u0026mdash;form the foundation for higher-order reasoning (Bloom, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). However, transitioning from LOTS to critical thinking requires structured learning strategies that promote active engagement with knowledge. Collaborative learning supports this process by encouraging students to discuss concepts, integrate diverse perspectives, and strengthen analytical reasoning (Rajiah, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Carstensen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the rise of artificial intelligence (AI), particularly ChatGPT, pharmacy education has access to new tools that provide instant information, personalized feedback, and clinical case simulations (Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). However, a key challenge remains: ensuring that these technologies do more than just provide information. AI must actively support cognitive development and help students strengthen critical thinking for real-world decision-making.\u003c/p\u003e \u003cp\u003eDespite the growing adoption of AI in pharmacy education, a significant gap remains in understanding how AI truly shapes students\u0026rsquo; cognitive development. Most research has focused on AI\u0026rsquo;s ability to improve information accessibility and support independent learning (Wang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, few studies have empirically investigated how AI-assisted collaborative learning contributes to the transition from LOTS to critical thinking. Heung, \u0026amp; Chiu, (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Gurnani, \u0026amp; Kaur, (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that while collaborative learning increases student engagement, its direct influence on analytical reasoning remains unclear. Additionally, many discussions surrounding AI assume that its integration will inherently enhance cognitive skills, despite the fact that, without the right pedagogical structure, AI may reinforce passive learning and memorization rather than deep understanding (Morrell et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further complicating this issue is the lack of faculty readiness in implementing AI-based instruction grounded in Bloom\u0026rsquo;s Taxonomy and concerns regarding algorithmic bias and unequal access to AI tools. Therefore, a more comprehensive investigation is needed to determine the conditions under which AI-assisted collaborative learning can truly promote critical thinking rather than simply functioning as an advanced information retrieval system.\u003c/p\u003e \u003cp\u003eThis study aims to bridge this gap by investigating how ChatGPT-supported collaborative learning supports cognitive development in pharmacy students, especially in transitioning from LOTS to critical thinking during clinical decision-making. This cognitive transition has been difficult to bridge through traditional instructional methods, which often reinforce surface-level knowledge retention instead of emphasizing deep analytical reasoning. It remains unclear whether ChatGPT-enabled collaborative learning promotes more than just improved recall and comprehension through AI-assisted learning. Therefore, this study asks: \u003cem\u003eHow does ChatGPT-based collaborative learning influence pharmacy students\u0026rsquo; cognitive processes in developing critical thinking for clinical decision-making?\u003c/em\u003e In this question, we examine whether AI-enhanced interactions aid conceptual understanding, strengthen students' abilities to apply knowledge in real-life scenarios, and support structured reasoning.\u003c/p\u003e \u003cp\u003eBeyond understanding AI\u0026rsquo;s direct influence on cognitive processes, it is equally important to explore the conditions under which AI-assisted learning effectively cultivates critical thinking. Cognitive skill development is not a uniform process; different students may require distinct learning pathways to transition from LOTS to higher-order reasoning. While some students may rely on deep comprehension to analyze AI-generated clinical recommendations, others may develop critical thinking by actively applying AI-generated knowledge in decision-making tasks. Identifying these distinct cognitive pathways is essential for designing AI-based learning environments that accommodate diverse learning styles. To address this, the second research question asks: \u003cem\u003eWhat cognitive conditions are necessary for fostering critical thinking, and how do different cognitive pathways lead to high or low reasoning in AI-based learning?\u003c/em\u003e By mapping these pathways, this study seeks to establish a clearer framework for structuring AI-driven collaborative learning that effectively supports students in developing independent analytical reasoning.\u003c/p\u003e \u003cp\u003eThis study investigates how AI-assisted collaborative learning facilitates the progression from LOTS to critical thinking in pharmacy education. To achieve this, SEM evaluates the extent to which collaborative learning enhances remembering, understanding, and applying, and how these cognitive processes contribute to critical thinking. Necessary Condition Analysis (NCA) identifies the essential cognitive skills required for students to transition from basic knowledge acquisition to deeper analytical reasoning. To translate these insights into actionable strategies, fuzzy set Qualitative Comparative Analysis (fsQCA) maps different cognitive pathways leading to high or low critical thinking, offering practical solutions for AI-enhanced pharmacy education. By integrating these methods, this study provides a comprehensive perspective on AI\u0026rsquo;s role in learning, ensuring that AI tools are leveraged to strengthen critical thinking rather than merely delivering information.\u003c/p\u003e \u003cp\u003eThe findings of this study contribute both theoretically and practically to pharmacy education. Theoretically, this study challenges the conventional view that remembering, understanding, and applying function as sequential cognitive stages; instead, it highlights their interconnected role in developing critical thinking (Nkhoma et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Arien-Zakay, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Practically, this research offers actionable insights for educators on how AI-assisted collaborative learning can be structured to not only facilitate content delivery but also support engagement, critical assessment, and clinical application of knowledge. By defining the conditions under which AI enhances cognitive development, this study provides an evidence-based framework for designing AI-integrated pharmacy curricula that cultivate students\u0026rsquo; analytical reasoning skills while maintaining their cognitive independence. Ultimately, these insights can guide institutions in implementing AI-driven learning environments that balance technological advancements with pedagogical strategies, ensuring students develop the competencies required for complex clinical decision-making.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2.1.\u003c/em\u003e Previous studies and gaps\u003c/h2\u003e \u003cp\u003ePrevious research has demonstrated the importance of collaborative learning shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Bloom's Taxonomy, and critical thinking in pharmacy education for improving student engagement and problem-solving skills. Collaborative learning has been recognized as a valuable pedagogical tool in fostering critical thinking, especially in interprofessional and interdisciplinary contexts where students apply theoretical knowledge to practical, real-world situations (Abdulhalim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Englin \u0026amp; Cillessen, 2023). Bloom\u0026rsquo;s Taxonomy offers a structured framework for cognitive development, guiding learners from foundational tasks such as remembering and understanding to advanced skills like analyzing and creating (Arien-Zakay, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, Kulikovskikh et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrated how clicker-based collaborative learning enhances higher-order thinking and reduces guessing during assessments. However, their approach was limited to clicker usage, lacking broader applicability. Similarly, Nkhoma et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) explored case-based learning integrated with Bloom\u0026rsquo;s Taxonomy, showing improvements in cognitive skills and practical knowledge application. However, their study did not address the progression from lower-order to higher-order thinking comprehensively.\u003c/p\u003e \u003cp\u003eDespite these advancements, significant gaps persist in integrating lower-order thinking skills within collaborative learning frameworks. Many curricula focus heavily on higher-order skills, such as evaluation and creation, while neglecting foundational stages like remembering and understanding, which are essential for long-term knowledge retention and application (Duncan-Hewitt \u0026amp; Austin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). For instance, Cheng et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined collaborative learning in flipped classrooms, emphasizing its impact on learning satisfaction and critical thinking. However, their research did not directly assess cognitive progression, leaving gaps in understanding how foundational skills contribute to critical thinking development. Addressing these challenges, this study introduces an AI-enhanced collaborative learning framework that integrates Bloom\u0026rsquo;s lower-order thinking skills to support critical thinking. By leveraging AI tools such as ChatGPT, this research aims to analyze how structured collaborative learning fosters the progression from foundational knowledge to advanced cognitive tasks, bridging the gaps identified in prior studies while equipping pharmacy students with essential skills for academic and professional success.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevious Studies and Gaps\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContext\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObjective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContribution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIdentified Gaps\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis Study\u0026rsquo;s Contribution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKulikovskikh et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsing clickers to facilitate collaborative learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImproving higher-order thinking while minimizing guessing during assessments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacilitates critical thinking by managing tasks effectively\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRestricted to clickers and lacks wider applicability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBroadens the application of collaborative learning across multiple areas in pharmacy education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNkhoma et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-based learning integrated with Bloom\u0026rsquo;s Taxonomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessing the development of cognitive skills through organized case analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImproves higher-order thinking and the practical application of knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDoes not emphasize the progression from lower-order to higher-order thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnalyze the progression from lower- to higher-order thinking through organized collaborative learning\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCheng et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollaborative learning within flipped classroom settings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExamining learning satisfaction in relation to personality traits and collaborative efforts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmphasizes the impact of collaborative learning on satisfaction and the development of critical thinking skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFails to directly evaluate cognitive progression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExamines the direct impact on Bloom\u0026rsquo;s Taxonomy and the development of critical thinking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI-enhanced pharmacy