AI Paradox in Higher Education: Understanding Over-Reliance, Its Impact, and Sustainable Integration

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Hussein This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6127885/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rapid AI development has significantly changed education. This study explored factors influencing educators' over-reliance on AI, the consequences of AI dependency, and strategies to balance AI use in higher education. A qualitative approach using semi-structured interviews and focus groups collected data from 46 diverse participants. Thematic analysis revealed factors driving AI dependency—including academic reputation, self-efficacy, and institutional policies—and consequences such as skills atrophy, procrastination, and social fragmentation. Findings suggest hybrid intelligence and balanced AI teaching can be beneficial. Limitations include a small sample, and future research should target larger, more diverse populations. AI dependency Generative AI procrastination AI reliance hybrid intelligence Introduction The rapid incorporation of artificial intelligence in education (AIED) has significantly changed the educational landscape, providing unique chances for tailored teaching and learning experiences, instant feedback, and enhanced efficiency [ 1 ]. Consequently, institutions across the globe are increasingly utilizing AI-powered tools, including generative models like ChatGPT, to customize the educational process, streamline administrative duties, and reduce faculty workload [ 2 ]. Despite the important benefits, stakeholders in the field of education have concerns regarding over-dependence on AI, simply defined as students’ and instructors’ uncritical acceptance of AI generated material [ 3 ]. In addition, overreliance on AIED hinders cognitive involvement, productive critical thinking and decision making and long-term skill growth [ 4 ]. While literature on the topic of AIED is abundant, it primarily focuses on the gains of integrating artificial intelligence in the educational setting [ 5 ]. Ethical, cognitive and professional challenges related to AIED, on the other hand, are far less explored [ 6 , 7 ]. Furthermore, most of the available research on overreliance on AIED is investigated in relation to students exploring how excessive dependence on AI can negatively impact their ability to think critically, independently and creatively and to perform self-directed learning [ 4 , 8 ]. Other student-related studies have linked AI reliance to psychological factors such as perfectionism, impulsivity, and the need for immediate cognitive relief, drawing comparisons to internet and social media dependence [ 9 ]. Empirical research investigating AI dependency among educators is still meager [ 7 ] and most of the existing studies explore how AI enhances educators' productivity and decision-making [ 10 ]. Risks and challenges pertaining to AIED, on the other hand, are less explored. In addition, most research on AIED has been conducted in Western or Chinese contexts, with limited cross-cultural perspectives [ 11 ]. Thus, the purpose of this study is threefold: to explore the underlying causes of educators’ overreliance on AI; to investigate how this reliance affects teaching autonomy, decision-making, and long-term practices and pedagogical identity; and to highlight potential challenges associated with AI reliance while suggesting strategies to overcome them. Through this empirical research which focuses on Palestinian, this research will provide insights into various aspects related to AI integration in higher education. The findings will inform policy recommendations, faculty development programs, and strategies to promote responsible AI use, ensuring that AI serves as a complement rather than a substitute for educators' professional expertise and pedagogical agency. Research questions 1. What are the key factors contributing to AI dependency among educators in higher education? 2. How does AI dependency impact educators' teaching autonomy, decision-making, and professional identity? 3. What strategies can help educators balance AI use while maintaining pedagogical creativity and instructional effectiveness? Theoretical Foundation of the Study The I-PACE Model (Interaction of Person-Affect-Cognition-Execution) , developed by [ 12 ], provides a comprehensive framework for understanding problematic technology use, including AI dependency. It explains how individual characteristics, emotional responses, cognitive processes, and behavioral execution interact over time, shaping patterns of technology reliance. The model consists of four key components: Person-Level Predispositions (P) , which include personality traits and cognitive styles that influence susceptibility to AI dependence; Affect (A) , which refers to emotional states such as stress, anxiety, or workload pressure that drive AI use; Cognition (C) , which examines decision-making processes and the extent to which AI alters critical thinking and pedagogical autonomy; and Execution (E) , which explores the long-term behavioral consequences of AI reliance, such as cognitive deskilling or shifts in professional identity. The I-PACE model serves as a valuable theoretical foundation for understanding how AI dependency develops, the factors that reinforce it, and its impact on educators’ autonomy and professional agency. By applying this model, the study analyzes the psychological implications of AI reliance, offering insights into how educators interact with AI tools and the broader implications for teaching and learning. Previous studies using I-PACE Research utilizing this model has demonstrated how individual predispositions, emotional states, and cognitive processes interact to influence excessive reliance on digital tools. For instance, studies on internet and social media addiction have found that individuals with high neuroticism, impulsivity, or perfectionism are more likely to develop problematic usage patterns, often as a coping mechanism for stress [ 12 ]. Similarly, research on AI reliance in education suggests that educators and students with low self-efficacy in digital literacy or high performance expectations tend to offload cognitive tasks to AI, reducing engagement in critical thinking and problem-solving [ 8 , 13 ]. Studies have also shown that institutional pressures and workload demands can lead professionals, including educators, to depend on AI for efficiency , sometimes at the cost of pedagogical autonomy and professional agency . By applying the I-PACE model , these studies provide a structured framework for analyzing how technology use evolves from occasional assistance to habitual dependence , highlighting the psychological, cognitive, and behavioral factors that contribute to AI over-reliance in educational and professional settings. AI reliance in education signifies an unhealthy dependence on AI tools—like ChatGPT or other automated systems—that can reduce student autonomy, hinder skill acquisition, and elevate the risk of academic stress [8. 14]. Although a measured or tactical application of AI has been proven to improve teaching effectiveness and student engagement, reliance suggests a behavior pattern in which students delegate crucial cognitive activities to technology rather than participating in reflective or creative thought [ 9 , 15 , 16 ]. This phenomenon is similar to issues with internet or social media usage, where immediate answers or emotional comfort replace sustained effort and perseverance [ 8 , 14 ]. Consistently depending on AI for writing tasks, research projects, or making decisions, students jeopardize their chances of developing critical thinking, creativity, and self-directed learning techniques [ 4 , 16 ]. Researchers emphasize that although AI can serve as a valuable complement to teaching strategies, instructors must stay alert to the equilibrium between embracing technology and preserving human-centered exploration [ 17 ]. The psychological implications of reliance on AI are complex, involving personality characteristics, emotional control, and cognitive distortions [ 8 ]. [ 7 ] and [ 8 ] state that neuroticism, self-critical perfectionism, and impulsivity elevate the chances of excessive dependence on AI-driven tools. Neuroticism, marked by increased responses to stress, frequently leads individuals to pursue immediate technological solutions for anxiety or performance apprehensions [ 18 ]. In a similar way, self-critical perfectionists might be drawn to the flawless results that AI is thought to produce, seeking to prevent errors and the unease linked to learning through trial and error [ 19 ]. Instead of developing study habits, impulsive learners might resort to AI whenever they face challenges, perpetuating a pattern of dependence on external sources. This AI dependency diminishes creativity, independent thought [ 8 ]. Scholars place AI reliance within existing frameworks to comprehend the intersection of personality, emotions, and technology. The I-PACE model highlights that individual