Unlocking Student Satisfaction through Affective AI Literacy: Advancing Technology Acceptance in AI-Enhanced Education

Unlocking Student Satisfaction through Affective AI Literacy: Advancing Technology Acceptance in AI-Enhanced Education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unlocking Student Satisfaction through Affective AI Literacy: Advancing Technology Acceptance in AI-Enhanced Education Madiha Shafiq, Zohra Saleem, Abdullah Ijaz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7453581/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted 13 You are reading this latest preprint version Abstract This study explores the impact of affective AI literacy on student satisfaction in Pakistan’s evolving higher education sector, which is placing greater emphasis on sustainable education and market-relevant skills. Technology Acceptance Model together with the Cognitive-Affective Theory of Learning with Media (CATLM) is used as theoretical lens for analysing this investigation. Conducted across three geographically distinct campuses of COMSATS University Islamabad, the research uses a convenience sampling approach. 237 computer science undergraduates participated through an online survey. Measurement items are adapted from established research to ensure validity and reliability, and the data is analysed using Structural Equation Modeling (SEM). Results indicate that affective AI literacy positively impacts students' perceptions of AI tools' usefulness (β = 0.655, p < .001), ease of use (β = 0.613, p < .001), and satisfaction (β = 0.148, p < .01). Perceived usefulness and perceived ease of use are found to mediate student satisfaction, enhancing student engagement and personalisation in learning. The study urges higher education to include emotional, ethical, and user-friendly AI considerations into curricula, examining how feelings and attitudes shape students' perceptions of AI’s usefulness, ease of use, and satisfaction to foster holistic AI literacy. However, limitations include the use of convenience sampling, which focused exclusively on computer science undergraduates from specific campuses, potentially limiting the generalisability to other disciplines or regions. Additionally, future research could explore additional factors like enjoyment, social influence, and academic performance to gain a broader understanding of AI literacy’s impact on student satisfaction. Affective AI literacy Perceived usefulness Perceived ease of use Student satisfaction Technology Acceptance Model Higher Education Institution Figures Figure 1 Figure 2 Introduction In the 21st century, the profound influence of Artificial Intelligence (AI) in various fields is indisputable. The domain of education, being a pivotal sector, is experiencing significant transformations as a result of the incorporation of AI technologies. These innovations incorporate a spectrum of tools such as intelligent tutoring systems and personalised learning environments, fundamentally changing the methods through which educational material is distributed and absorbed. With AI constantly reshaping the educational sphere, it is imperative for educational stakeholders to comprehend the effective adoption of AI technologies [ 1 ]. AI literacy, as refers to a set of skills that enable individuals to understand the effective use of AI technologies [ 2 ], [ 3 ], is increasingly acknowledged as an essential skill that students need to acquire especially within educational environments [ 4 ]. This literacy goes beyond mere technical expertise, including a wider range of skills that empower students to engage efficiently with AI technologies [ 5 ]. Despite the significant emphasis on the cognitive aspects of AI literacy, such as understanding AI algorithms and data analysis, the affective AI dimensions, which encompass attitudes, emotions, and values, play a crucial yet underexplored role in AI engagement and learning effectiveness (Palmquist et al. , 2025). Being a significant subset of AI literacy, affective AI literacy can be comprehensively understood through the ABCE model, comprising Affective, Behavioral, Cognitive, and Ethical dimensions. The multidimensional structure of ABCD model emphasizes the need for learners to not only comprehend how AI tools operates (cognitive), but also understand their appropriate usage behaviors (behavioral), emotional responses (affective) and engaging with AI technologies responsibly (ethical) (Ng et al., 2021). Considering this background, Affective AI literacy refers to the emotional and attitudinal elements that play a significant role in shaping how learners perceive and engage with AI technologies. This emotional engagement is particularly pertinent in educational contexts where AI tools are specifically designed to interact with students in customised and dynamic ways. Technology Acceptance Model (TAM) (Davis, 1989), together with the Cognitive-Affective Theory of Learning with Media (CATLM) (Moreno, 2006) provides a robust theoretical lens for analyzing the impact of learner's affective states, cognitive processes, and perceptions of technology on learning outcomes. CATLM speculates that when cognitive load is managed, motivation is maintained, and affective engagement is high, this lead towards meaningful learning. Similar factors are also highlighted in TAM's constructs of perceived usefulness and ease of use. The emotional response has a notable influence on student motivation, acceptance of technology, and, ultimately, the educational outcomes linked to AI utilization [ 7 ]. For instance, students with high intrinsic motivation might explore AI applications independently, while those with high self-efficacy are more likely to use AI in complex projects without feeling intimidated. Research indicates that affective factors often act as drivers of the acceptance and use of technology in educational settings, influencing the perceived ease of use and perceived usefulness (Lemay et al. , 2019). Despite the acknowledged significance of affective factors in technology adoption, a notable gap persists in literature. Previous research shows that students' emotional engagement with technology boosts motivation [ 9 ], yet the affective dimensions of AI literacy remain critically underexplored within the university context. This oversight hinders the development of a holistic AI literacy framework that addresses all facets of student engagement. While cognitive skills are important, affective AI literacy is crucial for sustained learning. Therefore, this study aims to bridge this gap by examining how affective AI literacy impacts perceived usefulness, perceived ease of use, and student satisfaction in an academic setting [ 2 ]. Pakistan’s higher education system, with over 200 institutions, is rapidly advancing to integrate modern technologies like AI despite challenges like resource limitations and regional disparities [ 10 ]. As AI transforms learning, understanding the affective AI domain becomes essential. Student’s attitudes and motivations influence their engagement to AI technologies, impacting learning effectiveness. By focusing on COMSATS University Islamabad, Pakistan, well-known for its integration of AI into educational methodologies across three distinct campuses in different parts of the country, this research provides a valuable insights into how students' emotional and attitudinal reactions to AI impact their practical engagement with the technology. These campuses collectively offer a thorough depiction of the diverse cultural backgrounds in the country, thereby enhancing the richness of the study's data. The outcomes are expected to guide the development of novel approaches in educational policies and curriculum formulation, especially in cultivating favorable emotional and attitudinal orientations towards AI among students. This paper proceeds by reviewing the relevant literature, detailing the methodology used to collect and analyse the data, presenting the findings, and finally discussing the implications of the results for theory and practice in higher education. Literature Review Theoretical Framework AI literacy in higher education is an emerging and vital area that focuses on equipping students and educators with the knowledge needed to effectively engage with AI technologies. Research indicates that AI literacy encompasses a range of competencies, from basic understanding of AI concepts to the ability to work with AI systems in complex, ambiguous situations [11], [12].The development of AI literacy is essential for preparing students to navigate an AI-driven world [13]. It has been conceptualized in various ways, but more recently, drawing from Bloom's taxonomy the ABCE model has been adopted. This model addresses the affective, behavioral, cognitive, and ethical dimensions of AI literacy [2]. However the affective dimension of AI literacy is critical, focusing on students’ emotions, attitudes, and motivation towards AI[14], [15]. It actually refers to a person’s innate emotional desire to accept the change. Positive emotions can enhance interest and motivation, while negative feelings might hinder learning progress. This emotional component is especially important as it influences students' overall engagement with AI learning materials and activities [16]. Affective AI literacy encompasses intrinsic motivation, self-efficacy, career interest, and confidence, making it an essential component in fostering a comprehensive understanding of AI. For example, high levels of intrinsic motivation, which reflect an internal desire to learn about AI for personal satisfaction or intellectual curiosity, can drive deeper engagement and exploration. Self-efficacy is students’ belief in their capability to understand and work with AI, also plays a crucial role, as students who feel competent are more likely to approach challenges confidently. Additionally, interest in AI-related careers can further shape affective AI literacy, as students with a career-oriented mindset may feel more motivated to learn about AI’s practical applications. Finally, confidence in AI knowledge can reinforce students’ willingness to participate in AI-related discussions and projects, creating a positive feedback loop that bolsters both interest and skill acquisition [17]. How learners process information in multimedia environments by integrating both cognitive and affective dimensions, is thoroughly explained by comprehensive framework of Cognitive-Affective Theory of Learning with Media (CATLM) (Moreno and Mayer, 2007). This theory speculates that meaningful learning is the outcome of cognitively engaged (through processes such as selecting, organizing, and integrating information), affectively motivated and emotionally supported mindset. Considering the context of AI literacy, CATLM together with Technology Acceptance Model (TAM) (Davis, 1989) offers a nuanced understanding of how learners accept and effectively engage with AI tools. CATLM emphasizes on the emotional and motivational factors that maintain engagement and create deeper conceptual understanding. In the background of AI literacy, usability of AI tools as well as the cognitive-affective conditions that support meaningful interaction with them should both be considered (Zawacki-Richter et al. , 2019). Consequently, CATLM provides a comprehensive theoretical lens for designing AI-based learning environments that enhance learners’ motivation, confidence, capacity to develop AI-related competencies, and also stay aligned with user expectations. On the other hand, the Technology Acceptance Model (TAM), developed by Davis (1989), provides a basis for understanding user acceptance of technology. The core premise of the model is that two key factors: the perceived usefulness and the perceived ease of use of a technology, shape an individual's attitude towards it, which subsequently influences their intention to adopt and utilize the technology [19]. Furthermore, student satisfaction is a key indicator of the effectiveness of AI-enabled learning [20]. Satisfaction reflects the extent to which students' expectations and experiences with AI-enabled technologies align, and it is a crucial predictor of continued use and engagement. Student satisfaction can be influenced by various factors, including perceived usefulness and perceived ease of use, as well as the emotional and social aspects of interacting with AI. Affective AI literacy may play a crucial role in shaping student satisfaction, as it can