education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRedefining critical thinking via AI-integrated Bloom\u0026rsquo;s Taxonomy (lower Order thinking)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntegrates AI into Bloom\u0026rsquo;s Taxonomy (lower order thinking) to foster critical thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimited AI-enhanced collaborative learning research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDevelops an AI-based framework for collaborative learning and critical thinking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. ChatGPT in pharmacy Education\u003c/h2\u003e \u003cp\u003eThe integration of ChatGPT into pharmacy education is reshaping how students engage with learning materials. Acting as a virtual teaching assistant, ChatGPT enhances comprehension by simplifying complex pharmaceutical concepts and offering immediate feedback (Zhang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Instructors use it to generate assessments, including multiple-choice questions, ensuring consistency in evaluation while reducing manual workload (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, concerns about content accuracy persist, as AI-generated explanations may require expert validation to avoid misinformation (Sridharan \u0026amp; Sequeira, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, while many students rely on ChatGPT for assignments and studying, excessive dependence on AI can weaken critical thinking skills (Orok et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A structured integration within pharmacy curricula, guided by human oversight, is necessary to maximize ChatGPT\u0026rsquo;s potential while mitigating risks (Abu Hammour et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond its role in content delivery, ChatGPT contributes to skill development in pharmacy education, particularly in clinical reasoning and patient counseling. By simulating patient interactions, it allows students to practice real-world decision-making, enhancing their ability to assess symptoms and provide tailored medication advice (Zhang et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). This interactive learning approach fosters deeper engagement and supports students in applying theoretical knowledge to practical scenarios (Ahmed et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, AI-driven chatbots have demonstrated effectiveness in improving medication adherence and patient education, extending their utility beyond academic settings (Abu Hammour et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, ethical concerns, including data privacy and the risk of bias in AI-generated content, highlight the need for careful regulation and instructor guidance (Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As pharmacy education continues evolving with AI, balancing technology integration with traditional pedagogical methods will be crucial in ensuring effective learning outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Critical Thinking\u003c/h2\u003e \u003cp\u003eCritical thinking remains a core skill in education, allowing students to analyze, interpret, and assess information to solve problems effectively (Vincent-Lancrin, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In pharmacy education, it plays a vital role in shaping clinical reasoning, enabling future pharmacists to evaluate drug interactions, assess patient conditions, and make sound treatment decisions (Aktoprak \u0026amp; Hursen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, despite its importance, many pharmacy programs have yet to fully develop students' critical thinking abilities, as rote memorization often takes precedence over deep analysis (Ara\u0026uacute;jo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Educators have introduced structured approaches such as problem-based learning, case studies, and simulations to encourage analytical thinking, but their success depends largely on how well they are integrated into the curriculum and how actively students engage with them (Prokop-Dorner et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To equip students for real-world clinical decision-making, pharmacy education must adopt teaching strategies that not only impart knowledge but also push students to question, evaluate, and apply their learning critically.\u003c/p\u003e \u003cp\u003eAI-driven tools like ChatGPT are increasingly integrated into pharmacy education, offering students interactive learning experiences that enhance knowledge retention and application (Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). These tools assist in analyzing pharmaceutical case studies, evaluating prescription guidelines, and simulating patient interactions, thereby strengthening diagnostic reasoning (Reffhaug \u0026amp; Lysgaard, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). AI can support inquiry-based learning by prompting students to justify treatment decisions, explore alternative solutions, and refine their understanding of clinical concepts (Garc\u0026iacute;a-Moro et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, without structured oversight, there is a risk that students may become overly dependent on AI-generated content, passively accepting responses rather than engaging in critical analysis (Abu Hammour et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To counter this, pharmacy curricula should be designed to balance AI integration with activities that encourage independent problem-solving and metacognitive reflection (Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile AI enhances learning efficiency, it cannot replace the cognitive depth required for critical thinking in pharmacy education (Aktoprak \u0026amp; Hursen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Over-reliance on AI-generated recommendations may weaken students\u0026rsquo; ability to assess clinical data independently, reducing their confidence in making complex decisions (Kerruish, 2023). Additionally, AI systems may introduce biases or inaccuracies, reinforcing the need for students to develop strong evaluative skills to verify information critically (Huang \u0026amp; Sang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ethical considerations, such as patient safety and the responsible use of AI in healthcare, must also be incorporated into pharmacy training to ensure that students understand both the benefits and limitations of AI assistance (Garc\u0026iacute;a-Moro et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Ultimately, AI should function as a complementary tool that reinforces, rather than replaces, critical thinking, ensuring pharmacy graduates possess both technological proficiency and independent analytical skills (Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Lower Order Thinking in Bloom Taxonomy\u003c/h2\u003e \u003cp\u003eBloom\u0026rsquo;s Taxonomy categorizes lower-order thinking skills\u0026mdash;Remembering, Understanding, and Applying\u0026mdash;as the building blocks of cognitive development and knowledge retention. At its core, Remembering requires students to recall facts, recognize key concepts, and retrieve previously learned information, ensuring they have a solid foundation for further learning (Vincent-Lancrin, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Once these basics are established, students move to Understanding, where they interpret, process, and articulate concepts in their own words, allowing for deeper engagement with the material (Aktoprak \u0026amp; Hursen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Applying marks the transition from theoretical knowledge to practical use, as students begin implementing learned concepts in real-world situations, reinforcing their ability to apply information effectively (Ara\u0026uacute;jo et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These cognitive skills are essential for progression to higher-order thinking, where students analyze, evaluate, and create new knowledge through critical reasoning and problem-solving (Prokop-Dorner et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe structured approach of Bloom\u0026rsquo;s Taxonomy helps educators design curricula that support student learning progression. By prioritizing lower-order thinking, instructors ensure that students first establish a solid conceptual framework before advancing to complex cognitive tasks (Garc\u0026iacute;a-Moro et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The emphasis on remembering key facts, understanding core principles, and applying knowledge in different contexts enhances comprehension and retention, making learning more meaningful (Huang \u0026amp; Sang, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, these skills are particularly relevant in pharmacy education, where accurate recall of medical information, conceptual understanding of drug interactions, and the ability to apply knowledge in clinical settings are critical for professional competence (Abu Hammour et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mastery of lower-order thinking allows students to develop confidence in their learning process, ultimately preparing them to engage with more advanced problem-solving and decision-making tasks (Li et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Collaborative learning\u003c/h2\u003e \u003cp\u003eCollaborative learning strengthens pharmacy education by fostering teamwork, enhancing student engagement, and deepening conceptual understanding. Unlike passive instructional methods, this approach actively involves students in discussions, case analyses, and problem-solving exercises, reinforcing their ability to navigate real-world pharmaceutical challenges (Cox \u003cem\u003eet al.\u003c/em\u003e, 2012). Interprofessional education (IPE) initiatives, where pharmacy students collaborate with peers from other healthcare fields, further refine their decision-making and patient care competencies (Fusco et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The use of collaborative testing, in which students first complete exams individually before retaking them in groups, has demonstrated improvements in academic performance and knowledge retention, supporting the argument that learning is most effective when students engage with and challenge each other\u0026rsquo;s reasoning (Prescott et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, effective implementation requires structured facilitation to ensure that collaboration remains purposeful, preventing imbalanced participation and ensuring equal cognitive engagement across teams (Abdulhalim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe integration of Bloom\u0026rsquo;s Taxonomy into collaborative learning amplifies its impact by structuring cognitive development across progressive levels. Educators can design group activities that transition students from recalling basic pharmaceutical facts to critically evaluating treatment options and formulating evidence-based recommendations (Kim \u003cem\u003eet al.