traits—like perfectionism or impulsivity—interact with emotional states and cognitive functions, affecting maladaptive technology use [ 8 , 12 ]. This model describes how some individuals react to stress or academic pressure by consistently looking for digital shortcuts. At the same time, Basic Psychological Needs (BPN) theory suggests that autonomy, competence, and relatedness are crucial for well-being and motivation [ 20 ]. If AI reliably addresses unmet needs—like providing quick responses that protect learners from the unease of ambiguity—an excessive dependence might develop, reducing the intrinsic motivation essential for authentic learning and developmental progress [ 8 , 13 ]. Simultaneously, determining which aspects of AI design—such as user interface, adaptive feedback, or customization features—enhance or reduce dependency could assist developers in creating more ethically responsible platforms [ 21 ]. Adverse academic emotions—like anxiety, frustration, or despair—can greatly heighten reliance on AI, hindering students’ motivation and their capacity to manage their own learning [ 22 ]. As unaddressed psychological needs build up, students might seek emotional support or affirmation from AI [ 8 , 23 ]. Performance expectations further amplify this dependence: when students think AI significantly enhances their grades or educational results, they might overrate the technology’s necessity [ 24 ]. This corresponds with more extensive research on performance expectancy, an element demonstrated to be essential in the adoption and ongoing utilization of new technologies [ 25 ]. Eventually, this persistent trend of pursuing immediate solutions or emotional comforts causes learners to neglect crucial cognitive activities like integrating information or contemplating mistakes [ 8 ]. Methodology This study employed a qualitative research approach to explore the factors influencing AI dependency and the consequences of AI dependency. The approach included semi-structured interviews and focus group sessions. Each of these methods was chosen to gather rich insights into faculty members’ experiences, perspectives and how using AI tools in their practices and how affect them [ 26 ], Semi-Structured Interviews Semi-structured interviews were utilized in this study to explore the factors influencing AI dependency and its consequences [ 27 ]. A total of twenty-two participants were interviewed using a purposive sampling approach to ensure diversity and representation. The researchers developed interview protocol based on the theoretical framework of the study (Appendix A). Participants were chosen to reflect a range of experiences with Generative AI tools, from those who were new to AI to those with substantial expertise, allowing for a comprehensive understanding of different approaches to integrating AI in teaching and scientific research in higher education. Furthermore, the study emphasized disciplinary diversity, involving faculty members from various academic fields to explore the motivations behind AI dependency, its consequences, and strategies for balancing its use. To ensure a broad and perspective, participants were drawn from multiple universities, capturing a variety of institutional policies and resources related to AI in education. This careful selection process ensured that the study provided a rich and detailed account of faculty members' experiences with AI, particularly regarding how they incorporate AI considerations into their teaching. All interviews were recorded and each one was 25–35 minutes. Focus Group Sessions Focus groups serve as a valuable qualitative research method, particularly effective for examining how group interactions and social dynamics influence participants’ perspectives and experiences [ 28 – 30 ]. Their interactive format encourages open discussions, allowing participants to share experiences, build on each other’s ideas, and engage in collective reflection. This setting facilitates the exchange of diverse viewpoints, the co-construction of knowledge, and an in-depth exploration of complex issues. In this study, focus groups provided an opportunity to examine how faculty members integrate Generative AI (Gen AI) into their teaching and research practices. Unlike individual interviews, focus groups generated rich data as participants not only responded to the facilitator’s prompts but also engaged with and expanded upon their peers’ insights, fostering a dynamic and reflective conversation [ 31 ]. Four focus group sessions were held, each consisting of eight participants, selected through a purposive sampling approach while ensuring that individuals who took part in the semi-structured interviews were excluded. This brought the total number of focus group participants to 24. Participants for the focus group sessions were selected using a purposive sampling approach, ensuring a diverse range of experiences and perspectives on AI dependency in higher education. Participants were required to be faculty members actively engaged in teaching, research, or administrative roles at higher education institutions. To ensure a balanced discussion, participants were chosen based on their experience with Generative AI (Gen AI) tools, ranging from minimal exposure to extensive integration in their professional activities. Furthermore, disciplinary diversity was considered, incorporating faculty from various academic fields to explore AI use across different domains. To maintain fresh perspectives and avoid redundancy, only individuals who had not participated in the semi-structured interviews were included in the focus groups. Moreover, participants were selected from many universities, allowing for a broader understanding of how institutional policies, technological resources, and AI adoption strategies influence dependency. Finally, participants were required to demonstrate willingness to share their experiences, engage in discussions, and reflect on their AI usage, ensuring dynamic and insightful interactions. The researchers used generated prompts from the findings of the semi-structured interviews data analysis (Appendix B). Therefore, the total number of participants across the semi-structured interviews and the four focus group sessions was 46 participants. Table 1 below provides demographic information about the participants. Table 1 Demographic information about the participants in the semi-structured interviews and focus group sessions. Variable Frequency % Gender Male 20 43.5 Female 26 56.5 Age (Years) 25–35 5 10.9 36–45 15 32.6 46–55 18 39.1 56+ 8 17.4 Frequency of Gen AI Use Daily 21 45.7 Weekly 14 30.4 Monthly 7 15.2 Occasionally 4 8.7 Discipline Medical Sciences 8 17.4 Humanities and Educational Sciences 8 17.4 Engineering Sciences 9 19.6 Social Sciences 7 15.2 Natural Sciences (Physics, Math, etc.) 6 13 Business and Communication 8 17.4 Data Collection Individual semi-structured interviews were conducted with 22 participants, with each session lasting between 25 and 35 minutes. To ensure accuracy, all interviews were audio-recorded. The interviews began with participants being asked to share their experiences with Generative AI (Gen AI) technology, focusing on how frequently they use AI in their academic work and the extent to which they rely on it for teaching, research, and administrative tasks. This approach allowed participants to talk about their experiences in integrating AI into their professional practices and adapting to the increasing presence of AI in higher education. Follow-up questions delved deeper into their experiences, particularly regarding their patterns of AI use, the tasks they delegate to AI tools, and their level of dependence on AI-generated outputs. Participants were also encouraged to reflect on their evolving relationship with AI, discussing how their reliance on these technologies has changed over time. They described specific scenarios in which they felt AI enhanced their efficiency or decision-making and instances where they recognized a growing dependency on AI-driven solutions. The goal of the interviews was to gather comprehensive insights into participants’ AI usage patterns, their motivations for using AI, and the challenges they face in maintaining a balance between employing AI and retaining their own professional agency. Four focus group sessions, each lasting approximately one hour, were conducted to further explore faculty members' perspectives on their use of Generative AI (Gen AI) and the extent to which they rely on AI tools in their academic work. Two sessions were held in person, while the other two took place via a video conferencing platform. The discussions were facilitated by two researchers who guided the conversations using prompts derived from the interview data (Appendix B). The focus groups aimed to provide a deeper understanding of how frequently faculty members use AI, the specific tasks for which they rely on AI, and their perceptions of AI dependency in their teaching, research, and administrative responsibilities. Participants reflected on their patterns of AI usage, their motivations for