help students navigate the complex emotional and social dynamics involved in working with AI systems [21], [22]. Conceptual Framework This investigation explores the impact of Affective AI Literacy on perceived usefulness, perceived ease of use, and student satisfaction. The proposed conceptual framework (see Fig. 1) introduces four main hypotheses to study the relationships between affective AI literacy and essential outcome variables. Perceived usefulness is an individual's assessment of a technology's ability to effectively support or enhance human activities. When users perceive a technology as useful in meeting their needs, their interest and engagement with that technology tend to increase, leading to higher adoption rates and more frequent use [23], [24]. Within the context of AI technologies, affective AI literacy, the combination of students’ emotional and motivational readiness to engage with AI, is expected to positively influence their perception of AI’s usefulness. When students feel both confident and motivated in their understanding of AI, they are more likely to view these technologies as beneficial tools that can enrich their educational experience and enhance their productivity [25], [26]. Thus, it is hypothesized that students with high levels of affective AI literacy will be better positioned to appreciate the advantages and potential applications of AI, which, in turn, will increase their perceived usefulness of these technologies. H1: High affective AI literacy leads to higher perceived usefulness. Perceived ease of use refers to the extent to which individuals believe that a technology will make their activities more manageable and require minimal effort [23]. This perception is an essential factor that can be significantly shaped by affective AI literacy. Affective AI literacy, which encompasses students' emotional readiness and motivation to engage with AI, influences their self-confidence and perceived ability to use AI technologies effectively. When students possess high level of affective AI literacy, they typically feel more capable and assured in their interactions with AI, which in turn lowers the perceived complexity and effort associated with using these tools [27]. Consequently, students with high affective AI literacy are expected to find AI technologies more intuitive and accessible, as their positive attitudes and motivation help create smoother and more user-friendly experiences with AI systems. Therefore, it is assumed: H2: High affective AI literacy leads to higher perceived ease of use. Student satisfaction (SS) represents students' contentment with their learning activities and the services provided, serving as a pivotal factor in determining the quality of education and sustaining engagement in learning [28]. As a critical outcome in educational settings, student satisfaction reflects the degree to which students feel positively about their overall educational experience [29]. When students feel motivated, confident, and competent in their interactions with AI, they are more likely to derive enjoyment from the learning process, thereby enhancing their satisfaction with their education [22]. Thus, it is assumed that high levels of affective AI literacy can contribute significantly to student satisfaction by cultivating positive attitudes and emotional responses towards AI, leading to a more fulfilling and satisfying educational experience. H3: High affective AI literacy leads to higher student satisfaction. Affective AI literacy may enhance student satisfaction indirectly by positively shaping their perceptions of AI technology's utility. When students possess strong affective AI literacy, they feel more confident, motivated, and emotionally prepared to engage with AI. This readiness allows them to see AI not merely as a tool, but as a valuable asset that can enrich their learning process, improve productivity, and offer practical solutions to educational challenges. As students begin to perceive AI technologies as beneficial and relevant to their learning goals, their overall satisfaction with the educational experience is likely to increase. This positive view fosters a sense of accomplishment, engagement, and enthusiasm toward their studies, ultimately contributing to a more fulfilling and enjoyable learning environment. In this way, affective AI literacy indirectly boosts student satisfaction by making AI technologies appear not only accessible but also impactful and relevant to their educational journey [22]. Therefore, it is hypothesized that the positive impact of affective AI literacy on student satisfaction is partly explained by its influence on perceived usefulness and perceived ease of use. H4a: Perceived usefulness mediate the relationship between affective AI literacy and student satisfaction. H4b: Perceived ease of use mediate the relationship between affective AI literacy and student satisfaction. Materials and Methods Population This study focuses on university students with prior AI coursework experience, specifically targeting those from COMSATS University Islamabad (CUI). The reason for selecting CUI is as it is ranked 3rd out of 67 public sector universities of Pakistan (Rankings, 2024). Working with 5 faculties (Engineering, Information Science and Technology, Business Administration and Architecture and Design), CUI has a trend-setting pedagogical approach. CUI’s campuses in Abbottabad, Islamabad, and Lahore are selected to ensure a diverse sample representing various cultural backgrounds across the country. The population consists of undergraduate students of computer science department from these three campuses, in their last two semesters who have taken AI courses (Artificial Intelligence, Programming for Artificial Intelligence, Introduction to Computer Vision, Machine Learning Fundamentals, Natural Language Processing, Artificial Neural Networks and Deep Learning and Knowledge-Based Systems) as part of their degree program. Out of 3,224 students enrolled in the computer science department across these campuses, 613 students fit the target profile for the study. Sampling A convenience sampling technique is selected due to the accessibility and proximity of the target student group, enabling efficient data collection. This choice is based on logistical considerations, given the dispersed nature of the campuses and constraints in time and resources. Additionally, convenience sampling facilitated rapid data gathering from students who met the study’s criteria of having completed relevant AI courses. This approach aligns with prior research in educational technology, where convenience sampling has been used to obtain targeted insights on digital literacy and AI competencies [ 30 ], [ 31 ]. Based on a 95% confidence interval, the calculated sample size is 237. Measurement Instrument Measurement items (See Table 1 ) for each construct are carefully selected and adapted from prior research to ensure both validity and reliability. Table 1 Variables Scales Items Affective AI literacy [ 2 ] 10 items Perceived Usefulness [ 18 ] 6 items Perceived Ease of Use [ 18 ] 6 items Student Satisfaction [ 32 ] 5 items Total 27 items Measurement Instrument A five-point Likert scale, ranging from "strongly disagree" to "strongly agree," is used, drawing on items from relevant and validated scales commonly utilized by scholars and researchers in their respective fields (For final questionnaire see Appendix). Data Collection & Analysis Techniques The questionnaire is distributed to students through their teachers, who provided both physical copies and digital versions via email and WhatsApp. With regular reminders to students and their teachers, a total of 237 complete questionnaires are collected and subsequently used for analysis. Data analysis is performed using SEM (Structure Equation Modeling) with the help of software tools SPSS and Smart PLS incorporating: Methods of descriptive statistics like frequency distribution, measure of central tendency and dispersion, preliminary analysis using correlation and factor analysis, validity and reliability analysis, path analysis for hypothesis testing and mediation analysis. Results Respondent’s Profile & Descriptive Statistics Table 2 Respondent’s Demographics Variable N (237) % Gender Male 177 74.6 Female 60 25.4 Age 18–24 223 94 25–34 14 6 Table 3 Mean Std. Deviation Skewness Kurtosis Statistic Statistic Statistic Std. Error Statistic Std. Error AI 3.83 .864 -1.253 .158 1.325 .315 PU 4.07 .991 -1.473 .158 1.834 .315 PEU 4.00 .974 -1.390 .158 1.839 .315 SS 3.86 .919 -1.137 .158 1.543 .315 Descriptive Statistics Correlation Analysis PEU (r = 0.613, p < 0.01), PU (r = 0.655, p < 0.01), and SS (r = 0.604, p < 0.01) all show a positive correlation with AI, suggesting that AI positively influences student satisfaction, driven by its perceived ease of use and perceived usefulness in education (see Table 4 ). A strong positive correlation indicates that respondents who perceive ease of use favorably also place a high value on perceived usefulness (r = 0.728, p < 0.01) and student satisfaction (r = 0.754, p < 0.01). The results show that student satisfaction is highly related to the perceived usefulness (r = 0.691, p < 0.01). Table 4 Correlation Al PEU PU SS Al PEU 0.613** PU 0.655** 0.728** SS 0.604** 0.754** 0.691** Note: **. Correlation is significant at the 0.01 level (2-tailed). AI (Artificial Intelligence), PU (Perceived Usefulness), PEU (Perceived Ease of Use) , SS = Student Satisfaction Measurement Model The study uses a factor analysis to identify the latent components that influence respondents' opinions of AI, such as Perceived Usefulness (PU), Perceived Ease of Use (PEU) and its impact on Student Satisfaction (SS) (See Table 5 ). The study evaluates the reliability and validity of the measurement instruments by calculating Cronbach's alpha, composite reliability, and average variance extracted (AVE) for the constructs. Cronbach’s alpha values ranged from 0.872 to 0.908, suggesting that the internal consistency for the constructs is satisfactory. The composite reliability values shows consistent patterns, varying between 0.921 and 0.936 [ 33 ]. These results indicate that the dependability is satisfactory, as values over the recommended threshold of 0.70 indicate that the measurement instruments are dependable. In addition, the average variance extracted values, which ranged from 0.596 to 0.829, indicated that the constructs were able to explain a significant amount of their own variability [ 33 ]. This finding supports the idea that the constructs have convergent validity. Overall, the evaluations of reliability and validity instill trust in the strength and accuracy of the measurement equipment used in the study, hence bolstering the legitimacy of the research findings. Table 5 Measurement Model Variables Indicators Factor loadings Cronbach’s Alpha Composite Reliability AVE AI Q1 0.695 0.903 0.922 0.596 Q2 0.831 Q3 0.777 Q4 Q5 Q6 Q7 Q8 0.839 0.783 0.780 0.718 0.742 PU Q9 0.915 0.897 0.936 0.829 Q10 0.888 Q11 0.911 Q12 0.828 PEU Q13 0.913 0.908 0.936 0.785 Q14 0.930 Q15 0.888 SS Q16 0.903 0.872 0.921 0.796 Q17 0.914 Q18 0.859 AI = Artificial Intelligence, PU = Perceived Usefulness, PEU = Perceived Ease of Use, SS = Student Satisfaction Discriminant Validity It is noteworthy that all construct pairs had HTMT ratios below the widely accepted cutoff of 0.85, indicating respectable discriminant validity [ 34 ]. As an illustration, the HTMT ratios of 0.665, 0.708 and 0.664 are found between AI and PEU, PU and SS respectively. Collectively, these findings confirm that the study's constructs are unique from one another and give confidence that the measurement model accurately reflects the distinctive features of each underlying construct, supporting the measurement model's discriminant validity (See Table 6 ). Table 6 Al EOU PU SS Al PEU 0.665 PU 0.708 0.806 SS 0.664 0.851 0.772 Heterotrait-Monotrait Ratio (HTMT) AI = Artificial Intelligence, PU = Perceived Usefulness, PEU = Perceived Ease of Use, SS = Student Satisfaction Model Fit Table 7 compares model fit indices for the Saturated and Estimated Models. The SRMR for the Saturated Model is 0.076, indicating a good fit (below the 0.08 threshold) [ 35 ], while the Estimated Model has an SRMR of 0.109, slightly exceeding the acceptable range. The Chi-square values are 671.475 for the Saturated Model and 707.251 for the Estimated Model, with lower values suggesting a better fit. Overall, the Saturated