\u003c/em\u003e, 2012). For instance, in pharmacy curricula, collaborative exercises such as case-based discussions encourage students to analyze patient scenarios, assess therapeutic alternatives, and justify clinical decisions\u0026mdash;tasks aligned with the higher tiers of Bloom\u0026rsquo;s hierarchy (Valcke et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Writing Across the Curriculum (WAC) programs have also demonstrated the value of collaborative learning, engaging students in discussions that stimulate advanced cognitive processing (Randolph, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Nevertheless, the alignment of collaborative learning with Bloom\u0026rsquo;s framework must be consistent across diverse educational settings to maximize its benefits, requiring ongoing refinement and interdisciplinary cooperation (Owen et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Research focus area and theoretical framework\u003c/h2\u003e \u003cp\u003eThis study\u0026rsquo;s conceptual framework emphasizes the foundational role of lower-order thinking within Bloom\u0026rsquo;s Taxonomy\u0026mdash;Remember, Understand, and Apply\u0026mdash;in collaborative learning settings for pharmacy education. These cognitive stages are critical for building a solid knowledge base that enables students to effectively progress toward higher-order skills. Collaborative learning enhances this process by encouraging students to recall essential facts, interpret core concepts, and apply knowledge in practical, real-world scenarios. The structured integration of Bloom\u0026rsquo;s lower-order levels ensures that students acquire the foundational skills needed to approach more advanced cognitive tasks with confidence (Krathwohl, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Shaikh et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the focus on lower-order thinking addresses a significant gap in pharmacy education, where students often rely on memorization rather than developing a deeper understanding. Collaborative activities, such as group-based discussions and applied problem-solving tasks, allow students to process and contextualize learned material, reinforcing comprehension and retention (Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Cain \u0026amp; Rajan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By engaging with these lower cognitive levels, students are equipped to bridge the gap between theoretical knowledge and practical application, ensuring their readiness for the complexities of clinical practice. Embedding collaborative learning within Bloom\u0026rsquo;s foundational tiers not only enhances educational outcomes but also ensures a robust platform for future cognitive and professional growth (Hamid et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chhatrala \u0026amp; Chaudhari, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This provides a more engaging learning experience for students while cultivating the analytical agility essential for effective clinical reasoning in real-world healthcare settings as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Hypothesis","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Collaborative Learning and Lower Order Thinking\u003c/h2\u003e \u003cp\u003eCollaborative learning fosters an interactive learning environment where students engage in shared problem-solving, enhancing their cognitive abilities through peer discussions and knowledge exchange (Zheng et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This approach is particularly beneficial in pharmacy education, where students must integrate theoretical knowledge with clinical applications. The incorporation of ChatGPT further optimizes collaborative learning by serving as a virtual teaching assistant, facilitating structured dialogue, and supporting interdisciplinary learning (Fui-Hoon Nah et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By offering instant explanations, structured prompts, and personalized learning pathways, ChatGPT enhances engagement and information retention. However, while AI-driven collaboration improves lower-order thinking skills, excessive reliance on AI-generated content may weaken independent reasoning, necessitating strategic integration within pharmacy curricula (Gonsalves, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith ChatGPT, students are able to remember pharmaceutical knowledge more effectively by using peer interaction in conjunction with AI-generated support. Remembering, as the first level of Bloom\u0026rsquo;s Taxonomy, involves recalling key concepts such as drug classifications, mechanisms of action, and clinical guidelines. In a collaborative setting, students strengthen their memory by discussing and explaining concepts to each other, reinforcing their understanding through active engagement. ChatGPT enhances this process by providing mnemonic aids, interactive flashcards, and AI-generated summaries, making it easier for students to retrieve and retain information (Ruiz-Rojas et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, AI-driven explanations allow students to check their recall accuracy instantly, helping them refine their understanding (Zhang et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). However, if students rely too much on AI-generated content, they may remember information only at a surface level without deeper processing. To prevent this, collaborative learning with ChatGPT should be structured to encourage active discussion, self-explanation, and problem-solving, ensuring that students develop strong and lasting recall abilities (Al Shloul et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, this study proposes:\u003cem\u003eH1: Collaborative learning supported by ChatGPT enhances students' ability to remember pharmaceutical knowledge\u003c/em\u003e\u003c/p\u003e \u003cp\u003eUnderstanding involves more than just remembering facts; it requires students to interpret, explain, and apply concepts in different contexts. Collaborative learning helps develop this skill by encouraging students to engage in discussions where they clarify ideas, challenge assumptions, and refine their understanding through peer interaction (Hui, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This interactive process ensures that students do not just memorize information but also learn to connect and use it effectively. ChatGPT further supports this learning approach by providing clear explanations, structured summaries, and real-world examples, helping students view information from different perspectives and reinforcing comprehension (Tu \u0026amp; Hwang, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, true understanding requires active participation, and relying too much on AI-generated content without critical engagement may limit deep learning. To maximize the benefits, students must interact with both their peers and AI, analyzing, questioning, and applying the information rather than just accepting it as presented (Al-Dujaili et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this way, collaborative learning, combined with AI-driven support, strengthens students' ability to understand complex concepts by promoting active engagement and deeper cognitive processing. Thus, this study proposes:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH2: Collaborative learning supported by ChatGPT enhances students' understand of complex pharmaceutical concepts.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eStudents work together to tackle clinical cases that ChatGPT generates, encouraging them to use what they\u0026rsquo;ve learned and apply it to practical situations. This group learning process allows students to discuss, question, and challenge each other\u0026rsquo;s ideas, creating a space where everyone can engage deeply with the material. As they work through these clinical scenarios together, they refine their understanding and problem-solving skills (Hamid et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). ChatGPT supports this by providing immediate, personalized feedback that helps students think critically and adjust their approaches. By combining collaboration with AI-driven guidance, students gain a stronger grasp of how theory translates into practice, ultimately bridging the gap between classroom learning and real-world decision-making (Roosan et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This collaborative approach is key in helping students not only learn but also actively apply their knowledge, making the learning experience richer and more practical. Thus, this study proposes:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH3: Collaborative learning with ChatGPT enhances students' ability to apply theoretical knowledge in pharmacy practice.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Bloom Taxonomy and Critical Thinking Skills\u003c/h2\u003e \u003cp\u003eLower-order thinking skills, like remembering, understanding, and applying, lay the groundwork for developing critical thinking. These skills help students recall important information, grasp key concepts, and apply them in different situations. Without mastering these basic steps, students would struggle to tackle more complex tasks like analyzing and evaluating problems. Tools like ChatGPT can be helpful in this process by providing interactive and personalized learning experiences, which make it easier for students to retain and understand the material (Sharma, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, while AI can strengthen these foundational skills, relying too heavily on it might reduce the cognitive effort needed for deeper, independent thinking. To truly foster critical thinking, it\u0026rsquo;s important that ChatGPT be used in a way that promotes active participation and encourages students to think critically, not just absorb information (Almazrou et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRemembering, as the first stage in Bloom's Taxonomy, plays a key role in fostering critical thinking, and using ChatGPT can significantly enhance this process. By helping students recall essential facts and concepts, ChatGPT provides a structured way for them to engage with the material, reinforcing their memory through mnemonics and adaptive recall exercises (Herrmann-Werner et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This active recall, especially when integrated into collaborative learning, encourages students to not just remember, but to apply what they\u0026rsquo;ve learned, setting the stage for deeper analysis and evaluation. As students retrieve information with the help of ChatGPT, they are forced to engage more critically with the material, making connections between concepts and strengthening their understanding. While remembering may seem basic, when done actively with AI support, it becomes the foundation for developing the higher-order thinking skills needed to analyze and evaluate complex scenarios, ultimately boosting critical thinking (Taesotikul et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, this study proposes:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH4: Remember, enhanced through ChatGPT, positively influences critical thinking.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eUnderstanding plays a critical role in bridging the gap between simple knowledge recall and more complex critical thinking. When students use ChatGPT to enhance their understanding, they gain the opportunity to explore concepts through multiple perspectives and explanations, which helps them connect and contextualize theoretical knowledge. By presenting different ways of interpreting information and offering case-based scenarios, ChatGPT enables students to see patterns and relationships that are key to critical thinking (Choi, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, while AI-generated content can improve comprehension, it is not enough to passively absorb information. For understanding to truly lead to critical thinking, students must engage actively with the material, questioning and reflecting on the AI-generated explanations. This engagement encourages independent thought and deeper analysis, ensuring that students do not simply accept AI-driven insights, but critically assess and apply them in meaningful ways (Anderson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bharatha et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, this study proposes:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH5: Understand, enhanced through ChatGPT, positively influences critical thinking\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eApplying knowledge is a crucial step in developing critical thinking, as it involves using what students have learned to solve real-world problems and make decisions. ChatGPT enhances this process by simulating real-life cases, offering practice scenarios, and providing immediate feedback on problem-solving in pharmaceutical contexts (Suriano et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Through these AI-driven exercises, students are able to actively engage in decision-making, testing their ability to apply knowledge in various situations (Gonsalves, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, while ChatGPT helps guide the application of knowledge, it cannot replace the depth of human judgment. Therefore, it is important for students to critically evaluate the AI-generated solutions, ensuring they develop independent reasoning and decision-making skills (Taesotikul et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In summary, ChatGPT can support the application of knowledge, but its impact on critical thinking depends on how actively students engage and reflect on their decisions. Thus, this study proposes:\u003c/p\u003e \u003cp\u003e \u003cem\u003eH6: Apply, enhanced through ChatGPT, positively influences critical thinking.