integrating AI into their workflows, and the challenges of maintaining a balance between AI assistance and professional autonomy. With participants' consent, all sessions were audio-recorded for accurate documentation. Data from these discussions offered fresh insights into faculty members' evolving relationships with AI, broadening the perspective on how AI influences decision-making, problem-solving, and academic practices. These insights enriched and complemented the semi-structured interview findings, contributing to a more comprehensive understanding of the opportunities and risks associated with frequent AI use in higher education. Data Analysis An inductive thematic analysis was conducted to analyze data from both semi-structured interviews and focus group discussions, following the six-step framework outlined by Braun and Clarke (2006). The dataset included 12.25 hours of interview recordings and 3.25 hours of focus group discussions, which were transcribed and validated by participants for accuracy. Using NVivo software, the researchers systematically coded the data, identifying subthemes that were further organized into overarching themes, informed by relevant literature. This approach ensured that themes emerged organically while remaining aligned with the study’s objectives. To enhance methodological rigor and credibility, a triangulation process was employed. Individual interview data were coded to gain detailed insights into participants’ experiences, then cross-referenced with focus group data to confirm or refine emerging themes. Artifacts related to AI usage were analyzed as a third data source, offering contextual validation. By comparing findings across multiple sources, the study ensured that identified themes were robust and reflective of diverse perspectives on AI dependency. Participant responses were categorized using standardized quantitative terms. 'Most' referred to responses from over 75% of participants, 'many' represented 50–75%, and 'some' indicated less than 50%. When participants expressed multiple viewpoints, responses were coded under all relevant themes to capture the complexity of AI integration in their academic work. This structured approach provided a clear and transparent representation of the key findings, ensuring that the analysis accurately reflected faculty members' experiences with AI in higher education. Trustworthiness To ensure the trustworthiness of this study, the researchers emphasized confirmability, credibility, dependability, and transferability throughout the research process. Credibility was reinforced by employing data collection methods, including semi-structured interviews and focus groups, allowing for triangulation to validate the findings. Confirmability was ensured through a systematic documentation of the research process and data analysis, maintaining transparency and minimizing researcher bias. To enhance accuracy, transcribed interviews were shared with participants for member checking, allowing them to review and refine their statements. The interview protocol was developed based on the study’s research questions, a pilot interview, and expert feedback from scholars in AI and educational technology. To maintain dependability, the researchers adopted a code-recode strategy, independently coding the data three times and comparing results for consistency. Since the interviews and focus group discussions were conducted in Arabic, a backward translation process was employed to ensure linguistic and contextual accuracy, with an interrater reliability score of 89%, reflecting strong agreement among coders. For transferability, purposive sampling was used to select participants from various academic disciplines and institutions, ensuring that findings remain relevant and applicable to similar higher education contexts. These methodological steps strengthened the credibility and reliability of the study’s findings, providing a robust foundation for understanding AI dependency among educators. Ethical Considerations IRB approval was obtained from the Institution Review Board (IRB) at AN Najah National University, under approval number Edu. Dec. 2024/18. Informed consent to participate in this study was obtained from all of the participants in the study. At the start of each session, participants were given a clear explanation of the study’s purpose, with an emphasis on the voluntary nature of participation and assurances that all responses would remain confidential and anonymous. They were informed that proceeding with the session would indicate their consent, and they were reminded of their right to withdraw at any time without consequences. These measures ensured ethical compliance and upheld participant autonomy throughout the research process. Results Research question #1: What are the key factors contributing to AI dependency among educators in higher education? The first research question aimed to explore the factors influencing AI dependency among educators with experience using Generative AI (Gen AI) tools. The analysis revealed that AI reliance is shaped by institutional, psychological, cognitive, technological, and individual factors. Table (2) presents the coding book for the research first question. Tabl 2. Coding book for the reasons for AI dependancy research question Theme Subtheme Example Quotation Institutional Factors Heavy Workload and Time Constraints AI helps me save time, especially for grading and administrative tasks. Without it, I would be overwhelmed. Institutional expectation of using AI "Our university expects us to use AI, but there’s little guidance on how to do it effectively." Lack of Institutional Guidance There are no clear guidelines on AI use in our institution, so I end up relying on it more than I probably should. Psychological Factors Anxiety and Performance Pressure I worry that if I don’t use AI, my teaching methods will become outdated compared to my colleagues. Perfectionism and Fear of Errors I use AI for generating content because I want everything to be perfect before presenting it to students. Cognitive Factors Cognitive Offloading Sometimes, I let AI generate responses instead of thinking through problems myself—it’s just quicker and easier. Loss of Pedagogical Creativity AI-generated lesson plans make it easy, but I feel like I’m losing my creative touch in designing courses. Technological Factors Ease of Access and Automation The convenience of AI makes it tempting to use for everything—I don’t even think about whether I should do it myself anymore. Lack of AI Literacy I don’t always understand how AI works, so I trust it more than I probably should. Feeling Powerful and Capable AI makes me feel more capable and in control of my workload. Individual Factors Academic Self-Efficacy I feel more confident in my academic abilities when AI helps me complete tasks efficiently. Individual Factors Academic Reputation Using AI-generated research summaries enhances my credibility and saves me time. High Performance Expectations There’s a lot of pressure to produce high-quality work, so I use AI to meet expectations. Low Academic Confidence I lack confidence in my academic writing, so I rely on AI to refine my work. Lack of Scientific Research Engagement I rarely engage in deep scientific research anymore because AI provides quick summaries and insights. Institutional Factors Heavy Workload and Time Constraints Many participants reported that AI significantly reduces their workload, allowing them to focus on other academic responsibilities. They highlighted how AI assists with grading, content generation, and administrative tasks, particularly in time-sensitive situations. Some educators pointed out that institutional demands for research productivity and teaching effectiveness push them to rely on AI to manage multiple responsibilities. "Without AI, I would spend hours grading assignments and drafting course materials. Now, I can do it in minutes, which helps me keep up with my workload."