Model shows a better fit based on lower SRMR, d_ULS, and Chi-square values, along with a higher NFI, though both models exhibit similar fit levels. Table 7 Model Fit Saturated model Estimated model SRMR 0.076 0.109 d_ULS 0.999 2.017 d_G 0.481 0.551 Chi-square 671.475 707.251 NFI 0.806 0.796 Hypotheses Testing The path value is demonstrated in Table 8 . The AI ->PU pathway's mean is 0.655, AI ->PEU pathway's mean is 0.613, AI ->SS pathway's mean is 0.148. The statistical analysis reveals a significant positive association between Artificial Intelligence and latent variables, as evidenced by the p-value of 0.000. Hence supporting hypotheses H1, H2 and H3. Table 8 Path Coefficient P values Result Al ->PU 0.655 0.000 Supported H1 Al ->PEU 0.613 0.000 Supported H2 Al ->SS 0.148 0.005 Supported H3 Path coefficients AI = Artificial Intelligence, PU = Perceived Usefulness, EOU = Perceived Ease of Use, SS = Student Satisfaction Mediation Analysis The respective results are shown in Table 9 . AI has a statistically significant positive direct effect on SS (β = 0.148, p < 0.01). Furthermore, results of mediation analysis show that AI has a significant positive direct effect on PU (β = 0.655, p < .001) and PU has a significant positive direct effect on SS (β = 0.236, p < .01). Moreover, the indirect effect (i.e., mediated by PU) of AI on SS showed a weak but positive effect (β = 0.115, p < .01) hence providing partial mediation and acceptance of hypotheses H4a. The results of mediation analysis show that AI has a significant positive direct effect on EOU (β = 0.613, p < .001) and EOU has a significant positive direct effect on SS (β = 0.492, p < .001). Moreover, the indirect effect (i.e., mediated by EOU) of AI on SS show positive effect (β = 0.301, p < .01) hence providing partial mediation and acceptance of hypotheses H4b. Table 9 Path Paths coefficients Specific Indirect effect Direct effect Result Comment a b ab c AI → SS 0.148 ** AI → PU → SS 0.655*** 0.236*** 0.155** Supported H4a Partial mediation AI → PEU → SS 0.613*** 0.492*** 0.301*** Supported H4b Partial mediation Results for Mediation AI = Artificial Intelligence, PU = Perceived Usefulness, EOU = Perceived Ease of Use, SS = Student Satisfaction Note: *p < .05, **p < 0.01, ***p < .001. Discussion AI literacy is a leading trend in the education sector and recognized as an essential skill increasingly expected of students. Among the four main areas of AI literacy, Affective AI literacy is particularly important, as it encompasses students' natural emotional and motivational responses to a subject [ 36 ]. Accordingly, the focus of this study is to investigate the impact of Affective AI literacy on perceived usefulness, perceived ease of use, and student satisfaction. The findings indicate a significant positive relationship (path coefficient = 0.655***) between affective AI literacy and perceived usefulness. When students score high in the affective aspect of AI literacy, which includes their emotional and motivational readiness to engage with AI, they are more likely to perceive AI as useful. Perceived usefulness indicates a user’s belief in a technology's ability to provide assistance in different tasks and enhance productivity. This actually refers to individual's evaluation of how well a technology can improve or streamline human activities [ 23 ], [ 24 ]. When users see a technology as beneficial in meeting their needs, their interest and engagement with that technology tend to grow, leading to higher adoption rates and more frequent use. In the context of AI, affective AI literacy (including students' emotional confidence and intrinsic motivation to use AI) is expected to strengthen the user’s perception of AI’s value. Students who feel confident in comprehending the use of AI tools, and at the same time they feel motivated, are more inclined to view it as a valuable tool. AI becomes a dynamic partner in their education, helping them manage tasks more efficiently, gain deeper insights, or access customized learning materials. Consequently, this positive viewpoint promotes a deeper integration of AI into their daily academic practices, resulting in improved productivity, greater engagement and enrich learning experiences [ 37 ]. The results of the present research also demonstrated a significant positive association (0.613***) between AI literacy and perceived ease of use, which refers to the extent to which users anticipate using new technology with minimal difficulty [ 37 ]. Studies have already shown that AI literacy contributes to a sense of empowerment and autonomy among users, which in turn raises their perceived ease of use (Venkatesh et al. , 2003; Sudaryanto et al. , 2023). When students have a high level of affective AI literacy, they are more likely to possess a strong motivation, confidence, interest, and self-efficacy in using AI technology. This literacy equips them with not only the technical skills but also the emotional and cognitive readiness to engage meaningfully with AI tools. Consequently, students who feel capable and knowledgeable about AI are less likely to experience pressure or nervousness around AI technologies, which can otherwise be a significant barrier to use. The sense of competence that comes with affective literacy creates a positive perception of AI’s ease of use, making the technology seem accessible and beneficial to their personal learning or productivity goals. This positive attitude also leads to reduces user resistance, a common challenge in technology adoption, since students with high AI literacy are less likely to view AI systems as overly complex. Instead, they recognize the utility of these tools and approach them as resources that enhance their capabilities rather than detract from them. Research supports that individuals with a strong sense of self-efficacy and affective technological literacy are tend to involve positively with digital tools, considering them easier to use and incorporating them more readily into their daily activities. As a result, their productivity and learning increase substantially (Venkatesh et al. , 2003; Sudaryanto et al. , 2023). Student satisfaction (SS) plays a major role in shaping the quality of education and preserving the aspect of learning (Vermisli et al. , 2022). The current investigation has established a significant positive relationship (0.148**) between affective AI literacy and student satisfaction. When students possess a high level of AI literacy, they are more comfortable and enthusiastic about interacting with AI technologies, which enhances their engagement and satisfaction with these tools. High affective AI literacy tends to promote engagement, and confidence in using AI tools, leading to a more enjoyable and satisfying educational experience. For example, a well-developed understanding with AI have been shown to reduce technology-related anxiety and promote positive attitudes, which in turn increase academic motivation and satisfaction. Moreover, when students can confidently engage with AI tools, they experience enhanced academic well-being, which has been positively linked to educational outcomes and personal satisfaction [ 40 ]. Additionally, when students are motivated and self-assured in using AI, they are more likely to explore its features fully, discovering the ways in which AI may streamline their work, personalize their learning, and make studying more effective. Research suggests that such positive experiences with technology usage contribute to heightened satisfaction, as students feel their needs and expectations are met, and they experience fewer frustrations or challenges [ 18 ]. Eventually, when students feel confident in their AI literacy, they not only adopt AI tools quickly but also derive more value from them, leading to greater satisfaction and enhanced learning experiences. The findings indicate that while there is a positive relationship between affective AI literacy and student satisfaction, introducing perceived ease of use and perceived usefulness as mediators significantly strengthens this impact. Particularly, perceived ease of use acts as a strong mediator (0.301***) between affective AI literacy and student satisfaction. A strong mediation of perceived ease of use between affective AI literacy and student satisfaction underscores the fundamental role of user-friendliness in the educational experience with AI-driven tools. Affective AI literacy involves understanding and effectively using AI by incorporating emotions, leads to enhanced students' ability to engage positively with educational technology. However, if these AI tools are perceived as challenging to use, this may not directly guide to increased satisfaction. When students perceive these tools as easy to use, it bridges the gap between literacy and satisfaction, allowing them to feel competent and empowered while interacting with AI in learning environments. Consequently, perceived ease of use can significantly enhance the positive effects of affective AI literacy on student satisfaction, highloghting its importance as a mediator in educational technology adoption (Al-Abdullatif and Alsubaie, 2024; Aurangzeb et al. , 2024). Moreover, studies applying the Technology Acceptance Model (TAM) show that students’ perceptions of usability directly affect their willingness to engage with AI in learning, indicating that ease of use amplifies the positive effects of AI literacy on satisfaction and motivation to adopt new tools [ 43 ]. Incorporating AI tools which are easy to use in educational settings can help in optimizing the benefits of AI literacy, assisting learners to feel more confident in their tech interactions. While perceived ease of use plays a stronger mediating role, perceived usefulness also significantly enhances the relationship (0.155**) between affective AI literacy and student satisfaction. Student’s satisfaction with AI tools increases when they find these tools helpful in achieving their educational goals, although not impacting as highly as perceived ease of use does. This suggests that while perceived ease of use primarily facilitates the comfort and accessibility of AI tools, perceived usefulness contributes by assuring students of the tool’s value in supporting their learning objectives [ 43 ]. Together, these factors underscore the importance of both intuitive design and clear utility in promoting satisfaction with AI in education (Luo et al. , 2025). Furthermore, these conclusions are also supported by the Cognitive Affective Theory of Learning with Media (CATLM), which signifies the relationship between cognitive and affective processes in multimedia learning environments. This theory suggests that when learners are emotionally involved with the technology and find these tools beneficial and easy to use, their meaningful learning outcomes enhances along with increased motivation and reduced cognitive load. Affective AI literacy, by enhancing emotional readiness and motivation, stimulates these areas. Ease of use lessens unnecessary cognitive effort, helping students to focus on meaningful learning tasks. Perceived usefulness contributes by reinforcing the instrumental value of AI in achieving learning outcomes, completing the motivational loop. Hence, incorporating easy to use and pedagogically meaningful AI tools can enhance the benefits of affective AI literacy. Collectively, insights from both TAM and CATLM highlight the importance of designing AI-driven educational technologies that are not only functionally effective but also emotionally intuitive, in order to maximize student satisfaction and academic success (Luo et al. , 2025). Conclusion Affective AI literacy has the capability to considerably increase student satisfaction and learning outcomes generally. By providing learners with the skills to comprehend and interact effectively with emotionally responsive AI, affective AI literacy facilitates them to benefit from technology that will be supportive of their learning needs. This literacy fosters a perception of AI tools as both beneficial and easy to use, which directly influences satisfaction by making learning more engaging and tailored to their needs. Students who feel competent in using AI-driven educational tools experience a smoother, more enjoyable learning process, ultimately reducing frustrations and promoting a positive attitude toward learning technology. Eventually, affective AI literacy bridges the gap between technological proficiency and emotional engagement, creating