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Research Design and Process\u003c/h2\u003e \u003cp\u003eExploring how lower-order thinking skills (LOTS) in Bloom\u0026rsquo;s Taxonomy contribute to critical thinking is key to understanding the role of ChatGPT in pharmacy education. While ChatGPT helps students remember, understand, and apply knowledge, its ability to foster deeper analytical thinking is still unclear. A major concern is that students might rely too heavily on AI-generated content, which could lead to passive learning instead of active engagement. This brings up an important question: \u003cem\u003eCan ChatGPT-driven LOTS naturally lead to critical thinking, or do students need additional learning strategies to make that transition?\u003c/em\u003e By addressing this, the study provides insights into how AI can be used more effectively to support meaningful cognitive development rather than just delivering information.\u003c/p\u003e \u003cp\u003eEstablishing a clear link between lower-order thinking skills (LOTS) and critical thinking requires a solid theoretical foundation. To achieve this, a systematic literature review was conducted using Google Scholar, Scopus, and Web of Science, focusing on AI in education, Bloom\u0026rsquo;s Taxonomy, and critical thinking development. While prior studies recognize ChatGPT\u0026rsquo;s role in improving knowledge recall and comprehension, they offer limited insights into whether AI-driven LOTS can effectively transition into higher-order cognitive skills (Hu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, much of the existing research prioritizes AI\u0026rsquo;s ability to organize and present information, rather than investigating whether it actively supports students in developing critical reasoning skills. These gaps highlight the need to evaluate ChatGPT\u0026rsquo;s effectiveness not just as a tool for reinforcing LOTS, but as a potential enabler for deeper intellectual engagement in pharmacy education.\u003c/p\u003e \u003cp\u003eTo address these gaps, this study explores whether ChatGPT-driven lower-order thinking skills (LOTS) can help students develop critical thinking in pharmacy education. It looks at how AI-assisted remember, understand, and apply of knowledge provide a foundation for deeper reasoning. Instead of treating ChatGPT as just a tool for retrieving information, this research considers how it can actively support students\u0026rsquo; cognitive growth. By drawing on educational theories and AI-based learning models, the study aims to find practical ways to make ChatGPT more effective in strengthening critical thinking skills in pharmacy students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Operationalization and Measures\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Collaborative learning\u003c/h2\u003e \u003cp\u003eCollaborative learning is an active educational method where students work together in discussions, problem-solving, and shared knowledge creation, prioritizing teamwork and critical thinking over passive absorption of information (Cain \u0026amp; Rajan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hamid et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, it involves pharmacy students collaborating to grasp concepts and address challenges, supported by ChatGPT as an AI tool that facilitates interaction and knowledge exchange. ChatGPT offers personalized assistance, delivers immediate feedback, and supports activities such as virtual tutoring, test preparation, and clinical scenario analysis. The effectiveness of collaborative learning in this context is measured through four dimensions: teamwork, problem-solving, knowledge sharing, and engagement. These metrics assess how ChatGPT enhances collaboration, deepens cognitive understanding, and promotes efficient learning, underscoring its contribution to a dynamic, technology-driven educational framework in pharmacy education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Bloom Taxonomy\u003c/h2\u003e \u003cp\u003eLower-order thinking skills (LOTS), as defined in Bloom's Taxonomy, encompass remembering, understanding, and applying, forming the foundation for cognitive development in pharmacy education. These skills are essential for students to establish a strong knowledge base that enables progression to higher-order thinking tasks such as analysis and evaluation. In pharmacy education, LOTS ensures students grasp fundamental concepts, facilitating their ability to navigate more complex clinical scenarios. By incorporating Bloom\u0026rsquo;s framework, educators can structure learning objectives to progressively develop students' cognitive abilities, emphasizing the importance of foundational skills for effective pharmacy practice (Ramirez, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hui, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study operationalizes each LOTS level to evaluate its role in building a comprehensive educational foundation for pharmacy students.\u003c/p\u003e \u003cp\u003eRemember refers to the ability to recall and retrieve facts or basic concepts accurately. In pharmacy education, this involves memorizing drug names, mechanisms of action, classifications, and common side effects, which are vital for ensuring safe and effective patient care (Bharatha et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, \u003cem\u003eremembering\u003c/em\u003e is measured through assessments that test students' ability to recall essential pharmaceutical knowledge, such as identifying drug interactions or naming medications in specific therapeutic classes. These measures evaluate the extent to which students can access foundational knowledge necessary for clinical decision-making. The emphasis on remembering ensures that students build a solid knowledge repository, enabling them to respond effectively in practice-oriented settings.\u003c/p\u003e \u003cp\u003eUnderstand, the second LOTS level, involves interpreting, summarizing, and explaining concepts. In pharmacy education, understanding enables students to make connections between pharmacological data and its clinical implications, such as explaining the effects of drug interactions or summarizing therapeutic protocols (Taesotikul et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, \u003cem\u003eunderstanding\u003c/em\u003e is operationalized through tasks that require students to explain drug mechanisms or interpret patient case studies. Assessments include written responses and group discussions where students demonstrate their comprehension by connecting theoretical knowledge to practical contexts. This level plays a critical role in bridging factual recall with deeper cognitive engagement, preparing students for informed clinical decision-making.\u003c/p\u003e \u003cp\u003eApply involves using acquired knowledge in practical situations, such as solving case studies or adjusting treatment plans based on specific patient needs. In pharmacy education, application tasks train students to translate theoretical concepts into actionable decisions, such as calculating dosages or tailoring therapies to patient-specific conditions (Roosan et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this study, \u003cem\u003eapplying\u003c/em\u003e is measured through simulations, role-playing scenarios, and problem-based learning exercises where students demonstrate their ability to implement pharmaceutical knowledge effectively. These assessments reflect real-world challenges, ensuring students develop the practical skills required for professional practice. By focusing on application, this study highlights the importance of translating foundational knowledge into actionable outcomes, a critical step toward professional competency in pharmacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Critical Thinking\u003c/h2\u003e \u003cp\u003eCritical thinking is defined as the ability of pharmacy students to actively engage with AI-generated information, assess its credibility, and apply logical reasoning in clinical decision-making (Altun \u0026amp; Yildirim, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Rather than passively accepting AI outputs, students must critically evaluate and integrate information to develop well-reasoned conclusions. This study measures critical thinking through five key indicators: (1) interpreting complex pharmaceutical concepts with AI assistance, (2) integrating diverse perspectives to construct structured arguments, (3) assessing the reliability of AI-generated content, (4) applying logical reasoning to clinical decisions, and (5) identifying patterns and key insights from AI discussions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Sampling Technique and Data Collection Procedure\u003c/h2\u003e \u003cp\u003eThis study utilizes purposive sampling, targeting Indonesian pharmacy students in higher education who have used ChatGPT for personalized learning. To ensure relevance, participants were required to meet three criteria: prior ChatGPT usage for collaborative learning, current enrollment in a higher education institution, and a minimum age of 18. These conditions were clearly stated at the start of the survey to maintain data integrity. By selecting participants with specific experience, this approach strengthens the study\u0026rsquo;s ability to generate meaningful insights into AI-driven learning, Bloom\u0026rsquo;s Taxonomy, and critical thinking (Yusuf et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData collection took place online between November 2024 and January 2025, using a structured survey with four sections. The first secured informed consent, while the second screened for eligibility. The third gathered demographic data, and the final section measured key constructs using 28 Likert-scale items. To enhance validity, the instrument was reviewed by educational psychology and technology experts and pilot-tested with pharmacy students before full deployment. The study ultimately obtained 655 valid responses, providing a solid foundation for analyzing AI\u0026rsquo;s role in cognitive development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Analysis Technique\u003c/h2\u003e \u003cp\u003eThis study uses PLS-SEM with SmartPLS 4.0, a method widely recognized for handling complex theoretical frameworks and hypothesis testing (Hair et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The analysis begins with the measurement model, where construct validity is assessed through convergent validity (measured by AVE and CR) and discriminant validity (evaluated using the Fornell-Larcker criterion and HTMT ratio) (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Henseler et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To address potential bias from common method variance (CMV), Harman\u0026rsquo;s Single Factor Test is conducted (Baumgartner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moving to the structural model, R\u0026sup2; values determine how well the independent variables explain the outcomes, while f\u0026sup2; effect sizes indicate the strength of each predictor (Falk \u0026amp; Miller, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Bootstrapping with 10,000 resamples provides a solid foundation for hypothesis testing by improving statistical reliability. Additionally, model fit is checked using SRMR, d_ULS, d_G, and NFI to ensure the framework is well-structured and meaningful (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). By following this rigorous yet practical approach, the study delivers strong empirical insights into how AI-enhanced collaborative learning supports critical thinking development in pharmacy education through Bloom\u0026rsquo;s Taxonomy.\u003c/p\u003e \u003cp\u003eTo deepen the analysis, this study applies NCA and fsQCA. NCA identifies conditions that must be present for critical thinking to develop, where a factor is considered necessary if its consistency is above 0.90 and coverage exceeds 0.50 (Pappas \u0026amp; Woodside, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This helps determine whether LOTS play a crucial role in shaping critical thinking. Meanwhile, fsQCA examines different combinations of conditions to identify which factors significantly contribute, which are irrelevant, and which may hinder the outcome. By analyzing consistency and coverage values, fsQCA provides practical insights into the most effective ways to enhance AI-supported learning. The analysis is conducted using fsQCA version 4.1 (Ragin, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), ensuring that findings go beyond statistical validation and offer practical recommendations for improving critical thinking development in pharmacy education.