(E5) However, a few participants expressed concerns that using AI could increase there worload in the filed of social sciences. A fellow up questions was aslked, there response was because they were not familiar with this new tool. "It’s a double-edged sword—AI helps me finish tasks faster, but I sometimes feel need more time to understand which increase the time that I need to finish writing the tasks “ (E19). Lack of Institutional Guidance Most participants mentioned that their institutions have not provided clear policies or training on AI use, leaving them uncertain about the extent to which they should rely on it. Some educators stated that this lack of guidance makes them more dependent on AI, as they have no structured support to develop balanced AI integration strategies. "There are no clear policies on how much AI use is appropriate, so I just use it however I think best." (E8). On the other hand, a few participants reported that their institutions may eventually regulate AI use more strictly, limiting its benefits. "I depend on AI now, but if my university decides to impose restrictions, I might struggle to adjust back to traditional methods." (E5) Psychological Factors Anxiety and Performance Pressure Several participants pointed out that AI use helps them manage anxiety related to teaching and research expectations. Some expressed that they feel pressure to stay updated with AI trends, fearing that not integrating AI into their work could make them appear outdated compared to their colleagues. "Everyone around me is using AI, and I don’t want to be left behind. I feel like I need to use it to stay relevant." (E2). Nevertheless, a few participants mentioned that AI reliance sometimes increases stress, particularly when they feel uncertain about its accuracy. "I depend on AI, but at times, I worry that I might be using incorrect or biased information without realizing it." (E9). Perfectionism and Fear of Errors Some participants described themselves as perfectionists, stating that AI helps them ensure accuracy in research, lesson planning, and assessment design. They mentioned that AI allows them to refine content and eliminate errors, making them more confident in their work. "I use AI to check everything. I don’t want to submit anything that isn’t polished and error-free." But, a few educators expressed concerns that this dependency could prevent them from developing confidence in their own academic skills. "I feel like I’ve lost my ability to draft a paper from scratch. I always turn to AI first." (E 6) Cognitive Factors Cognitive Offloading Most participants acknowledged that AI has become their default tool for information retrieval and content creation, reducing the effort required for critical thinking and problem-solving. "I used to brainstorm lesson plans myself. Now, I ask AI, and it gives me something within seconds." (E15) However, some educators expressed concern that relying on AI too frequently might weaken their cognitive engagement with their work. "I wonder if I’m losing my ability to think critically because AI does the thinking for me." (E 10). Loss of Pedagogical Creativity A few participants pointed out that AI-generated content makes teaching more convenient but limits their creativity in designing course materials. "I used to create my own case studies and interactive activities. Now, I just modify what AI generates." (E17) On the other hand, some educators mentioned that AI enhances their creativity by providing new perspectives and ideas they wouldn’t have considered otherwise. "AI gives me different angles to approach a topic, which actually improves my lesson planning." (E22) Technological Factors Ease of Access and Automation Many participants expressed that AI tools are readily available and easy to use, making them an attractive option for daily academic tasks. They mentioned that AI helps them save time, generate ideas, and organize content efficiently. "Why spend hours writing something when AI can give me a great draft in seconds?" (E 21) However, a few educators pointed out that the ease of access makes it tempting to rely on AI for everything, leading to unintentional dependency. "The more I use AI, the harder it becomes to work without it. It’s like having a calculator for everything." (E 20) Lack of AI Literacy Some participants admitted that they lack a deep understanding of AI’s inner workings, leading them to trust its outputs without fully questioning them. "I know AI isn’t perfect, but I don’t always know how to verify the accuracy of what it generates." (E 18) A few educators expressed concerns that this lack of AI literacy might make them overly dependent on AI-generated insights. "I sometimes use AI without thinking critically because I assume it knows better than I do." (E1) Feeling Powerful and Capable Some educators pointed out that AI gives them a sense of empowerment, allowing them to produce high-quality content more efficiently. "With AI, I can complete tasks that would have taken me hours in just a few minutes. It makes me feel more capable." (E3) Moreover, a few participants expressed concerns that this sense of control is misleading, as they might be overestimating AI’s reliability. "AI makes me feel smarter, but I sometimes wonder if I’m just relying on it too much without questioning its accuracy." (E 3). Individual Factors Based on the coding book (Table 2) in the supplementary files, individual factors include academic reputation, high performance expectation, low academic self-efficacy, and lack of scientific research engagement. Academic reputation The majority of participants reported that their academic reputation is a crucial factor influencing their reliance on AI, particularly in scientific research. One faculty member noted that he is a well-known researcher who frequently publishes scientific work. However, due to his busy schedule with teaching and other responsibilities, he uses AI to assist in writing. Academic Self-Efficacy Some participants mentioned that using AI enhances their confidence in their academic abilities. "AI helps me refine my ideas, making my work more professional and well-structured." (E6) Other participants expressed concern that relying on AI might affect their academic reputation, as AI-generated content could be perceived as lacking originality. "I worry that using AI too much might make others question the authenticity of my work." (E7) Low Academic Confidence A few participants admitted that AI serves as a tool for procrastination, allowing them to delay tasks while still producing quick results when needed. "I put off writing papers because I know AI can help me generate content at the last minute." On the other hand, some educators expressed that AI use compensates for their low academic confidence, making them feel more capable in completing their work. "I don’t always trust my writing skills, so AI gives me the reassurance I need to finalize my work." Motivational Decline For the purpose of this study, the researchers consider motivation decline referes to the gradual reduction in an individual's drive, enthusiasm, or willingness to engage in various academic activities. Many subthemes reported by the participants fail within motivation decline category including procrastination, increased laziness, erode intrinsic motivation, reduce individual initiatives, and deeply depends on AI in simple tasks. Several participants mentioned that AI has encouraged procrastination, as they know they can complete tasks quickly with AI assistance. "Before AI, I would start preparing my lectures well in advance. Now, I just rely on AI to generate materials the night before." (EFG28). However, a few educators pointed out that while AI helps them meet deadlines, it sometimes leads to rushed work that lacks depth. "I meet my deadlines, but sometimes I feel like my work isn’t as thoughtful as it used to be because I rely on AI to speed things up." (E15) A few educators mentioned that AI has made them more efficient but acknowledged that it can be tempting to take shortcuts. "I’m more productive with AI, but I also recognize that I use it to avoid thinking through certain problems myself." (EFG13) Pedagogical Erosion In this study, the researchers defined pedagogical erosion as the gradual decline in the effectiveness, relevance, and innovation of teaching practices over time. Therefore, the researchers found many subthemes categorized under this theme including perceived professional inadequacy, erosion pedagogy autonomy, reduce active engagement with students, weakened academic mentorship, and reduce self-confidence. Some participants pointed out that AI helps them design better materials by providing fresh perspectives and structuring information effectively. "AI gives me a strong starting point for my lessons. I still personalize them, but it definitely saves me time." Few participants mentioned that their interactions with students have become less personal since integrating AI into their workflow. "I used to spend more time giving individualized feedback. Now, I use AI-generated comments, and I feel like I’m not engaging with my students as much."(E11) On the other hand, some educators argued that AI allows them to focus on more meaningful discussions rather than repetitive grading tasks. "AI handles the routine work, so I can dedicate more time to having deeper discussions with my students." (E20) Many participants expressed concerns that AI has made them doubt their own expertise, as they often feel AI-generated content is more structured or insightful than what they can produce on their own. "Sometimes I feel like AI writes better than I do. It’s unsettling to think that I might not be as good as I used to be."