a supportive environment that strengthens confidence, satisfaction, and motivation in students’ academic journeys. Implications of the Study This study’s findings have significant implications, especially for higher education in developing countries like Pakistan. The results show how crucial it is to include emotional factors like motivation, confidence, and ethical awareness when designing AI courses. Doing this helps create learning experiences that feel more engaging and personalized, making students more satisfied and ready to embrace AI technology. It also points to the need for easy-to-use educational tools that connect with both the feelings and thinking of students. On a social level, helping students develop emotional awareness around AI can lead to more inclusive and thoughtful use of technology. It encourages people to use AI in ways that respect different cultures and values. In countries facing resource constraints and rapid digital transformation, such an approach is vital for ensuring equitable access to AI education and preparing students to navigate the ethical challenges of emerging technologies. Limitations The study has several limitations that may affect the generalizability and validity of its findings. First, it employs a convenience sampling method and focuses exclusively on undergraduate computer science students from COMSATS University campuses in Pakistan, which may not represent the broader student population. This sampling limitation restricts the application of the findings to other disciplines, regions, and potentially different student demographics. Additionally, the research relies on self-reported data collected via an online questionnaire, a method that can introduce biases like social desirability or inaccuracies in self-assessment, affecting the data's reliability. The cross-sectional study design further limits the ability to draw causal conclusions or observe changes in affective AI literacy and student satisfaction over time. The absence of potential mediating factors, such as enjoyment, self-regulation, academic performance, social influence, technological anxiety, and learning autonomy, suggests that additional research is needed to gain a fuller understanding of factors influencing student satisfaction with AI. Future studies could address these limitations by incorporating a more diverse sample, applying longitudinal research methods, and including participants from a variety of academic backgrounds and geographical regions. Declarations Availability of data and materials It is available on request. Competing interests There were no competing interests among the participants during the course of this research. Funding There was no funding received for carrying out this research. Authors’ contributions Acknowledgements We would like to express our sincere gratitude to all the teachers who generously contributed their time and effort to assist in the data collection process for this study. Their support, cooperation, and commitment were invaluable in ensuring the accuracy and completeness of the data. Clinical trial number Not applicable Consent to participate Not applicable Consent to publish Not applicable Ethics approval This study was reviewed and approved by the Academic Research Committee at COMSATS University Islamabad. All procedures involving human participants were in accordance with ethical standards, and informed consent was obtained from all participants. References Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education–where are the educators? Int J Educ Technol High Educ. 2019;16(1):1–27. Ng DTK, Wu W, Leung JKL, Chiu TKF, Chu SKW. Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. Br J Educ Technol. 2024;55(3):1082–104. Ashok M, Madan R, Joha A, Sivarajah U. Ethical framework for Artificial Intelligence and Digital technologies. Int J Inf Manage. 2022;62:102433. Irwanto I. Research trends on artificial intelligence in K-12 education in Asia: a bibliometric analysis using the Scopus database (1996–2025). Discov Artif Intell. 2025;5(1):155. Long D, Magerko B. What is AI literacy? Competencies and design considerations, in Proceedings of the 2020 CHI conference on human factors in computing systems , 2020, pp. 1–16. Palmquist A, Sigurdardottir HDI, Myhre H. Exploring interfaces and implications for integrating social-emotional competencies into AI literacy for education: a narrative review. J Comput Educ, pp. 1–37, 2025. Lee H-Y, Lin C-J, Wang W-S, Chang W-C, Huang Y-M. Precision education via timely intervention in K-12 computer programming course to enhance programming skill and affective-domain learning objectives. Int J STEM Educ. 2023;10(1):52. Lemay DJ, Doleck T, Bazelais P. Context and technology use: Opportunities and challenges of the situated perspective in technology acceptance research. Br J Educ Technol. 2019;50(5):2450–65. Getenet S, Cantle R, Redmond P, Albion P. Students’ digital technology attitude, literacy and self-efficacy and their effect on online learning engagement. Int J Educ Technol High Educ. 2024;21(1):3. Dahri NA et al. Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Educ Inf Technol, pp. 1–50, 2024. Chan CKY. A comprehensive AI policy education framework for university teaching and learning. Int J Educ Technol High Educ. 2023;20(1):38. Su J, Yang W. Artificial intelligence (AI) literacy in early childhood education: An intervention study in Hong Kong. Interact Learn Environ, pp. 1–15, 2023. Laupichler MC, Aster A, Schirch J, Raupach T. Artificial intelligence literacy in higher and adult education: A scoping literature review. Comput Educ Artif Intell. 2022;3:100101. Asio JMR. AI literacy, self-efficacy, and self-competence among college students: variances and interrelationships among variables. MOJES Malaysian Online J Educ Sci. 2024;12(3):44–60. Liu Y, Zhang H, Jiang M, Chen J, Wang M. A systematic review of research on emotional artificial intelligence in English language education. System. 2024;126:103478. Vistorte AOR, Deroncele-Acosta A, Ayala JLM, Barrasa A, López-Granero C, Martí-González M. Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review. Front Psychol. 2024;15:1387089. Ng DTK, Leung JKL, Chu SKW, Qiao MS. Conceptualizing AI literacy: An exploratory review. Comput Educ Artif Intell. 2021;2:100041. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, pp. 319–40, 1989. Al-Abdullatif AM, Gameil AA. The Effect of Digital Technology Integration on Students’ Academic Performance through Project-Based Learning in an E-Learning Environment. Int J Emerg Technol Learn, 16, 11, 2021. Hooda M, Rana C, Dahiya O, Shet JP, Singh BK. Integrating LA and EDM for improving students Success in higher Education using FCN algorithm, Math. Probl. Eng. , vol. 2022, no. 1, p. 7690103, 2022. Sajja R, Sermet Y, Cikmaz M, Cwiertny D, Demir I. Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. Information. 2024;15(10):596. Rodway P, Schepman A. The impact of adopting AI educational technologies on projected course satisfaction in university students. Comput Educ Artif Intell. 2023;5:100150. Arta TLF, Azizah SN. Pengaruh Perceived Usefulness, Perceived Ease Of Use dan E-Service Quality Terhadap Keputusan Menggunakan Fitur Go-Food dalam Aplikasi Gojek. J Ilm Mhs Manajemen Bisnis Dan Akunt. 2020;2(2):291–303. Ardiyanti A, Susilowati E. Perceived Usefulness and Technology Readiness Mediate Perceived Ease of Use and Digital Competence on Technology Adoption of Artificial Intelligence, in Proceedings of International Conference on Economics Business and Government Challenges , 2024, vol. 7, no. 1, pp. 124–133. Kim J, Lee H, Cho YH. Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Educ Inf Technol. 2022;27(5):6069–104. Jin S-H, Im K, Yoo M, Roll I, Seo K. Supporting students’ self-regulated learning in online learning using artificial intelligence applications. Int J Educ Technol High Educ. 2023;20(1):37. Lai C, Shum M, Tian Y. Enhancing learners’ self-directed use of technology for language learning: the effectiveness of an online training platform. Comput Assist Lang Learn. 2016;29(1):40–60. Vermisli S, Cevik E, Cevik C. The effect of perceived stress and digital literacy on student satisfaction with distance education. Rev da Esc Enferm da USP. 2022;56:e20210488. Martin F, Bolliger DU. Developing an online learner satisfaction framework in higher education through a systematic review of research. Int J Educ Technol High Educ. 2022;19(1):50. Kong S-C, Cheung WM-Y, Zhang G. Evaluating an artificial intelligence literacy programme for developing university students’ conceptual understanding, literacy, empowerment and ethical awareness. Educ Technol Soc. 2023;26(1):16–30. Dawa T, Dhendup S, Tashi S, Rosso M. University Students’ Perspective on ChatGPT and Technology Literacies. Educ Innov Pract, 7, 1, 2024. Mohammadi H. Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Comput Hum Behav. 2015;45:359–74. Hair JF, Risher JJ, Sarstedt M, Ringle CM. The Results of PLS-SEM Article information. Eur Bus Rev. 2018;31(1):2–24. Hair JF Jr, Hult GTM, Ringle CM, Sarstedt M, Danks NP, Ray S. Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature; 2021. Hu L, Bentler PM. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychol Methods. 1998;3(4):424. Rogaten J, et al. Reviewing affective, behavioural and cognitive learning gains in higher education. Assess Eval High Educ. 2019;44(3):321–37. Yao N, Wang Q. Factors influencing pre-service special education teachers’ intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness. Heliyon. 2024;10:14. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Q, pp. 425–78, 2003. Sudaryanto MR, Hendrawan MA, Andrian T. The Effect of Technology Readiness, Digital Competence, Perceived Usefulness, and Ease of Use on Accounting Students Artificial Intelligence Technology Adoption, in E3S Web of Conferences , 2023, vol. 388, p. 4055. Xiao J, Alibakhshi G, Zamanpour A, Zarei MA, Sherafat S, Behzadpoor S-F. How AI literacy affects students’ educational attainment in online learning: testing a structural equation model in higher education context. Int Rev Res Open Distrib Learn. 2024;25(3):179–98. Aurangzeb W, Kashan S, Rehman ZU. Investigating Technology Perceptions Among Secondary School Teachers: A Systematic Literature Review on Perceived Usefulness and Ease of Use. Acad Educ Soc Sci Rev. 2024;4(2):160–73. Al-Abdullatif AM, Alsubaie MA. ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy. Behav Sci (Basel). 2024;14(9):845. Saqr RR, Al-Somali SA, Sarhan MY. Exploring the acceptance and user satisfaction of AI-driven e-learning platforms (Blackboard, Moodle, Edmodo, Coursera and edX): an integrated technology model. Sustainability. 2023;16(1):204. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Discover Artificial Intelligence → Version 1 posted Editorial decision: Revision requested 27 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers invited by journal 10 Sep, 2025 Editor invited by journal 01 Sep, 2025 Editor assigned by journal 01 Sep, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 25 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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The domain of education, being a pivotal sector, is experiencing significant transformations as a result of the incorporation of AI technologies. These innovations incorporate a spectrum of tools such as intelligent tutoring systems and personalised learning environments, fundamentally changing the methods through which educational material is distributed and absorbed. With AI constantly reshaping the educational sphere, it is imperative for educational stakeholders to comprehend the effective adoption of AI technologies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. AI literacy, as refers to a set of skills that enable individuals to understand the effective use of AI technologies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], is increasingly acknowledged as an essential skill that students need to acquire especially within educational environments [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This literacy goes beyond mere technical expertise, including a wider range of skills that empower students to engage efficiently with AI technologies [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the significant emphasis on the cognitive aspects of AI literacy, such as understanding AI algorithms and data analysis, the affective AI dimensions, which encompass attitudes, emotions, and values, play a crucial yet underexplored role in AI engagement and learning effectiveness (Palmquist \u003cem\u003eet al.