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Sample Profile\u003c/h2\u003e \u003cp\u003eThis study involved 665 pharmacy students from Indonesia, providing a robust foundation for examining the impact of collaborative learning on lower-order and critical thinking skills (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Female participants signified the majority of the sample, accounting for 59% (n\u0026thinsp;=\u0026thinsp;391), while male students made up 41% (n\u0026thinsp;=\u0026thinsp;274), confirming a diverse range of perspectives. This higher proportion of female students likely reflects the growing trend of women pursuing pharmacy education in Indonesia, where healthcare fields often attract more female students. The largest age group was 18\u0026ndash;25 years (55%, n\u0026thinsp;=\u0026thinsp;368), displaying the typical age for higher education in Indonesia, followed by students aged 26\u0026ndash;35 years (38%, n\u0026thinsp;=\u0026thinsp;254). A smaller proportion of participants were aged 36\u0026ndash;45 years (6%, n\u0026thinsp;=\u0026thinsp;40), with only 0.4% (n\u0026thinsp;=\u0026thinsp;3) above 45 years.\u003c/p\u003e \u003cp\u003eRegarding marital status, 73% of participants were single (n\u0026thinsp;=\u0026thinsp;487), which allies with the idea that younger students often prioritize education before marriage, while the 27% of married participants (n\u0026thinsp;=\u0026thinsp;178) may consider older students returning to education for career advancement. Considering educational backgrounds: 46% held undergraduate degrees (n\u0026thinsp;=\u0026thinsp;305), 32% had senior high school qualifications (n\u0026thinsp;=\u0026thinsp;214), 17% held diplomas (n\u0026thinsp;=\u0026thinsp;116), and 5% were postgraduates (n\u0026thinsp;=\u0026thinsp;31). This diverse educational profile enhances the study's relevance, aligning with Bloom\u0026rsquo;s Taxonomy by capturing a broad spectrum of cognitive development stages. These varied demographic and educational characteristics enrich the study's findings and reinforce their applicability to pharmacy education, particularly in understanding cognitive skill progression within the framework of Bloom\u0026rsquo;s Taxonomy.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample Profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge (Years Old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducational Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior High school or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Common Method Variance\u003c/h2\u003e \u003cp\u003eIdentifying potential bias in the data is crucial for ensuring research validity, which is why this study applies Harman\u0026rsquo;s single-factor test, a widely recognized method for detecting Common Method Variance (CMV). This approach involves loading all measured items onto a single factor to determine whether a dominant variance source exists (Baumgartner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A CMV value below 50% signifies that response bias is minimal and that the collected data maintain consistency and validity. The results indicate that eigenvalues range from 0.210 to 8.492, which is significantly lower than the 50% threshold, confirming that CMV is not a concern in this study. Furthermore, an analysis of Variance Inflation Factor (VIF) values shows a range of 25.46 to 67.60, suggesting that the constructs are well-defined and do not suffer from problematic multicollinearity (Hair et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These findings reinforce the credibility of the research model, ensuring that the observed relationships reflect genuine associations rather than statistical distortions caused by measurement bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Results from SEM\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.3.1 Validity and Reliability Assessment\u003c/h2\u003e \u003cp\u003eThis study conducted a thorough validity and reliability assessment to confirm the robustness of the measurement model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). OL for all items exceeded the 0.70 threshold, indicating strong item involvements to their respective constructs (Hair et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). CA values ranged from 0.770 to 0.915, and CR values fell between 0.897 and 0.937, both exceeding the recommended minimum of 0.70. Furthermore, AVE values for all constructs were above 0.50, with scores such as 0.665 for Collaborative Learning and 0.748 for critical thinking, confirming acceptable convergent validity (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). These results demonstrate that the constructs are both reliable and valid, ensuring the measurement model captures the intended concepts accurately. Consequently, the study's findings are grounded in robust statistical foundations, free from concerns of measurement error or inconsistencies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConvergent Validity and Reliability Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCollaborative Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRemember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eUnderstand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUD11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eApply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCritical Thinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNotes:\u003c/p\u003e \u003cp\u003ea. OL, Outer Loadings; CA, Cronbach\u0026rsquo;s Alpha; CR, Composite Reliability; AVE, Average Variance Extracted.\u003c/p\u003e \u003cp\u003eb. CL, Collaborative Learning; RM, Remember; UD, Understand; AP, Apply; AN, Analyze; EV, Evaluate; CR, Create; CT, Critical Thinking.\u003c/p\u003e \u003cp\u003ec. The threshold for OL\u0026thinsp;\u0026gt;\u0026thinsp;0.70; CA\u0026thinsp;\u0026gt;\u0026thinsp;0.70; CR\u0026thinsp;\u0026gt;\u0026thinsp;0.70 and AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDiscriminant validity was assessed using the Fornell-Larcker criterion and the HTMT method to confirm the distinctiveness of the constructs in the study (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The square root of the AVE for each construct, highlighted in bold, exceeded the highest correlation between the constructs, thereby satisfying the Fornell-Larcker criterion (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). For instance, the square root of AVE for Collaborative Learning (0.815) was greater than its highest correlation with any other construct (0.902 with critical thinking). Similarly, critical thinking displayed a square root of AVE (0.865) that surpassed its intercorrelations with other constructs, such as 0.752 with Apply and 0.809 with collaborative learning. These results indicate that the constructs are empirically distinct and that the measurement model ensures strong discriminant validity across all constructs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Validity of Fornell-Larcker Criterion and HTMT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.815\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.752\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.918\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.880\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.902\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.903\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.775\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.708\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.760\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.865\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.910\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.941\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.902\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.895\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.865\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNotes:\u003c/p\u003e \u003cp\u003ea. The italic values indicates the HTMT with the threshold of \u0026lt;\u0026thinsp;0.90.\u003c/p\u003e \u003cp\u003eb. The bolded values represent the square root of AVE.\u003c/p\u003e \u003cp\u003ec. Other values represent intercorrelations between constructs for measuring the Fornell-Larcker criterion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, the HTMT values were below the threshold of 0.90, further confirming discriminant validity as proposed by Henseler et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For example, the HTMT value between Collaborative Learning and Understand was 0.918, and between Understand and Apply, it was 0.910, both within acceptable ranges. This demonstrates that the constructs maintain their uniqueness without significant overlap. The combination of Fornell-Larcker and HTMT assessments highlights the integrity of the measurement model, ensuring it is robust and suitable for hypothesis testing. These findings provide a strong foundation for subsequent structural analyses and confirm that the constructs are sufficiently distinct to capture the targeted dimensions effectively. Discriminant validity was evaluated using the cross-loadings matrix, confirming that all indicators loaded more strongly on their respective constructs than on others (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For instance, AP1 and AP3 had higher loadings on Apply (0.897 and 0.906), while CL3 loaded strongly on Collaborative Learning (0.858). These findings affirm construct distinctiveness and measurement validity, supporting their reliability for hypothesis testing (Hair et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiscriminant Validity of Cross-Loadings Matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.897\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e0.906\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.774\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.814\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.858\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.800\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e0.829\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.871\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.860\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.854\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.888\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e0.850\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.893\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.914\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.840\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUD11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.865\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.881\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.874\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote: The values printed in italic are the outer loadings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e5.3.2 Hypothesis Testing\u003c/h2\u003e \u003cp\u003eThe outcomes defined in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, and Fig.\u0026nbsp;4 (a-d) display the fundamental role of collaborative learning (CL) in increasing lower-order cognitive processes, including remembering (RM), understanding (UD), and applying (AP). Hypotheses H1 to H3 receive strong support, with CL exerting significant effects on RM (β\u0026thinsp;=\u0026thinsp;0.622, f\u0026sup2; = 0.630, T\u0026thinsp;=\u0026thinsp;11.184), UD (β\u0026thinsp;=\u0026thinsp;0.812, f\u0026sup2; = 1.936, T\u0026thinsp;=\u0026thinsp;27.735), and AP (β\u0026thinsp;=\u0026thinsp;0.723, f\u0026sup2; = 1.093, T\u0026thinsp;=\u0026thinsp;14.717). These results emphasize that collaborative learning activities promote critical foundational skills by engaging learners in group-based interactions that support memory retention, conceptual understanding, and practical application (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Moreover, the substantial effect sizes for UD and AP highlight the effectiveness of collaborative learning in cultivating deeper comprehension and real-world problem-solving, aligning closely with the goals of Bloom's Taxonomy.