(EFG35) However, a few educators pointed out that AI boosts their confidence by helping them refine their work. "AI isn’t replacing my skills—it’s just giving me an extra layer of support to make my work better." (EFG14) Some participants mentioned that they feel less capable without AI, particularly when completing research or complex writing tasks. "If AI were suddenly unavailable, I don’t know how I’d manage my workload. I’ve started to rely on it too much." On the other hand, a few educators expressed that AI dependency is a matter of perspective and that it can be used wisely without losing professional competence. "AI is a tool, not a replacement. As long as we use it strategically, we can avoid becoming too dependent." (E16) Ethical and Integrity Risks In this study, the researchers defined ethical and integrity risks as the challenges that arise when AI influences academic practices. Several subthemes emerged under this category, including increased plagiarism rate, increased copyright infringement, academic misconduct, undermining human responsibility, and increased misleading information. Some participants expressed concerns that AI facilitates plagiarism and reduces students’ accountability for their work. " It’s so easy for students to copy-paste AI-generated responses without truly engaging with the material." (E7) Others pointed out that AI tools blur the lines of originality and authorship, making it difficult to differentiate between human and machine-generated work. "I worry that students are submitting AI-generated essays without understanding the concepts themselves." (E12) A few educators highlighted that AI-generated content sometimes lacks accountability, as it can produce misleading or biased information. " AI sometimes generates convincing but factually incorrect statements, and students don’t always verify them." (EFG21) On the other hand, some faculty members believe AI can be leveraged ethically if students are taught responsible AI use. "We should integrate AI literacy into our curriculum so students learn how to use these tools without compromising integrity." (E26) Social Fragmentation The researchers identified social fragmentation as the weakening of human connections and interactions due to AI integration in education. Subthemes under this category include negatively impacting students’ emotional state, diminishing social development, reducing human interaction, and decreasing collaboration among humans. Many participants noted that students are becoming more isolated as AI tools replace traditional peer interactions in collaborative assignments. "Group discussions are not as engaging anymore because students rely on AI instead of exchanging ideas with their peers." (E9) However, some faculty members suggested that AI can be used to enhance social learning if implemented thoughtfully. "If we integrate AI strategically—such as using it to facilitate discussions rather than replace them—it can actually strengthen collaboration." (E30) Creativity Suppression Creativity suppression refers to the risk that AI may homogenize knowledge production, restrict creativity, and limit students’ ability to seek diverse information. Several subthemes emerged, including homogenization of knowledge production, restricted creativity, and restricted information seeking. Many participants reported that AI-generated content tends to produce generic, standardized responses, leading to a decline in original thought. "Students’ assignments are starting to look the same because they rely on AI-generated structures." (EFG15) Some educators emphasized that AI discourages deep research, as students may settle for AI-generated summaries instead of exploring diverse sources. "Before AI, students would read multiple papers. Now, they just use AI to summarize, and I feel like they’re missing out on critical engagement." (E14) Table 3 Consequences of AI dependency Main Theme Subtheme Skills Atrophy Loss of critical thinking Weakening problem-solving abilities Diminishing research skills Diminishing writing skills Reduced analytical skills Decreased thinking capacity Loss of decision-making abilities Reduce ability to synthesize independently Weakened judgment Motivational Decline Over-reliance on AI for simple tasks Procrastination Reduced motivation Increased laziness Erode intrinsic motivation Reduce individual initiatives Pedagogical Erosion Erosion of pedagogical autonomy Reduced active engagement with students Weakened academic mentorship Reduce self-confidence Perceived professional inadequacy Ethical and Integrity risks Increased plagiarism rate Increased copyright infringement Academic misconduct Undermine human responsibility Increased misleading information Social Fragmentation Negatively impact students' emotional state Diminish social development Reduce human interaction Decreased collaboration among humans Creativity Suppression Homogenization of knowledge production Restricted creativity Restricted information seeking Establishing clear boundaries for AI use Selective Use of AI for Instructional Tasks Most participants reported that they use AI selectively, ensuring that it serves as a supporting tool rather than a replacement for their instructional role. They emphasized that while AI can streamline certain tasks, fundamental teaching activities such as mentoring, fostering discussions, and assessing student progress should remain human-led. "AI helps with structuring lesson plans and summarizing content, but I make sure that my role as an educator remains central in guiding discussions and engaging with students." (E4) Some participants pointed out that AI is particularly useful for administrative tasks and content organization but should not dictate teaching methods. "I let AI handle routine tasks like scheduling and summarizing, but when it comes to interactive teaching, I rely on my own expertise and instincts." (EFG33) However, a few participants expressed concerns that without defined limits, educators might unconsciously begin to depend too much on AI-generated content. "I initially used AI just for support, but over time, I found myself relying on it more than I intended. Now, I consciously limit my AI use to avoid dependency." (EFG28) Encouraging critical engagement with AI Verifying AI-Generated Content Many participants mentioned that they actively verify and refine AI-generated outputs before integrating them into their teaching materials. They expressed that AI can sometimes produce misleading, biased, or overly simplified information, requiring educators to act as content curators rather than passive users. "I never use AI-generated content without reviewing it first. It can be a great starting point, but I always fact-check and refine the materials to align with my course objectives." (EFG31) Some educators pointed out that they encourage students to critically analyze AI-generated responses rather than accepting them at face value. "I tell my students that AI is a tool, not an authority. They need to critique what it produces, question its assumptions, and think beyond the outputs." (EFG32) On the other hand, a few participants expressed concern that not all educators take the time to evaluate AI content carefully, which could lead to inaccuracies in teaching. "One of my worries is that some educators might blindly trust AI-generated materials, which could introduce errors or outdated information into their lessons." (EFG17) Balancing AI with human-centered teaching Hybrid intelligence The majority of participants emphasized the significance of hybrid intelligence in mitigating excessive reliance on AI in academic work and scientific research. When asked to elaborate on the concept of hybrid intelligence, participants defined it as the integration of human intelligence with machine intelligence, ensuring that AI-generated outputs are critically assessed before being adopted. This approach encourages users to engage in deeper reflection and independent analysis rather than passively accepting AI-generated content. Prioritizing interactive and discussion-based learning Most participants expressed that they intentionally design their courses to emphasize live discussions, debates, and student-driven learning to counterbalance AI-generated content. They believe that human interaction remains essential for fostering deep understanding and critical thinking. "AI is great for providing structured information, but meaningful learning happens when students engage in discussions, challenge ideas, and interact with their peers and instructors." (E15) Some participants mentioned that they use AI to supplement discussions by generating diverse perspectives, but they ensure that students analyze and debate those perspectives rather than passively accepting them. "I sometimes use AI to provide multiple viewpoints on a topic, but I make sure my students critically evaluate and compare them rather than just absorbing the information." (EFG25) However, a few participants pointed out that some educators struggle to balance AI integration with traditional teaching, leading to a more passive learning environment. "I've seen cases where AI-generated lectures replace interactive teaching, and I worry that students might become passive learners rather than active thinkers." (EFG32) Maintaining pedagogical creativity Using AI as a Spark for Innovation, Not a Substitute for Creativity Many participants reported that they leverage AI to generate new ideas for lesson planning but ensure that their personal creativity remains the driving force. They view