\u003c/em\u003e, 2025).\u003c/p\u003e\u003cp\u003eBeing a significant subset of AI literacy, affective AI literacy can be comprehensively understood through the ABCE model, comprising Affective, Behavioral, Cognitive, and Ethical dimensions. The multidimensional structure of ABCD model emphasizes the need for learners to not only comprehend how AI tools operates (cognitive), but also understand their appropriate usage behaviors (behavioral), emotional responses (affective) and engaging with AI technologies responsibly (ethical) (Ng et al., 2021). Considering this background, Affective AI literacy refers to the emotional and attitudinal elements that play a significant role in shaping how learners perceive and engage with AI technologies. This emotional engagement is particularly pertinent in educational contexts where AI tools are specifically designed to interact with students in customised and dynamic ways. Technology Acceptance Model (TAM) (Davis, 1989), together with the Cognitive-Affective Theory of Learning with Media (CATLM) (Moreno, 2006) provides a robust theoretical lens for analyzing the impact of learner's affective states, cognitive processes, and perceptions of technology on learning outcomes. CATLM speculates that when cognitive load is managed, motivation is maintained, and affective engagement is high, this lead towards meaningful learning. Similar factors are also highlighted in TAM's constructs of perceived usefulness and ease of use. The emotional response has a notable influence on student motivation, acceptance of technology, and, ultimately, the educational outcomes linked to AI utilization [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For instance, students with high intrinsic motivation might explore AI applications independently, while those with high self-efficacy are more likely to use AI in complex projects without feeling intimidated. Research indicates that affective factors often act as drivers of the acceptance and use of technology in educational settings, influencing the perceived ease of use and perceived usefulness (Lemay \u003cem\u003eet al.\u003c/em\u003e, 2019).\u003c/p\u003e\u003cp\u003eDespite the acknowledged significance of affective factors in technology adoption, a notable gap persists in literature. Previous research shows that students' emotional engagement with technology boosts motivation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], yet the affective dimensions of AI literacy remain critically underexplored within the university context. This oversight hinders the development of a holistic AI literacy framework that addresses all facets of student engagement. While cognitive skills are important, affective AI literacy is crucial for sustained learning. Therefore, this study aims to bridge this gap by examining how affective AI literacy impacts perceived usefulness, perceived ease of use, and student satisfaction in an academic setting [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePakistan\u0026rsquo;s higher education system, with over 200 institutions, is rapidly advancing to integrate modern technologies like AI despite challenges like resource limitations and regional disparities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As AI transforms learning, understanding the affective AI domain becomes essential. Student\u0026rsquo;s attitudes and motivations influence their engagement to AI technologies, impacting learning effectiveness. By focusing on COMSATS University Islamabad, Pakistan, well-known for its integration of AI into educational methodologies across three distinct campuses in different parts of the country, this research provides a valuable insights into how students' emotional and attitudinal reactions to AI impact their practical engagement with the technology. These campuses collectively offer a thorough depiction of the diverse cultural backgrounds in the country, thereby enhancing the richness of the study's data. The outcomes are expected to guide the development of novel approaches in educational policies and curriculum formulation, especially in cultivating favorable emotional and attitudinal orientations towards AI among students.\u003c/p\u003e\u003cp\u003eThis paper proceeds by reviewing the relevant literature, detailing the methodology used to collect and analyse the data, presenting the findings, and finally discussing the implications of the results for theory and practice in higher education.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eTheoretical Framework\u003c/p\u003e\n\u003cp\u003eAI literacy in higher education is an emerging and vital area that focuses on equipping students and educators with the knowledge needed to effectively engage with AI technologies. Research indicates that AI literacy encompasses a range of competencies, from basic understanding of AI concepts to the ability to work with AI systems in complex, ambiguous situations [11], [12].The development of AI literacy is essential for preparing students to navigate an AI-driven world [13]. It has been conceptualized in various ways, but more recently, drawing from Bloom's taxonomy the ABCE model has been adopted. This model addresses the affective, behavioral, cognitive, and ethical dimensions of AI literacy [2]. However the affective dimension of AI literacy is critical, focusing on students’ emotions, attitudes, and motivation towards AI[14], [15]. It actually refers to a person’s innate emotional desire to accept the change. Positive emotions can enhance interest and motivation, while negative feelings might hinder learning progress. This emotional component is especially important as it influences students' overall engagement with AI learning materials and activities [16]. Affective AI literacy encompasses intrinsic motivation, self-efficacy, career interest, and confidence, making it an essential component in fostering a comprehensive understanding of AI. For example, high levels of intrinsic motivation, which reflect an internal desire to learn about AI for personal satisfaction or intellectual curiosity, can drive deeper engagement and exploration. Self-efficacy is students’ belief in their capability to understand and work with AI, also plays a crucial role, as students who feel competent are more likely to approach challenges confidently. Additionally, interest in AI-related careers can further shape affective AI literacy, as students with a career-oriented mindset may feel more motivated to learn about AI’s practical applications. Finally, confidence in AI knowledge can reinforce students’ willingness to participate in AI-related discussions and projects, creating a positive feedback loop that bolsters both interest and skill acquisition [17].\u003c/p\u003e\n\u003cp\u003eHow learners process information in multimedia environments by integrating both cognitive and affective dimensions, is thoroughly explained by comprehensive framework of Cognitive-Affective Theory of Learning with Media (CATLM) (Moreno and Mayer, 2007). This theory speculates that meaningful learning is the outcome of cognitively engaged (through processes such as selecting, organizing, and integrating information), affectively motivated and emotionally supported mindset. Considering the context of AI literacy, CATLM together with Technology Acceptance Model (TAM) (Davis, 1989) offers a nuanced understanding of how learners accept and effectively engage with AI tools. CATLM emphasizes on the emotional and motivational factors that maintain engagement and create deeper conceptual understanding. In the background of AI literacy, usability of AI tools as well as the cognitive-affective conditions that support meaningful interaction with them should both be considered (Zawacki-Richter \u003cem\u003eet al.\u003c/em\u003e, 2019). Consequently, CATLM provides a comprehensive theoretical lens for designing AI-based learning environments that enhance learners’ motivation, confidence, capacity to develop AI-related competencies, and also stay aligned with user expectations.\u003c/p\u003e\n\u003cp\u003eOn the other hand, the Technology Acceptance Model (TAM), developed by Davis (1989), provides a basis for understanding user acceptance of technology. The core premise of the model is that two key factors: the perceived usefulness and the perceived ease of use of a technology, shape an individual's attitude towards it, which subsequently influences their intention to adopt and utilize the technology [19]. Furthermore, student satisfaction is a key indicator of the effectiveness of AI-enabled learning [20]. Satisfaction reflects the extent to which students' expectations and experiences with AI-enabled technologies align, and it is a crucial predictor of continued use and engagement. Student satisfaction can be influenced by various factors, including perceived usefulness and perceived ease of use, as well as the emotional and social aspects of interacting with AI. Affective AI literacy may play a crucial role in shaping student satisfaction, as it can help students navigate the complex emotional and social dynamics involved in working with AI systems [21], [22].\u003c/p\u003e\n\u003cp\u003eConceptual Framework\u003c/p\u003e\n\u003cp\u003eThis investigation explores the impact of Affective AI Literacy on perceived usefulness, perceived ease of use, and student satisfaction. The proposed conceptual framework (see Fig.\u0026nbsp;1) introduces four main hypotheses to study the relationships between affective AI literacy and essential outcome variables.\u003c/p\u003e\n\u003cp\u003ePerceived usefulness is an individual's assessment of a technology's ability to effectively support or enhance human activities. When users perceive a technology as useful in meeting their needs, their interest and engagement with that technology tend to increase, leading to higher adoption rates and more frequent use [23], [24]. Within the context of AI technologies, affective AI literacy, the combination of students’ emotional and motivational readiness to engage with AI, is expected to positively influence their perception of AI’s usefulness. When students feel both confident and motivated in their understanding of AI, they are more likely to view these technologies as beneficial tools that can enrich their educational experience and enhance their productivity [25], [26]. Thus, it is hypothesized that students with high levels of affective AI literacy will be better positioned to appreciate the advantages and potential applications of AI, which, in turn, will increase their perceived usefulness of these technologies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH1: High affective AI literacy leads to higher perceived usefulness.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePerceived ease of use refers to the extent to which individuals believe that a technology will make their activities more manageable and require minimal effort [23]. This perception is an essential factor that can be significantly shaped by affective AI literacy. Affective AI literacy, which encompasses students' emotional readiness and motivation to engage with AI, influences their self-confidence and perceived ability to use AI technologies effectively. When students possess high level of affective AI literacy, they typically feel more capable and assured in their interactions with AI, which in turn lowers the perceived complexity and effort associated with using these tools [27]. Consequently, students with high affective AI literacy are expected to find AI technologies more intuitive and accessible, as their positive attitudes and motivation help create smoother and more user-friendly experiences with AI systems. Therefore, it is assumed:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH2: High affective AI literacy leads to higher perceived ease of use.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStudent satisfaction (SS) represents students' contentment with their learning activities and the services provided, serving as a pivotal factor in determining the quality of education and sustaining engagement in learning [28]. As a critical outcome in educational settings, student satisfaction reflects the degree to which students feel positively about their overall educational experience [29]. When students feel motivated, confident, and competent in their interactions with AI, they are more likely to derive enjoyment from the learning process, thereby enhancing their satisfaction with their education [22]. Thus, it is assumed that high levels of affective AI literacy can contribute significantly to student satisfaction by cultivating positive attitudes and emotional responses towards AI, leading to a more fulfilling and satisfying educational experience.