\u003c/p\u003e \u003cp\u003eFurther analysis reveals that the transition from lower-order to higher-order cognitive processes is facilitated through these foundational skills. The path coefficients show that RM significantly influences CT (β\u0026thinsp;=\u0026thinsp;0.098, f\u0026sup2; = 0.025, T\u0026thinsp;=\u0026thinsp;2.623), although with a small effect size, indicating that while memory plays a role in critical thinking, its impact is limited. Conversely, UD demonstrates a strong relationship with CT (β\u0026thinsp;=\u0026thinsp;0.438, f\u0026sup2; = 0.233, T\u0026thinsp;=\u0026thinsp;6.065), with a moderate-to-large effect size, underscoring the importance of understanding in developing critical thinking abilities. These findings suggest that the ability to comprehend and contextualize information is a crucial precursor to advanced reasoning, consistent with prior studies emphasizing the role of comprehension in higher-order cognitive tasks (Hair et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypothesis Testing Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHyphotesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ef\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBootstrapping CI 97.5%\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;10,000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDecision\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1. CL \u0026loz; RM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.622***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2. CL \u0026loz; UD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.812***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3. CL \u0026loz; AP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.723***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4. RM \u0026loz; CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5. UD \u0026loz; CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.438***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH6. AP \u0026loz; CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.178**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNotes:\u003c/p\u003e \u003cp\u003ea. CL, Collaborative Learning; RM, Remember; UD, Understand; AP, Apply; AN, Analyze; EV, Evaluate; CR, Create; CT, Critical Thinking.\u003c/p\u003e \u003cp\u003eb. \u003cem\u003eβ\u003c/em\u003e, Path Coefficient; CI, Confidence Interval.\u003c/p\u003e \u003cp\u003ec. Significance level determined by p-value of ***P \u0026lt; 0.001; **P \u0026lt; 0.010; *P \u0026lt; 0.050.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLastly, applying (AP) also significantly impacts CT (β\u0026thinsp;=\u0026thinsp;0.178, f\u0026sup2; = 0.058, T\u0026thinsp;=\u0026thinsp;3.066), reinforcing the idea that hands-on practice and real-world applications contribute meaningfully to critical thinking development. However, the effect size for AP remains moderate, indicating that while application is valuable, it requires integration with other cognitive skills for optimal impact on critical reasoning. Together, these results validate the structural framework of this study, illustrating how collaborative learning facilitates the progression from foundational cognitive processes to critical thinking. These findings provide empirical support for the integration of collaborative learning strategies into pharmacy education to enhance both lower-order and critical thinking skills.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Necessary Condition Analysis\u003c/h2\u003e \u003cp\u003eThis study applies PLS-SEM and Necessary Condition Analysis (NCA) to examine how lower-order thinking skills (LOTS)\u0026mdash;Remember, Understand, and Apply\u0026mdash;contribute to critical thinking in pharmacy education. PLS-SEM explores the relationships between these cognitive processes and critical thinking, while NCA determines which skills are essential for its development. The findings show that understanding is the most crucial factor (d\u0026thinsp;=\u0026thinsp;0.411, p\u0026thinsp;=\u0026thinsp;0.000). This means that students must first comprehend concepts before they can critically engage with information. Without a strong foundation in understanding, students may struggle to process ideas effectively. This limitation makes it difficult for them to assess or question information, reducing their ability to think critically.\u003c/p\u003e \u003cp\u003eThe ability to apply knowledge is also significant (d\u0026thinsp;=\u0026thinsp;0.409, p\u0026thinsp;=\u0026thinsp;0.000). Students who actively use what they learn develop stronger critical thinking skills. This suggests that learning should move beyond memorization, as applying concepts in practical situations reinforces deeper cognitive engagement. On the other hand, remembering (d\u0026thinsp;=\u0026thinsp;0.097, p\u0026thinsp;=\u0026thinsp;0.000) does not appear to be a strict requirement for critical thinking. While recall is useful for retaining knowledge, simply remembering facts does not automatically lead to deeper reasoning. These findings suggest that AI-assisted learning should prioritize strengthening understanding and application. This approach ensures that students develop the cognitive foundation necessary to transition from LOTS to critical thinking in pharmacy education\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNecessary Condition Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCritical Thinking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect Size (d)\u003c/p\u003e \u003cp\u003e(CE-FDH)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollaborative Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNotes: Effect size of 0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 is small effect size, 0.1\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.30 medium effect size.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results emphasize that critical thinking in pharmacy education is shaped by understanding and applying, while remembering plays a supporting role. Understanding enables students to analyze, evaluate, and adapt knowledge to complex scenarios, making it the strongest driver of higher-order reasoning. Applying reinforces this process by bridging theory with practice, allowing students to refine their decision-making skills in real-world contexts. Remembering, while necessary, has a limited effect since recall alone does not foster deep reasoning unless integrated with comprehension and practical use. These findings highlight the need for learning strategies that prioritize understanding and application, ensuring students develop critical thinking essential for clinical decision-making.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.5. Model Robustness Testing\u003c/h2\u003e \u003cp\u003eTo ensure the reliability of the research model, this study applies three evaluation methods: Goodness of Fit (GoF) measurement, model fit assessment through various statistical indices, and an analysis of explanatory power using the R-Square (R\u0026sup2;) value. The GoF approach estimates overall model fit by calculating the square root of the mean R\u0026sup2; value multiplied by the Average Variance Extracted (AVE). To interpret the results, this study follows Huang et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Phaosathianphan \u0026amp; Leelasantitham (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), who classify model fit levels into four categories: poor (\u0026lt;\u0026thinsp;0.1), low (0.10\u0026ndash;0.25), moderate (0.25\u0026ndash;0.36), and high (\u0026gt;\u0026thinsp;0.36). These benchmarks are consistent with the standards proposed by Tenenhaus et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Wetzels et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Based on the findings, the GoF value of 0.668 indicates a strong model fit, suggesting that the proposed framework effectively represents the data and is well-equipped for hypothesis testing.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGoF = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{\\stackrel{-}{AVE}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{\\stackrel{-}{{R}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003eEq.\u0026nbsp;1\u003c/em\u003e\u003c/p\u003e\u003cp\u003eGoF = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{0.58775x0.758}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e \u003cem\u003eGoF\u0026thinsp;=\u0026thinsp;0.668\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo further validate the research model, this study examines its explanatory power using the R-Square (R\u0026sup2;) approach. This method assesses how well the independent variables account for variations in the dependent variables. According to Falk and Miller (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), an R\u0026sup2; value above 0.1 is the minimum requirement for a model to be considered reliable. If the value is lower, the model lacks sufficient explanatory power to support the hypotheses. The results indicate that collaborative learning significantly influences the cognitive stages of Bloom\u0026rsquo;s Taxonomy, with R\u0026sup2; values of 0.387 for remember, 0.659 for understand, and 0.522 for apply. Furthermore, critical thinking has an R\u0026sup2; value of 0.783, demonstrating that it is strongly shaped by remember, understand, and apply. Since all values surpass the required threshold, these findings confirm that the model is robust and effectively explains the relationships between the key variables.\u003c/p\u003e \u003cp\u003eIn order to ensure the model\u0026rsquo;s reliability, this study examines its fit indices, which help determine whether the model aligns well with the data. The analysis reveals fit values of SRMR 0.064, d_ULS 0.691, d_G 0.337, Chi-Square 713.94, and NFI 0.862. Based on the criteria outlined by Hair et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), a model is considered a good fit when the SRMR is below 0.080 and the NFI exceeds 0.70. Since both conditions are met, the results confirm that the model achieves an acceptable level of fit. This further strengthens the confidence in the model\u0026rsquo;s structure and its ability to support hypothesis testing. With all three robustness checks aligning with established standards, the research model is well-validated and ready for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.6. Results from fsQCA\u003c/h2\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e5.6.1 Calibration selection and truth table construction\u003c/h2\u003e \u003cp\u003eThe fsQCA analysis was initiated by calibrating the raw data to construct a truth table in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, as guided by Pappas and Woodside (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This process transformed the antecedents\u0026mdash;CL, RM, UD, AP\u0026mdash;into fuzzy set values ranging from \u0026ldquo;0\u0026rdquo; (non-membership) to \u0026ldquo;1\u0026rdquo; (full membership). Calibration ensured the logical combinations of antecedents aligned with the outcomes of high or low CT. Following calibration, the truth table was constructed to observe the raw consistency of different configurations. Consistency values greater than 0.90 indicate robust relationships between antecedents and outcomes. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents these calibrated configurations, providing a framework for understanding how specific combinations of antecedents contribute to either high or low CT. This rigorous calibration process ensures that the analysis adheres to the fuzzy set logic, establishing reliability and validity for interpreting the findings.\u003c/p\u003e \u003cp\u003eFor high CT, the truth table identified five key configurations with raw consistency values exceeding 0.85, confirming their significance. The configuration CL\u0026thinsp;=\u0026thinsp;1, RM\u0026thinsp;=\u0026thinsp;1, UD\u0026thinsp;=\u0026thinsp;1, AP\u0026thinsp;=\u0026thinsp;1, which included 347 cases, achieved the highest consistency of 0.985, demonstrating that full membership across all antecedents leads to superior CT outcomes. Another notable configuration, CL\u0026thinsp;=\u0026thinsp;0, RM\u0026thinsp;=\u0026thinsp;1, UD\u0026thinsp;=\u0026thinsp;1, AP\u0026thinsp;=\u0026thinsp;1, involving four cases, achieved a consistency of 0.938, highlighting the critical role of memory, understanding, and application in promoting high CT, even when collaborative learning is not fully present. These findings affirm that fostering engagement in memory retention, comprehension, and practical application is pivotal for enhancing CT levels. The consistency values further validate the robustness of these configurations, illustrating their alignment with Bloom\u0026rsquo;s Taxonomy, which emphasizes progressive cognitive skill development.