AI as a brainstorming partner rather than a content creator. "I use AI to generate multiple lesson ideas, but I always customize them to fit my teaching style and my students' needs." (E3) Some educators pointed out that AI helps them explore innovative approaches to teaching but emphasized that human creativity is irreplaceable. "AI can suggest engaging activities, but it’s my job to refine them and add the human element that makes learning meaningful." (E22) On the other hand, a few participants expressed concerns that excessive AI use might lead to a decline in originality if educators become overly reliant on AI-generated content. "I fear that if we depend too much on AI, we might lose the uniqueness of our teaching styles. That’s why I try to balance AI use with my own creative input." (EFG26) Fostering student AI literacy and ethical use Teaching Students to Use AI Responsibly Some participants expressed that they incorporate AI literacy into their courses to help students use AI effectively while avoiding over-reliance. They believe that educators should guide students in understanding AI’s strengths and limitations. "I teach my students how to use AI as a research tool but also emphasize that they must engage critically with the results rather than just accepting AI-generated answers." (EFG32) Most participants reported that they actively discourage students from using AI for academic shortcuts, instead encouraging them to use AI as a means of enhancing learning. "AI can help students brainstorm ideas, but they need to put in the effort to analyze and expand on those ideas instead of just submitting AI-generated content." (EFG34) However, a few participants pointed out that some students misuse AI for assignments, and educators must establish clear guidelines on ethical AI use. "We need to teach students that AI is a tool to assist learning, not a way to bypass intellectual effort." (EFG11) Continuous professional development and peer collaboration Engaging in AI training and professional learning communities Several participants mentioned that they actively seek professional development opportunities to improve their AI literacy and refine their AI integration strategies. They believe that educators must continuously learn to ensure that AI is used responsibly and effectively. "I regularly attend AI workshops to stay updated on best practices. The more I understand AI, the better I can integrate it into my teaching without becoming dependent on it." (E5) Some educators pointed out that they collaborate with colleagues to share AI integration strategies, discuss ethical concerns, and develop guidelines for responsible AI use. "We have faculty discussions on AI use where we exchange ideas on how to integrate AI without compromising teaching quality. These discussions help us find the right balance." (EFG36) On the other hand, a few participants expressed that some institutions lack adequate AI training programs, leaving educators to navigate AI integration on their own. "I wish there were more structured AI training for educators. Right now, we mostly figure it out through trial and error." (EFG35) Discussion This study highlights the factors behind AI dependency, the consequences of AI dependency in higher education, and the strategies to balance the use of AI in teaching and scientific research. Therefore, the findings of provide a crucial understanding of AI dependency among educators in higher education, highlighting institutional, psychological, cognitive, technological, and individual factors contributing AI dependency. Furthermore, the consequences of AI reliance were explored, emphasizing skills atrophy, pedagogical erosion, motivational decline, ethical risks, social fragmentation, and creativity suppression. These findings reflect and extend the existing literature on AI integration in education. Consistent with prior research [ 10 ] this study found that institutional workload pressures drive educators to rely on AI to manage administrative and instructional responsibilities. Similar to the findings of [ 4 ], educators in this study reported that AI tools significantly alleviate grading and content creation burdens. However, as [ 7 ] pointed out, the lack of institutional guidelines on AI use fosters uncertainty, leading some educators to overuse AI in ways that could compromise their pedagogical agency. Psychological factors, particularly anxiety and perfectionism, emerged as critical drivers of AI reliance. These results corroborate prior findings by [ 9 ], who identified impulsivity and perfectionism as predictors of problematic AI use. Educators in this study expressed concerns about staying current with AI advancements, mirroring similar trends found in student-related studies where fear of falling behind led to increased technology dependence [ 8 ]. Moreover, self-critical perfectionists reported using AI to ensure accuracy, reflecting the findings of [ 32 ] and [ 8 ] who highlighted the link between perfectionism and digital tool dependency. Cognitive offloading emerged as a prevalent theme, with educators admitting that AI-generated content reduced their engagement in deep critical thinking and pedagogical creativity. These findings resonate with those of [ 4 ], who warned that excessive AI reliance may erode analytical skills. The concern that AI use weakens decision-making aligns with research by [ 33 ] who found that habitual reliance on digital tools can limit autonomous cognitive processing. In terms of technological factors, ease of access and automation were key contributors to AI dependency. Participants noted that AI streamlines academic tasks but simultaneously fosters overreliance, similar to the patterns observed in problematic internet use [ 25 ]. Additionally, the study found that limited AI literacy contributes to blind trust in AI outputs, echoing the findings of [ 17 ], who emphasized the need for critical evaluation of AI-generated content. The results of this study reflect previous research highlighting the impact of academic reputation on technology adoption. [ 34 ] found that individuals with higher professional expectations tend to integrate technological tools to enhance their efficiency and maintain their academic standing. Similarly, [ 8 ] demonstrated that faculty members with strong publication records are more likely to use AI tools to optimize research processes, ensuring timely and high-quality outputs. Furthermore, [ 17 ] emphasized that performance pressure in academia drives educators to seek digital assistance, which, while beneficial, may also lead to overreliance on AI-generated content. Despite these advantages, the study also revealed concerns regarding AI’s potential impact on academic integrity and originality. Some participants worried that excessive dependence on AI might blur the line between authentic scholarly contributions and AI-assisted content. These concerns echo findings from [ 35 ] who warned that AI-generated texts may compromise academic originality if not critically evaluated and ethically used. Additionally, [ 36 ] highlighted that while AI can enhance efficiency, intrinsic motivation and intellectual engagement remain essential in academic work. Moreover, this study identified skills atrophy as a significant consequence of AI dependency, particularly in research, writing, and decision-making. The findings align with those of [ 8 ] who demonstrated that AI reliance reduces engagement in self-directed learning and problem-solving. Educators expressed concerns that frequent AI use undermines their ability to synthesize information independently, similar to what [ 35 ] observed in digital learning environments. Pedagogical erosion was another major consequence, with educators reporting diminished instructional autonomy and reduced student engagement. As [ 13 , 36 ] pointed out, academic emotions play a crucial role in sustaining motivation; when AI replaces human interaction in teaching, it may erode the intrinsic motivation of both educators and students. This aligns with [ 37 ] who argued that cognitive shortcuts often reduce long-term engagement in skill-building activities. This study also reinforced concerns about academic integrity, plagiarism, and misinformation in AI-generated content, similar to findings by [ 34 ] on technology adoption risks. Educators worried that AI facilitates academic dishonesty, supporting [ 35 ] argument that AI tools must be used with ethical safeguards. Social fragmentation was another notable theme, with educators highlighting reduced human interaction and student collaboration. These findings are consistent with [39] who found that technology-mediated learning environments often limit meaningful peer engagement. Finally, creativity suppression was reported as a risk, with educators noting that AI-generated content tends to standardize knowledge production. This aligns with research by [ 37 ] who emphasized the importance of intrinsic motivation in fostering innovation. When AI generates predefined structures, students and educators may lose opportunities to explore