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eH3: High affective AI literacy leads to higher student satisfaction.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAffective AI literacy may enhance student satisfaction indirectly by positively shaping their perceptions of AI technology's utility. When students possess strong affective AI literacy, they feel more confident, motivated, and emotionally prepared to engage with AI. This readiness allows them to see AI not merely as a tool, but as a valuable asset that can enrich their learning process, improve productivity, and offer practical solutions to educational challenges. As students begin to perceive AI technologies as beneficial and relevant to their learning goals, their overall satisfaction with the educational experience is likely to increase. This positive view fosters a sense of accomplishment, engagement, and enthusiasm toward their studies, ultimately contributing to a more fulfilling and enjoyable learning environment. In this way, affective AI literacy indirectly boosts student satisfaction by making AI technologies appear not only accessible but also impactful and relevant to their educational journey [22]. Therefore, it is hypothesized that the positive impact of affective AI literacy on student satisfaction is partly explained by its influence on perceived usefulness and perceived ease of use.\u003c/p\u003e\n\u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eH4a: Perceived usefulness mediate the relationship between affective AI literacy and student satisfaction.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eH4b: Perceived ease of use mediate the relationship between affective AI literacy and student satisfaction.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003ePopulation\u003c/p\u003e\u003cp\u003eThis study focuses on university students with prior AI coursework experience, specifically targeting those from COMSATS University Islamabad (CUI). The reason for selecting CUI is as it is ranked 3rd out of 67 public sector universities of Pakistan (Rankings, 2024). Working with 5 faculties (Engineering, Information Science and Technology, Business Administration and Architecture and Design), CUI has a trend-setting pedagogical approach. CUI\u0026rsquo;s campuses in Abbottabad, Islamabad, and Lahore are selected to ensure a diverse sample representing various cultural backgrounds across the country. The population consists of undergraduate students of computer science department from these three campuses, in their last two semesters who have taken AI courses (Artificial Intelligence, Programming for Artificial Intelligence, Introduction to Computer Vision, Machine Learning Fundamentals, Natural Language Processing, Artificial Neural Networks and Deep Learning and Knowledge-Based Systems) as part of their degree program. Out of 3,224 students enrolled in the computer science department across these campuses, 613 students fit the target profile for the study.\u003c/p\u003e\u003cp\u003eSampling\u003c/p\u003e\u003cp\u003eA convenience sampling technique is selected due to the accessibility and proximity of the target student group, enabling efficient data collection. This choice is based on logistical considerations, given the dispersed nature of the campuses and constraints in time and resources. Additionally, convenience sampling facilitated rapid data gathering from students who met the study\u0026rsquo;s criteria of having completed relevant AI courses. This approach aligns with prior research in educational technology, where convenience sampling has been used to obtain targeted insights on digital literacy and AI competencies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Based on a 95% confidence interval, the calculated sample size is 237.\u003c/p\u003e\u003cp\u003eMeasurement Instrument\u003c/p\u003e\u003cp\u003eMeasurement items (See Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for each construct are carefully selected and adapted from prior research to ensure both validity and reliability.\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\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\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eItems\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffective AI literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 items\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Usefulness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 items\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Ease of Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 items\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 items\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27 items\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eMeasurement Instrument\u003c/h3\u003e\n\u003cp\u003eA five-point Likert scale, ranging from \"strongly disagree\" to \"strongly agree,\" is used, drawing on items from relevant and validated scales commonly utilized by scholars and researchers in their respective fields (For final questionnaire see Appendix).\u003c/p\u003e\u003cp\u003eData Collection \u0026amp; Analysis Techniques\u003c/p\u003e\u003cp\u003eThe questionnaire is distributed to students through their teachers, who provided both physical copies and digital versions via email and WhatsApp. With regular reminders to students and their teachers, a total of 237 complete questionnaires are collected and subsequently used for analysis. Data analysis is performed using SEM (Structure Equation Modeling) with the help of software tools SPSS and Smart PLS incorporating: Methods of descriptive statistics like frequency distribution, measure of central tendency and dispersion, preliminary analysis using correlation and factor analysis, validity and reliability analysis, path analysis for hypothesis testing and mediation analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eRespondent\u0026rsquo;s Profile \u0026amp; Descriptive Statistics\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eRespondent\u0026rsquo;s Demographics\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN (237)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eDescriptive Statistics\u003c/h3\u003e\n\u003cp\u003eCorrelation Analysis\u003c/p\u003e\n\u003cp\u003ePEU (r\u0026thinsp;=\u0026thinsp;0.613, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), PU (r\u0026thinsp;=\u0026thinsp;0.655, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and SS (r\u0026thinsp;=\u0026thinsp;0.604, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) all show a positive correlation with AI, suggesting that AI positively influences student satisfaction, driven by its perceived ease of use and perceived usefulness in education (see Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). A strong positive correlation indicates that respondents who perceive ease of use favorably also place a high value on perceived usefulness (r\u0026thinsp;=\u0026thinsp;0.728, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and student satisfaction (r\u0026thinsp;=\u0026thinsp;0.754, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The results show that student satisfaction is highly related to the perceived usefulness (r\u0026thinsp;=\u0026thinsp;0.691, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePEU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.613**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.655**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.728**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.604**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.754**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.691**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNote: **. Correlation is significant at the 0.01 level (2-tailed).\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eAI (Artificial Intelligence), PU (Perceived Usefulness), PEU (Perceived Ease of Use)\u003c/em\u003e,\u003c/p\u003e\n\u003cp\u003eSS\u0026thinsp;=\u0026thinsp;Student Satisfaction\u003c/p\u003e\n\u003cp\u003eMeasurement Model\u003c/p\u003e\n\u003cp\u003eThe study uses a factor analysis to identify the latent components that influence respondents\u0026apos; opinions of AI, such as Perceived Usefulness (PU), Perceived Ease of Use (PEU) and its impact on Student Satisfaction (SS) (See Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The study evaluates the reliability and validity of the measurement instruments by calculating Cronbach\u0026apos;s alpha, composite reliability, and average variance extracted (AVE) for the constructs. Cronbach\u0026rsquo;s alpha values ranged from 0.872 to 0.908, suggesting that the internal consistency for the constructs is satisfactory. The composite reliability values shows consistent patterns, varying between 0.921 and 0.936 [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. These results indicate that the dependability is satisfactory, as values over the recommended threshold of 0.70 indicate that the measurement instruments are dependable. In addition, the average variance extracted values, which ranged from 0.596 to 0.829, indicated that the constructs were able to explain a significant amount of their own variability [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. This finding supports the idea that the constructs have convergent validity. Overall, the evaluations of reliability and validity instill trust in the strength and accuracy of the measurement equipment used in the study, hence bolstering the legitimacy of the research findings.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eMeasurement Model\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicators\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor loadings\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComposite Reliability\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003cp\u003eQ7\u003c/p\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eAI\u0026thinsp;=\u0026thinsp;Artificial Intelligence, PU\u0026thinsp;=\u0026thinsp;Perceived Usefulness, PEU\u0026thinsp;=\u0026thinsp;Perceived Ease of Use, SS\u0026thinsp;=\u0026thinsp;Student Satisfaction\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eDiscriminant Validity\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that all construct pairs had HTMT ratios below the widely accepted cutoff of 0.85, indicating respectable discriminant validity [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. As an illustration, the HTMT ratios of 0.665, 0.708 and 0.664 are found between AI and PEU, PU and SS respectively. Collectively, these findings confirm that the study\u0026apos;s constructs are unique from one another and give confidence that the measurement model accurately reflects the distinctive features of each underlying construct, supporting the measurement model\u0026apos;s discriminant validity (See Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEOU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAl\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eHeterotrait-Monotrait Ratio (HTMT)\u003c/h2\u003e\n \u003cp\u003eAI\u0026thinsp;=\u0026thinsp;Artificial Intelligence, PU\u0026thinsp;=\u0026thinsp;Perceived Usefulness, PEU\u0026thinsp;=\u0026thinsp;Perceived Ease of Use, SS\u0026thinsp;=\u0026thinsp;Student Satisfaction\u003c/p\u003e\n \u003cp\u003eModel Fit\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e compares model fit indices for the Saturated and Estimated Models. The SRMR for the Saturated Model is 0.076, indicating a good fit (below the 0.08 threshold) [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e], while the Estimated Model has an SRMR of 0.109, slightly exceeding the acceptable range. The Chi-square values are 671.475 for the Saturated Model and 707.251 for the Estimated Model, with lower values suggesting a better fit. Overall, the Saturated Model shows a better fit based on lower SRMR, d_ULS, and Chi-square values, along with a higher NFI, though both models exhibit similar fit levels.