\u003c/p\u003e \u003cp\u003eFor low CT, six configurations were identified, with consistency values ranging from 0.805 to 0.978. The configuration CL\u0026thinsp;=\u0026thinsp;0, RM\u0026thinsp;=\u0026thinsp;1, UD\u0026thinsp;=\u0026thinsp;0, AP\u0026thinsp;=\u0026thinsp;0 had the highest consistency of 0.978 across seven cases, indicating that limited understanding and application significantly contribute to low CT. Conversely, the configuration CL\u0026thinsp;=\u0026thinsp;1, RM\u0026thinsp;=\u0026thinsp;1, UD\u0026thinsp;=\u0026thinsp;1, AP\u0026thinsp;=\u0026thinsp;1, which appeared in 347 cases, resulted in low CT outcomes with a consistency of 0.104, suggesting alternative factors may influence these outcomes despite high antecedent membership. These findings emphasize the necessity of focusing on understanding and application components to reduce low CT instances. The results highlight the importance of addressing weak antecedent engagement to effectively mitigate factors that hinder critical thinking development in educational contexts.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTruth Table for High and Low Critical Thinking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAntecedents for\u0026nbsp;High Critical Thinking (CT)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe outcome for High CT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRaw Consistency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAntecedents for\u0026nbsp;Low Critical Thinking (CT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThe outcome for Low CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRaw Consistency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e5.6.2 fsQCA Analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e presents the configurations for achieving high and low CT. For high CT, two configurations (P1 and P2) emerge with overall solution coverage of 0.963 and solution consistency of 0.956. Configuration P1 indicates that the presence of RM and UD, combined with a \"don't care\" condition for CL and Apply AP, leads to high CT. It achieves a raw coverage of 0.963 and unique coverage of 0.867, with a strong consistency value of 0.960. Configuration P2 highlights the importance of RM and AP in fostering high CT, with CL and UD being \"don't care\" conditions. While P2 demonstrates a slightly lower consistency of 0.903, its unique contribution is minimal (0.000), suggesting its supplementary role. These results validate that RM consistently contributes across both configurations, aligning with Bloom's Taxonomy, which emphasizes foundational cognitive skills as precursors to higher-order thinking.\u003c/p\u003e \u003cp\u003eConversely, low CT is explained by a single configuration (P1) with an overall solution coverage of 0.786 and consistency of 0.840. In this scenario, RM is the sole present condition, while CL, UD, and AP are absent. This configuration underscores that while memory retention is essential, the absence of deeper cognitive engagement and application limits the development of CT. The raw and unique coverage for low CT is identical (0.786), signifying the exclusivity of this configuration in explaining low CT outcomes. These findings highlight the necessity of integrating higher cognitive processes such as understanding and application into collaborative learning to prevent stagnation in critical thinking development. The distinctions between high and low CT configurations provide actionable insights for designing educational strategies that promote cognitive growth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfiguration Analysis Favorable for High and Low Critical Thinking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfiguration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHigh Critical Thinking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow Critical Thinking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollaborative Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eⓍ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eⓍ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaw Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique Coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall solution coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall solution consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNotes:\u003c/p\u003e \u003cp\u003e\u0026ldquo;●\u0026rdquo; indicates presence of conditions, \u0026ldquo;Ⓧ\u0026rdquo; indicates absence of conditions, and \u0026ldquo;blank space\u0026rdquo; indicates a \u0026ldquo;don't care\u0026rdquo; condition.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study highlights the complexity of developing critical thinking in pharmacy education, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The analysis identifies two pathways (p1 and p2) that lead to high critical thinking, offering valuable insights into how different cognitive elements interact. P1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) shows that high critical thinking can still be achieved with a don't care condition for collaborative learning and apply, as long as remember and understand are present. This configuration has a consistency of 0.960 and coverage of 0.963, indicating a strong relationship. P2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e) presents an alternative path where high critical thinking is supported by remember and apply, even when collaborative learning is in a don't care condition. This configuration has a consistency of 0.903 and coverage of 0.096, showing flexibility in how cognitive skills contribute to critical thinking. On the other hand, the analysis of low critical thinking (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e) reveals that even when remember is present, the absence of understanding and application limits students\u0026rsquo; ability to think critically. This configuration has a consistency of 0.840 and coverage of 0.786, reinforcing the idea that critical thinking requires more than just recalling information.\u003c/p\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis study investigates how ChatGPT-supported collaborative learning shapes critical thinking in pharmacy education using Bloom\u0026rsquo;s Taxonomy as a framework. By employing SEM, NCA, and fsQCA, this study provides a multi-dimensional perspective on how lower-order cognitive processes (Remembering, Understanding, Apply) contribute to higher-order reasoning. SEM explores how collaborative learning influences cognitive development and its impact on critical thinking. NCA determines which cognitive processes are essential for critical thinking to emerge, distinguishing between necessary and non-essential skills. fsQCA extends these insights by identifying different configurations of cognitive processes that lead to high or low critical thinking, providing practical solutions for enhancing learning strategies. Together, these findings offer a structured understanding of cognitive development and present evidence-based recommendations for designing AI-enhanced learning environments that effectively foster critical thinking in pharmacy education.\u003c/p\u003e \u003cp\u003eSEM findings highlight that collaborative learning in ChatGPT-enhanced pharmacy education significantly strengthened understanding, application, and remembering, with understanding and applying having the greatest impact. Through discussion, students became more engaged in concepts by clarifying ideas, gaining insights into different perspectives, and refining their reasoning based on feedback. This process enhanced understanding because students actively processed information rather than passively consuming it. Collaborative learning also enhanced applying by helping students assess treatment options, justify pharmacological decisions, and relate theory to clinical practice. Remembering is also important, as retention improves with frequent talks, problem solving, and contextual learning rather than memory alone. Pires et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasize the necessity of designing ChatGPT-assisted collaborative learning to improve comprehension, encourage real-world application, and increase retention through meaningful involvement.\u003c/p\u003e \u003cp\u003eDeveloping critical thinking in ChatGPT-enhanced pharmacy education is primarily driven by understanding, while applying and remembering have a moderately significant impact. Understanding enables students to critically assess AI-generated clinical recommendations, evaluate treatment options, and make informed decisions. Without strong comprehension, they struggle to analyze ChatGPT-generated insights beyond surface-level recall. Applying further strengthens critical thinking by helping students connect theoretical knowledge to real-world scenarios, refine clinical reasoning, and make structured decisions. While applying and remembering are both moderately significant, their impact depends on how students actively process and engage with AI-generated content. Remembering builds a knowledge base but does not lead to deeper reasoning unless paired with comprehension and practical use. Zaidi et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggest that ChatGPT-assisted learning should focus on strengthening comprehension and application while ensuring memorization supports rather than replaces critical engagement with AI-generated information.\u003c/p\u003e \u003cp\u003eThe NCA findings highlight that understanding and applying are essential for fostering critical thinking, while remembering plays a supporting role. Understanding is the strongest driver, enabling students to interpret AI-generated responses, critically assess clinical recommendations, and differentiate between reliable and biased information. Without deep comprehension, students risk accepting AI outputs without scrutiny, limiting their ability to think critically. Applying further reinforces critical thinking by allowing students to test ChatGPT-generated knowledge in real-world problem-solving, ensuring that learning moves beyond passive consumption. When students actively apply insights in clinical scenarios, they refine their decision-making and analytical skills. Remembering, while necessary for knowledge retention, has a limited impact since recall alone does not promote deeper reasoning unless paired with comprehension and practical application. AI-assisted learning should focus on interactive engagement, ensuring students critically process ChatGPT\u0026rsquo;s outputs, as Almazrou et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasize.\u003c/p\u003e \u003cp\u003eThe fsQCA findings reveal that different combinations of lower-order thinking skills influence how students develop critical thinking in ChatGPT-enhanced pharmacy education. Two pathways lead to high critical thinking, both requiring remembering but with either understanding or applying playing a key role. In the first configuration, students who comprehend and retain ChatGPT-generated pharmaceutical information can critically evaluate AI-generated treatment recommendations without necessarily applying them. This suggests that deep understanding helps learners assess the accuracy of AI-provided drug interactions and clinical guidelines instead of accepting them without question. In the second configuration, students who apply retained knowledge, even with limited understanding, can still develop critical thinking. This indicates that actively engaging with AI-generated case studies and using them in clinical decision-making helps refine reasoning, even when conceptual comprehension is incomplete. When only remembering is present, critical thinking remains weak because memorizing AI-generated pharmaceutical content does not lead to independent clinical judgment. As Taesotikul et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight, AI-assisted learning should be designed to foster critical assessment and application of ChatGPT-generated insights.