diverse ideas, ultimately limiting creative expression. The findings of this study provides several strategies to mitigate AI overreliance, including setting clear boundaries for AI use, verifying AI-generated content, fostering student AI literacy, and prioritizing interactive learning. These strategies align with [ 8 ] who recommended structured AI policies to ensure that AI serves as an educational enhancement rather than a substitute. One of the key strategies identified in this study to mitigate excessive reliance on AI is hybrid intelligence. Participants highlighted that hybrid intelligence—an approach combining human cognitive abilities with machine intelligence—ensures that AI-generated content is c The findings resonate with previous studies emphasizing the role of hybrid intelligence in optimizing AI use while preserving human creativity and decision-making. [ 4 ] suggested that while AI tools streamline tasks, human oversight remains critical in ensuring the quality and originality of academic work. Similarly, [ 33 ] emphasized that hybrid intelligence fosters a symbiotic relationship between AI and users, where AI assists in data processing while human judgment ensures critical reflection and ethical decision-making. Moreover, research by [ 35 ] highlighted that hybrid intelligence encourages metacognitive engagement, requiring individuals to analyze and evaluate AI outputs rather than passively accepting them. This aligns with [ 37 ] who argued that deep cognitive involvement is essential for sustained intellectual development and professional growth. In the academic context, hybrid intelligence supports the idea that AI should function as a collaborative tool rather than a replacement for human expertise [ 1 ]. Furthermore, the implementation of hybrid intelligence addresses concerns about skills atrophy, as identified in this study. By requiring educators to interact with AI outputs actively, this approach maintains critical thinking skills and pedagogical autonomy. This supports the findings of [ 8 ] who demonstrated that educators who critically engage with AI tools retain stronger analytical abilities compared to those who rely on AI passively. Moreover, continuous professional development was emphasized as a crucial strategy. As [ 1 ] noted, educators who engage in AI literacy training are better equipped to integrate AI responsibly while maintaining pedagogical integrity. Peer collaboration and faculty discussions were also suggested as essential components in developing institutional guidelines for responsible AI use. Limitation and future studies This study has several limitations that should be acknowledged. First, the study was conducted within a specific higher education context, which may limit the generalizability of the findings to different educational systems and cultural settings. Future research should explore AI dependency in diverse academic institutions across different regions to provide a broader understanding of the phenomenon. Second, the study primarily relied on self-reported data from faculty members, which may be subject to social desirability bias. Participants may have underreported their reliance on AI or overstated their critical engagement with AI tools. Future studies could incorporate observational methods or data analytics to assess actual AI usage patterns and their impact on pedagogical practices. Third, while this study focused on the perspectives of educators, it did not examine the viewpoints of students regarding AI dependency in learning. Given that AI is increasingly being integrated into student learning processes, future research should explore how AI reliance affects student engagement, cognitive development, and academic performance. Additionally, this study explored AI dependency at a general level but did not investigate the potential disciplinary differences in AI use. Certain academic disciplines may have unique AI integration challenges and opportunities. Future studies should examine how AI reliance varies across disciplines such as humanities, social sciences, engineering, and medical sciences. Future research should explore long-term consequences of AI dependency on faculty professional identity, academic creativity, and knowledge production. Longitudinal studies would be beneficial in assessing how AI reliance evolves over time and what institutional interventions might mitigate potential risks. Theoretical and Practical Implications The findings of this study contribute to theoretical discussions on technology adoption in higher education by highlighting AI dependency as a complex interplay of institutional, psychological, and cognitive factors. This study expands on existing theoretical frameworks such as the I-PACE model [ 12 ] by demonstrating how AI reliance among educators aligns with patterns of problematic technology use. From a practical perspective, these findings emphasize the need for clear institutional policies, professional development programs, and AI literacy initiatives that promote responsible AI integration. By fostering hybrid intelligence, educators can maintain their pedagogical autonomy while leveraging AI's benefits, ensuring a balanced and ethical approach to AI use in academia. Conclusion This study examines AI reliance among educators, analyzing the institutional, psychological, and cognitive factors that influence its integration into academic practices. While AI contributes to efficiency, knowledge management, and academic productivity, excessive dependence may present challenges related to pedagogical autonomy, critical thinking, and professional identity. The findings highlight the value of hybrid intelligence as a means of integrating AI responsibly, ensuring it complements rather than replaces human intellectual engagement. By contextualizing AI reliance within professional agency and academic integrity, this study builds on existing models of AI adoption. However, the findings should be interpreted within the study's scope, as variations in institutional policies, technological access, and disciplinary contexts may influence AI adoption differently. From a practical perspective, the results reinforce the need for institutional policies, faculty training, and AI literacy initiatives to support responsible AI use. Rather than advocating for or against AI in education, this study emphasizes the need for a balanced approach where AI serves as a tool to enhance human capabilities. Future research should further investigate AI’s long-term influence on faculty development, student learning, and knowledge production, particularly across diverse academic settings and evolving technological landscapes. Declarations Acknowledgments Not applicable Funding No funding was received for this study. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Helsinki declaration. In accordance with ethical guidelines, informed consent for participation was obtained from all the participants. This study was approved by the Scientific Research Ethics Committee of An Najah National University reference (Edu. Dec. 2024/18). Author Contribution: The author was responsible for all aspects of the research, including conceptualization, design, data collection, data analysis, and manuscript preparation. The author has read and approved the final version of the manuscript Competing interests The author declares no competing interests. 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Soc Pers Psychol Compass. 2007;1(1):115–28. https://doi.org/10.1111/j.1751-9004.2007.00015.x . Przybylski AK, Weinstein N, Murayama K, Lynch MF, Ryan RM. The ideal self at play: The appeal of video games that let you be all you can be. Psychol Sci. 2012;23(1):69–76. https://doi.org/10.1177/0956797611418676 . Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6127885","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442749795,"identity":"602618fe-17a4-471d-8a7b-906fda8539f1","order_by":0,"name":"Zuheir N Khlaif","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIie3OMQrCMBTG8VcEXZ64CtX2Cp9kVDxLpFCXFhwdPUB01lt0E7eUroqr4KK7m4tgQdPNLXUTzH8IGd4veUQu1y/G7GlCEJirbJjDW9QgZIgQ3xISk2qyHgnVQevHDNNtS1/vMxr1M20h3nIlcwWkOyUjf02xsJJGh6EZZZppKX2mYmIlTUPyEpjieImeTC874bZCwYDEScbmF20nXd6j6AGD7HSJh4xIbGwkVMngeisR4phEZ56P+ysb+dxREqH+eFXri+ddLpfrr3oDJqlDmwsR2isAAAAASUVORK5CYII=","orcid":"","institution":"An Najah National University","correspondingAuthor":true,"prefix":"","firstName":"Zuheir","middleName":"N","lastName":"Khlaif","suffix":""},{"id":442749796,"identity":"260d71e3-d44b-4dc1-a886-12e1c00e7726","order_by":1,"name":"Bilal Hamamra","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Bilal","middleName":"","lastName":"Hamamra","suffix":""},{"id":442749797,"identity":"c56a0433-690a-4266-933a-0410e48482b9","order_by":2,"name":"Elham T. 