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cem\u003eModel Fit\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSaturated model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimated model\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRMR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ed_ULS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ed_G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-square\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e671.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e707.251\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNFI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eHypotheses Testing\u003c/p\u003e\n \u003cp\u003eThe path value is demonstrated in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The AI -\u0026gt;PU pathway\u0026apos;s mean is 0.655, AI -\u0026gt;PEU pathway\u0026apos;s mean is 0.613, AI -\u0026gt;SS pathway\u0026apos;s mean is 0.148. The statistical analysis reveals a significant positive association between Artificial Intelligence and latent variables, as evidenced by the p-value of 0.000. Hence supporting hypotheses H1, H2 and H3.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePath Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl -\u0026gt;PU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported H1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl -\u0026gt;PEU\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported H2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAl -\u0026gt;SS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported H3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003ePath coefficients\u003c/h2\u003e\n \u003cp\u003eAI\u0026thinsp;=\u0026thinsp;Artificial Intelligence, PU\u0026thinsp;=\u0026thinsp;Perceived Usefulness, EOU\u0026thinsp;=\u0026thinsp;Perceived Ease of Use, SS\u0026thinsp;=\u0026thinsp;Student Satisfaction\u003c/p\u003e\n \u003cp\u003eMediation Analysis\u003c/p\u003e\n \u003cp\u003eThe respective results are shown in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. AI has a statistically significant positive direct effect on SS (\u0026beta;\u0026thinsp;=\u0026thinsp;0.148, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, results of mediation analysis show that AI has a significant positive direct effect on PU (\u0026beta;\u0026thinsp;=\u0026thinsp;0.655, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and PU has a significant positive direct effect on SS (\u0026beta;\u0026thinsp;=\u0026thinsp;0.236, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Moreover, the indirect effect (i.e., mediated by PU) of AI on SS showed a weak but positive effect (\u0026beta;\u0026thinsp;=\u0026thinsp;0.115, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) hence providing partial mediation and acceptance of hypotheses H4a. The results of mediation analysis show that AI has a significant positive direct effect on EOU (\u0026beta;\u0026thinsp;=\u0026thinsp;0.613, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and EOU has a significant positive direct effect on SS (\u0026beta;\u0026thinsp;=\u0026thinsp;0.492, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Moreover, the indirect effect (i.e., mediated by EOU) of AI on SS show positive effect (\u0026beta;\u0026thinsp;=\u0026thinsp;0.301, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) hence providing partial mediation and acceptance of hypotheses H4b.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePaths coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific Indirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eComment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI \u0026rarr; SS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003e0.148 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI \u0026rarr; PU \u0026rarr; SS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.655***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.236***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.155**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported H4a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial mediation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI \u0026rarr; PEU \u0026rarr; SS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.613***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.492***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.301***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSupported H4b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial mediation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eResults for Mediation\u003c/h2\u003e\n \u003cp\u003eAI\u0026thinsp;=\u0026thinsp;Artificial Intelligence, PU\u0026thinsp;=\u0026thinsp;Perceived Usefulness, EOU\u0026thinsp;=\u0026thinsp;Perceived Ease of Use, SS\u0026thinsp;=\u0026thinsp;Student Satisfaction \u003cem\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAI literacy is a leading trend in the education sector and recognized as an essential skill increasingly expected of students. Among the four main areas of AI literacy, Affective AI literacy is particularly important, as it encompasses students' natural emotional and motivational responses to a subject [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Accordingly, the focus of this study is to investigate the impact of Affective AI literacy on perceived usefulness, perceived ease of use, and student satisfaction. The findings indicate a significant positive relationship (path coefficient\u0026thinsp;=\u0026thinsp;0.655***) between affective AI literacy and perceived usefulness. When students score high in the affective aspect of AI literacy, which includes their emotional and motivational readiness to engage with AI, they are more likely to perceive AI as useful. Perceived usefulness indicates a user\u0026rsquo;s belief in a technology's ability to provide assistance in different tasks and enhance productivity. This actually refers to individual's evaluation of how well a technology can improve or streamline human activities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. When users see a technology as beneficial in meeting their needs, their interest and engagement with that technology tend to grow, leading to higher adoption rates and more frequent use. In the context of AI, affective AI literacy (including students' emotional confidence and intrinsic motivation to use AI) is expected to strengthen the user\u0026rsquo;s perception of AI\u0026rsquo;s value. Students who feel confident in comprehending the use of AI tools, and at the same time they feel motivated, are more inclined to view it as a valuable tool. AI becomes a dynamic partner in their education, helping them manage tasks more efficiently, gain deeper insights, or access customized learning materials. Consequently, this positive viewpoint promotes a deeper integration of AI into their daily academic practices, resulting in improved productivity, greater engagement and enrich learning experiences [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe results of the present research also demonstrated a significant positive association (0.613***) between AI literacy and perceived ease of use, which refers to the extent to which users anticipate using new technology with minimal difficulty [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Studies have already shown that AI literacy contributes to a sense of empowerment and autonomy among users, which in turn raises their perceived ease of use (Venkatesh \u003cem\u003eet al.\u003c/em\u003e, 2003; Sudaryanto \u003cem\u003eet al.\u003c/em\u003e, 2023). When students have a high level of affective AI literacy, they are more likely to possess a strong motivation, confidence, interest, and self-efficacy in using AI technology. This literacy equips them with not only the technical skills but also the emotional and cognitive readiness to engage meaningfully with AI tools. Consequently, students who feel capable and knowledgeable about AI are less likely to experience pressure or nervousness around AI technologies, which can otherwise be a significant barrier to use. The sense of competence that comes with affective literacy creates a positive perception of AI\u0026rsquo;s ease of use, making the technology seem accessible and beneficial to their personal learning or productivity goals. This positive attitude also leads to reduces user resistance, a common challenge in technology adoption, since students with high AI literacy are less likely to view AI systems as overly complex. Instead, they recognize the utility of these tools and approach them as resources that enhance their capabilities rather than detract from them. Research supports that individuals with a strong sense of self-efficacy and affective technological literacy are tend to involve positively with digital tools, considering them easier to use and incorporating them more readily into their daily activities. As a result, their productivity and learning increase substantially (Venkatesh \u003cem\u003eet al.\u003c/em\u003e, 2003; Sudaryanto \u003cem\u003eet al.\u003c/em\u003e, 2023).\u003c/p\u003e\u003cp\u003eStudent satisfaction (SS) plays a major role in shaping the quality of education and preserving the aspect of learning (Vermisli \u003cem\u003eet al.\u003c/em\u003e, 2022). The current investigation has established a significant positive relationship (0.148**) between affective AI literacy and student satisfaction. When students possess a high level of AI literacy, they are more comfortable and enthusiastic about interacting with AI technologies, which enhances their engagement and satisfaction with these tools. High affective AI literacy tends to promote engagement, and confidence in using AI tools, leading to a more enjoyable and satisfying educational experience. For example, a well-developed understanding with AI have been shown to reduce technology-related anxiety and promote positive attitudes, which in turn increase academic motivation and satisfaction. Moreover, when students can confidently engage with AI tools, they experience enhanced academic well-being, which has been positively linked to educational outcomes and personal satisfaction [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, when students are motivated and self-assured in using AI, they are more likely to explore its features fully, discovering the ways in which AI may streamline their work, personalize their learning, and make studying more effective. Research suggests that such positive experiences with technology usage contribute to heightened satisfaction, as students feel their needs and expectations are met, and they experience fewer frustrations or challenges [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Eventually, when students feel confident in their AI literacy, they not only adopt AI tools quickly but also derive more value from them, leading to greater satisfaction and enhanced learning experiences.\u003c/p\u003e\u003cp\u003eThe findings indicate that while there is a positive relationship between affective AI literacy and student satisfaction, introducing perceived ease of use and perceived usefulness as mediators significantly strengthens this impact. Particularly, perceived ease of use acts as a strong mediator (0.301***) between affective AI literacy and student satisfaction. A strong mediation of perceived ease of use between affective AI literacy and student satisfaction underscores the fundamental role of user-friendliness in the educational experience with AI-driven tools. Affective AI literacy involves understanding and effectively using AI by incorporating emotions, leads to enhanced students' ability to engage positively with educational technology. However, if these AI tools are perceived as challenging to use, this may not directly guide to increased satisfaction. When students perceive these tools as easy to use, it bridges the gap between literacy and satisfaction, allowing them to feel competent and empowered while interacting with AI in learning environments. Consequently, perceived ease of use can significantly enhance the positive effects of affective AI literacy on student satisfaction, highloghting its importance as a mediator in educational technology adoption (Al-Abdullatif and Alsubaie, 2024; Aurangzeb \u003cem\u003eet al.