\u003c/p\u003e"},{"header":"7. Implication","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e7.1. Implication for Theory Development\u003c/h2\u003e \u003cp\u003eThis study refines learning and cognitive development theories by showing how ChatGPT-assisted collaborative learning reshapes the role of lower-order cognitive processes in critical thinking. Traditional models often present understanding, applying, and remembering as separate stages, but these findings suggest they work together as interconnected processes in AI-assisted learning. Collaborative learning strengthens understanding by prompting students to engage actively with AI-generated content, clarify complex concepts, and refine their reasoning through discussion. Applying helps students connect theoretical knowledge to clinical practice, evaluate treatment options, and make informed decisions based on AI-generated insights. Remembering becomes more effective when students reinforce comprehension through meaningful interaction rather than passive memorization. These insights refine adaptive learning theories by emphasizing that AI-enhanced education must be designed to foster active engagement, ensuring students critically process and apply knowledge rather than simply recalling information (Pires et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zaidi et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe fsQCA findings introduce an important shift in understanding how cognitive processes interact in different learning environments. Unlike traditional models that assume a linear progression from remembering to critical thinking, the results suggest multiple pathways leading to high or low reasoning skills. Some students develop strong critical thinking by comprehending and evaluating AI-generated content, even without applying it directly. Others refine their reasoning by actively applying retained knowledge, even with limited conceptual understanding. These insights challenge static cognitive models by highlighting that AI-assisted learning enables flexible cognitive pathways, where either deep comprehension or applied engagement can drive reasoning (Cain \u0026amp; Rajan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, students who rely solely on remembering struggle to critically assess AI-generated content, reinforcing that memorization without comprehension or application limits higher-order reasoning (Taesotikul et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings call for a reassessment of how AI tools are integrated into pharmacy education, ensuring they support diverse learning trajectories.\u003c/p\u003e \u003cp\u003eBeyond refining existing frameworks, these findings shape future AI-assisted learning models by emphasizing that ChatGPT\u0026rsquo;s role in cognitive development depends on how students engage with AI-generated outputs. Prior research has positioned AI as a content delivery tool, but this study reveals that learning effectiveness is determined by structured engagement, reflection, and application. Without intentional learning strategies, students may default to passively accepting AI-generated knowledge without deeper analysis, limiting the development of independent judgment. These findings contribute to adaptive learning theories, reinforcing that AI should not replace critical engagement but serve as a structured scaffold for developing reasoning skills (Almazrou et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As ChatGPT and similar tools become more embedded in pharmacy education, these insights highlight the need for AI-driven learning designs that balance content delivery with analytical thinking, ensuring students develop the cognitive adaptability needed for complex clinical decision-making (Valcke et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e7.2. Implication for Pharmacy Education\u003c/h2\u003e \u003cp\u003eIntegrating AI-assisted collaborative learning into pharmacy education requires deliberate instructional strategies that foster active engagement and critical thinking. This study reveals that students do not develop higher-order reasoning simply by interacting with AI-generated content, but through structured processes of understanding, applying, and remembering. Educators must design learning experiences that push students beyond passive acceptance of AI-generated insights to maximize ChatGPT's impact. Without structured engagement, there is a risk that students will over-rely on AI outputs rather than critically evaluating and applying information in clinical decision-making. By aligning AI-enhanced learning with Bloom's Taxonomy, students develop analytical reasoning, refine their ability to assess, and apply complex pharmaceutical knowledge. The following implications provide practical recommendations for implementing these findings in pharmacy education.\u003c/p\u003e \u003cp\u003eThe SEM results show that collaborative learning enhances understanding, applying, and remembering, with understanding having the strongest influence on critical thinking. Applying and remembering also contribute, but their effects are moderately significant. These findings suggest that structured peer discussions and case-based learning help students process information deeply rather than passively accepting AI-generated content. Educators should use AI-assisted problem-solving tasks where students analyze clinical cases, justify treatment options, and integrate pharmacological knowledge into decision-making. To reinforce applying, simulated patient consultations and AI-driven diagnostic exercises can help students practice real-world clinical reasoning. Remembering should be strengthened through repeated engagement and reflection, ensuring students retain and use knowledge meaningfully rather than relying on memorization alone.\u003c/p\u003e \u003cp\u003eThe NCA findings highlight that understanding and applying are necessary conditions for critical thinking, while remembering plays a supporting role. Without a strong foundation in understanding, students may struggle to assess AI-generated recommendations critically, increasing the risk of overreliance on algorithmic outputs. Applying bridges theory and practice, helping students validate AI-generated clinical insights through experiential learning. Educators should design tasks that require students to challenge AI recommendations, cross-reference them with clinical guidelines, and apply them in case-based learning. Reflection checkpoints, where students justify their clinical decisions and discuss AI-generated responses, can reinforce deeper reasoning and analytical skills.\u003c/p\u003e \u003cp\u003eThe fsQCA results reveal that high critical thinking is achieved through different cognitive pathways. One configuration shows that students with strong understanding and remembering can critically evaluate AI-generated clinical insights, even without direct application. This suggests that pharmacy educators should emphasize deep comprehension of AI-driven recommendations, encouraging students to verify drug interactions and assess treatment accuracy before applying them. Another configuration shows that students who actively apply retained knowledge can still develop critical thinking, even with limited conceptual understanding. This highlights the value of hands-on clinical simulations, where students practice applying ChatGPT-generated knowledge in decision-making, refining reasoning through experience rather than theoretical mastery alone.\u003c/p\u003e \u003cp\u003eIn contrast, low critical thinking emerges when only remembering is present, without understanding or applying. Students who memorize AI-generated outputs without processing them critically are less likely to develop independent judgment. This reinforces the need for educators to go beyond content delivery and encourage interactive learning. Pharmacy programs should implement structured case discussions, problem-based learning, and AI-assisted role-play scenarios. It\u0026rsquo;s ensuring that students do not simply recall information, but actively engage in its analysis and application.\u003c/p\u003e \u003c/div\u003e"},{"header":"8. Conclusion, Limitation and Future Research Avenues","content":"\u003cp\u003eThis study demonstrates that ChatGPT-assisted collaborative learning enhances critical thinking in pharmacy education by strengthening lower-order cognitive processes\u0026mdash;understanding, applying, and remembering. Using SEM, NCA, and fsQCA, the findings show that understanding plays the most significant role in fostering critical thinking, while applying and remembering provide essential support. Collaborative learning improves conceptual engagement, problem-solving, and knowledge retention, ensuring that AI-generated content is processed critically rather than passively accepted. The fsQCA results further reveal that students can develop high critical thinking through different cognitive pathways\u0026mdash;either by deeply understanding and retaining AI-generated knowledge or by applying learned concepts in real-world scenarios. However, the findings also caution that low critical thinking occurs when students rely solely on remembering without integrating comprehension or application, reinforcing the need for structured, interactive learning strategies that encourage deeper reasoning and independent decision-making.\u003c/p\u003e \u003cp\u003eDespite these contributions, this study has limitations that should be addressed in future research. First, the study focuses on pharmacy education, which may limit generalizability to other fields that require different cognitive demands. Future research should examine how AI-assisted collaborative learning influences critical thinking across various disciplines. Second, while the study uses a mixed-method analytical approach, it does not account for long-term cognitive development, which is crucial for assessing how AI integration shapes students\u0026rsquo; reasoning skills over time. Future studies should incorporate longitudinal research designs to evaluate the sustained impact of AI-assisted learning (Zaidi et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Third, the study does not assess how students' prior digital literacy affects their ability to critically engage with AI-generated insights, a factor that could influence learning outcomes (Almazrou et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Addressing these limitations can further refine AI-enhanced learning models and ensure that AI tools like ChatGPT support rather than replace independent analytical thinking in education.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdulhalim, A. M., Sammarco, V., Jayasekera, J., \u0026amp; Ogbonna, E. (2011). 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Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance. \u003cem\u003eJournal of Research on Technology in Education\u003c/em\u003e, 1-27.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ChatGPT-Assisted Learning, Collaborative Learning, Critical Thinking, Bloom’s Taxonomy, Pharmacy Education","lastPublishedDoi":"10.21203/rs.3.rs-6307782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6307782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines how ChatGPT-supported collaborative learning influences critical thinking in education using Bloom\u0026rsquo;s Taxonomy. Purposive sampling was used to collect data from 665 Indonesian pharmacy students through an online survey. PLS-SEM assessed the direct effects of cognitive processes on critical thinking. NCA identified essential cognitive conditions, while fsQCA explored different cognitive pathways leading to high or low critical thinking. Collaborative learning significantly enhances understanding, applying, and remembering. Understanding has the strongest effect on critical thinking, while applying and remembering have moderate effects. These findings suggest that deep comprehension drives analytical reasoning, whereas applying and remembering serve complementary roles. NCA confirms that understanding and applying are necessary for fostering critical thinking, while remembering plays a supporting role. fsQCA results indicate that students who combine deep understanding with memory retention exhibit strong critical thinking. In contrast, students who rely solely on remembering without comprehension or application struggle to develop higher-order reasoning. This study reveals that ChatGPT does not inherently enhance critical thinking but must be integrated into structured collaborative learning. Effective AI-assisted education requires active discussion, application, and critical evaluation of AI-generated insights. These findings offer a framework for optimizing AI-driven pharmacy education to support both knowledge acquisition and analytical reasoning in clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Does ChatGPT-Enhanced Collaborative Learning Foster Critical Thinkingin Education? A Bloom’s Taxonomy Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 12:16:24","doi":"10.21203/rs.3.rs-6307782/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"558fc46b-475b-4950-aede-4807a19a91c4","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-27T12:16:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-27 12:16:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6307782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6307782","identity":"rs-6307782","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}