\u003c/em\u003e, 2024). Moreover, studies applying the Technology Acceptance Model (TAM) show that students\u0026rsquo; perceptions of usability directly affect their willingness to engage with AI in learning, indicating that ease of use amplifies the positive effects of AI literacy on satisfaction and motivation to adopt new tools [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Incorporating AI tools which are easy to use in educational settings can help in optimizing the benefits of AI literacy, assisting learners to feel more confident in their tech interactions. While perceived ease of use plays a stronger mediating role, perceived usefulness also significantly enhances the relationship (0.155**) between affective AI literacy and student satisfaction. Student\u0026rsquo;s satisfaction with AI tools increases when they find these tools helpful in achieving their educational goals, although not impacting as highly as perceived ease of use does. This suggests that while perceived ease of use primarily facilitates the comfort and accessibility of AI tools, perceived usefulness contributes by assuring students of the tool\u0026rsquo;s value in supporting their learning objectives [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Together, these factors underscore the importance of both intuitive design and clear utility in promoting satisfaction with AI in education (Luo \u003cem\u003eet al.\u003c/em\u003e, 2025).\u003c/p\u003e\u003cp\u003eFurthermore, these conclusions are also supported by the Cognitive Affective Theory of Learning with Media (CATLM), which signifies the relationship between cognitive and affective processes in multimedia learning environments. This theory suggests that when learners are emotionally involved with the technology and find these tools beneficial and easy to use, their meaningful learning outcomes enhances along with increased motivation and reduced cognitive load. Affective AI literacy, by enhancing emotional readiness and motivation, stimulates these areas. Ease of use lessens unnecessary cognitive effort, helping students to focus on meaningful learning tasks. Perceived usefulness contributes by reinforcing the instrumental value of AI in achieving learning outcomes, completing the motivational loop. Hence, incorporating easy to use and pedagogically meaningful AI tools can enhance the benefits of affective AI literacy. Collectively, insights from both TAM and CATLM highlight the importance of designing AI-driven educational technologies that are not only functionally effective but also emotionally intuitive, in order to maximize student satisfaction and academic success (Luo \u003cem\u003eet al.\u003c/em\u003e, 2025).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAffective AI literacy has the capability to considerably increase student satisfaction and learning outcomes generally. By providing learners with the skills to comprehend and interact effectively with emotionally responsive AI, affective AI literacy facilitates them to benefit from technology that will be supportive of their learning needs. This literacy fosters a perception of AI tools as both beneficial and easy to use, which directly influences satisfaction by making learning more engaging and tailored to their needs. Students who feel competent in using AI-driven educational tools experience a smoother, more enjoyable learning process, ultimately reducing frustrations and promoting a positive attitude toward learning technology. Eventually, affective AI literacy bridges the gap between technological proficiency and emotional engagement, creating a supportive environment that strengthens confidence, satisfaction, and motivation in students\u0026rsquo; academic journeys.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eImplications of the Study\u003c/h2\u003e\u003cp\u003eThis study\u0026rsquo;s findings have significant implications, especially for higher education in developing countries like Pakistan. The results show how crucial it is to include emotional factors like motivation, confidence, and ethical awareness when designing AI courses. Doing this helps create learning experiences that feel more engaging and personalized, making students more satisfied and ready to embrace AI technology. It also points to the need for easy-to-use educational tools that connect with both the feelings and thinking of students. On a social level, helping students develop emotional awareness around AI can lead to more inclusive and thoughtful use of technology. It encourages people to use AI in ways that respect different cultures and values. In countries facing resource constraints and rapid digital transformation, such an approach is vital for ensuring equitable access to AI education and preparing students to navigate the ethical challenges of emerging technologies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThe study has several limitations that may affect the generalizability and validity of its findings. First, it employs a convenience sampling method and focuses exclusively on undergraduate computer science students from COMSATS University campuses in Pakistan, which may not represent the broader student population. This sampling limitation restricts the application of the findings to other disciplines, regions, and potentially different student demographics. Additionally, the research relies on self-reported data collected via an online questionnaire, a method that can introduce biases like social desirability or inaccuracies in self-assessment, affecting the data's reliability. The cross-sectional study design further limits the ability to draw causal conclusions or observe changes in affective AI literacy and student satisfaction over time. The absence of potential mediating factors, such as enjoyment, self-regulation, academic performance, social influence, technological anxiety, and learning autonomy, suggests that additional research is needed to gain a fuller understanding of factors influencing student satisfaction with AI. Future studies could address these limitations by incorporating a more diverse sample, applying longitudinal research methods, and including participants from a variety of academic backgrounds and geographical regions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAvailability of data and materials\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIt is available on request.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThere were no competing interests among the participants during the course of this research.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThere was no funding received for carrying out this research.\u003c/p\u003e\n\u003ch2\u003eAuthors’ contributions\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all the teachers who generously contributed their time and effort to assist in the data collection process for this study. Their support, cooperation, and commitment were invaluable in ensuring the accuracy and completeness of the data.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003e\u0026nbsp; Not applicable\u003c/p\u003e\n\u003ch2\u003eConsent to participate\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNot applicable\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eConsent to publish\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNot applicable\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eEthics approval\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study was reviewed and approved by the Academic Research Committee at COMSATS University Islamabad. All procedures involving human participants were in accordance with ethical standards, and informed consent was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZawacki-Richter O, Mar\u0026iacute;n VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education\u0026ndash;where are the educators? Int J Educ Technol High Educ. 2019;16(1):1\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNg DTK, Wu W, Leung JKL, Chiu TKF, Chu SKW. Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. Br J Educ Technol. 2024;55(3):1082\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAshok M, Madan R, Joha A, Sivarajah U. Ethical framework for Artificial Intelligence and Digital technologies. 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Sustainability. 2023;16(1):204.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Affective AI literacy, Perceived usefulness, Perceived ease of use, Student satisfaction, Technology Acceptance Model, Higher Education Institution","lastPublishedDoi":"10.21203/rs.3.rs-7453581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7453581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the impact of affective AI literacy on student satisfaction in Pakistan\u0026rsquo;s evolving higher education sector, which is placing greater emphasis on sustainable education and market-relevant skills. Technology Acceptance Model together with the Cognitive-Affective Theory of Learning with Media (CATLM) is used as theoretical lens for analysing this investigation. Conducted across three geographically distinct campuses of COMSATS University Islamabad, the research uses a convenience sampling approach. 237 computer science undergraduates participated through an online survey. Measurement items are adapted from established research to ensure validity and reliability, and the data is analysed using Structural Equation Modeling (SEM). Results indicate that affective AI literacy positively impacts students' perceptions of AI tools' usefulness (β\u0026thinsp;=\u0026thinsp;0.655, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), ease of use (β\u0026thinsp;=\u0026thinsp;0.613, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and satisfaction (β\u0026thinsp;=\u0026thinsp;0.148, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Perceived usefulness and perceived ease of use are found to mediate student satisfaction, enhancing student engagement and personalisation in learning. The study urges higher education to include emotional, ethical, and user-friendly AI considerations into curricula, examining how feelings and attitudes shape students' perceptions of AI\u0026rsquo;s usefulness, ease of use, and satisfaction to foster holistic AI literacy. However, limitations include the use of convenience sampling, which focused exclusively on computer science undergraduates from specific campuses, potentially limiting the generalisability to other disciplines or regions. Additionally, future research could explore additional factors like enjoyment, social influence, and academic performance to gain a broader understanding of AI literacy\u0026rsquo;s impact on student satisfaction.\u003c/p\u003e","manuscriptTitle":"Unlocking Student Satisfaction through Affective AI Literacy: Advancing Technology Acceptance in AI-Enhanced Education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 13:08:50","doi":"10.21203/rs.3.rs-7453581/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-27T10:19:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T10:26:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T08:50:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331453083404465486391839470283641735041","date":"2025-09-15T19:20:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T17:21:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185055306409307512667688342832394143497","date":"2025-09-13T01:12:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189836524253478539994749945833317586077","date":"2025-09-12T19:20:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261674645279199818057498704360368889384","date":"2025-09-11T01:45:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-10T19:11:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-01T13:45:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T04:26:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-01T04:25:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Artificial Intelligence","date":"2025-08-25T12:01:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78ee13b5-188e-45e7-96fb-7dc6028e0528","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:03:32+00:00","versionOfRecord":{"articleIdentity":"rs-7453581","link":"https://doi.org/10.1007/s44163-025-00806-8","journal":{"identity":"discover-artificial-intelligence","isVorOnly":false,"title":"Discover Artificial Intelligence"},"publishedOn":"2026-01-31 15:59:15","publishedOnDateReadable":"January 31st, 2026"},"versionCreatedAt":"2025-09-18 13:08:50","video":"","vorDoi":"10.1007/s44163-025-00806-8","vorDoiUrl":"https://doi.org/10.1007/s44163-025-00806-8","workflowStages":[]},"version":"v1","identity":"rs-7453581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7453581","identity":"rs-7453581","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}