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Consequently, institutions across the globe are increasingly utilizing AI-powered tools, including generative models like ChatGPT, to customize the educational process, streamline administrative duties, and reduce faculty workload [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite the important benefits, stakeholders in the field of education have concerns regarding over-dependence on AI, simply defined as students\u0026rsquo; and instructors\u0026rsquo; uncritical acceptance of AI generated material [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In addition, overreliance on AIED hinders cognitive involvement, productive critical thinking and decision making and long-term skill growth [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile literature on the topic of AIED is abundant, it primarily focuses on the gains of integrating artificial intelligence in the educational setting [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Ethical, cognitive and professional challenges related to AIED, on the other hand, are far less explored [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, most of the available research on overreliance on AIED is investigated in relation to students exploring how excessive dependence on AI can negatively impact their ability to think critically, independently and creatively and to perform self-directed learning [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Other student-related studies have linked AI reliance to psychological factors such as perfectionism, impulsivity, and the need for immediate cognitive relief, drawing comparisons to internet and social media dependence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmpirical research investigating AI dependency among educators is still meager [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and most of the existing studies explore how AI enhances educators' productivity and decision-making [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Risks and challenges pertaining to AIED, on the other hand, are less explored. In addition, most research on AIED has been conducted in Western or Chinese contexts, with limited cross-cultural perspectives [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, the purpose of this study is threefold: to explore the underlying causes of educators\u0026rsquo; overreliance on AI; to investigate how this reliance affects teaching autonomy, decision-making, and long-term practices and pedagogical identity; and to highlight potential challenges associated with AI reliance while suggesting strategies to overcome them. Through this empirical research which focuses on Palestinian, this research will provide insights into various aspects related to AI integration in higher education. The findings will inform policy recommendations, faculty development programs, and strategies to promote responsible AI use, ensuring that AI serves as a complement rather than a substitute for educators' professional expertise and pedagogical agency.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch questions\u003c/b\u003e \u003c/p\u003e \u003cp\u003e1. What are the key factors contributing to AI dependency among educators in higher education?\u003c/p\u003e \u003cp\u003e2. How does AI dependency impact educators' teaching autonomy, decision-making, and professional identity?\u003c/p\u003e\u003cp\u003e3. What strategies can help educators balance AI use while maintaining pedagogical creativity and instructional effectiveness?\u003c/p\u003e \n\u003ch3\u003eTheoretical Foundation of the Study\u003c/h3\u003e\n\u003cp\u003eThe \u003cb\u003eI-PACE Model (Interaction of Person-Affect-Cognition-Execution)\u003c/b\u003e, developed by [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], provides a comprehensive framework for understanding problematic technology use, including AI dependency. It explains how \u003cb\u003eindividual characteristics, emotional responses, cognitive processes, and behavioral execution\u003c/b\u003e interact over time, shaping patterns of technology reliance. The model consists of four key components: \u003cb\u003ePerson-Level Predispositions (P)\u003c/b\u003e, which include personality traits and cognitive styles that influence susceptibility to AI dependence; \u003cb\u003eAffect (A)\u003c/b\u003e, which refers to emotional states such as stress, anxiety, or workload pressure that drive AI use; \u003cb\u003eCognition (C)\u003c/b\u003e, which examines decision-making processes and the extent to which AI alters critical thinking and pedagogical autonomy; and \u003cb\u003eExecution (E)\u003c/b\u003e, which explores the long-term behavioral consequences of AI reliance, such as cognitive deskilling or shifts in professional identity. The I-PACE model serves as a valuable theoretical foundation for understanding how AI dependency develops, the factors that reinforce it, and its impact on educators\u0026rsquo; autonomy and professional agency. By applying this model, the study analyzes the psychological implications of AI reliance, offering insights into how educators interact with AI tools and the broader implications for teaching and learning.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePrevious studies using I-PACE\u003c/h2\u003e \u003cp\u003eResearch utilizing this model has demonstrated how \u003cb\u003eindividual predispositions, emotional states, and cognitive processes\u003c/b\u003e interact to influence excessive reliance on digital tools. For instance, studies on \u003cb\u003einternet and social media addiction\u003c/b\u003e have found that individuals with high \u003cb\u003eneuroticism, impulsivity, or perfectionism\u003c/b\u003e are more likely to develop problematic usage patterns, often as a coping mechanism for stress [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, research on \u003cb\u003eAI reliance in education\u003c/b\u003e suggests that educators and students with \u003cb\u003elow self-efficacy in digital literacy\u003c/b\u003e or \u003cb\u003ehigh performance expectations\u003c/b\u003e tend to offload cognitive tasks to AI, reducing engagement in \u003cb\u003ecritical thinking and problem-solving\u003c/b\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have also shown that \u003cb\u003einstitutional pressures and workload demands\u003c/b\u003e can lead professionals, including educators, to \u003cb\u003edepend on AI for efficiency\u003c/b\u003e, sometimes at the cost of \u003cb\u003epedagogical autonomy and professional agency\u003c/b\u003e. By applying the \u003cb\u003eI-PACE model\u003c/b\u003e, these studies provide a structured framework for analyzing how technology use evolves from \u003cb\u003eoccasional assistance to habitual dependence\u003c/b\u003e, highlighting the \u003cb\u003epsychological, cognitive, and behavioral factors\u003c/b\u003e that contribute to AI over-reliance in educational and professional settings.\u003c/p\u003e \u003cp\u003eAI reliance in education signifies an unhealthy dependence on AI tools\u0026mdash;like ChatGPT or other automated systems\u0026mdash;that can reduce student autonomy, hinder skill acquisition, and elevate the risk of academic stress [8. 14]. Although a measured or tactical application of AI has been proven to improve teaching effectiveness and student engagement, reliance suggests a behavior pattern in which students delegate crucial cognitive activities to technology rather than participating in reflective or creative thought [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This phenomenon is similar to issues with internet or social media usage, where immediate answers or emotional comfort replace sustained effort and perseverance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Consistently depending on AI for writing tasks, research projects, or making decisions, students jeopardize their chances of developing critical thinking, creativity, and self-directed learning techniques [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Researchers emphasize that although AI can serve as a valuable complement to teaching strategies, instructors must stay alert to the equilibrium between embracing technology and preserving human-centered exploration [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe psychological implications of reliance on AI are complex, involving personality characteristics, emotional control, and cognitive distortions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] state that neuroticism, self-critical perfectionism, and impulsivity elevate the chances of excessive dependence on AI-driven tools. Neuroticism, marked by increased responses to stress, frequently leads individuals to pursue immediate technological solutions for anxiety or performance apprehensions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In a similar way, self-critical perfectionists might be drawn to the flawless results that AI is thought to produce, seeking to prevent errors and the unease linked to learning through trial and error [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Instead of developing study habits, impulsive learners might resort to AI whenever they face challenges, perpetuating a pattern of dependence on external sources. This AI dependency diminishes creativity, independent thought [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eScholars place AI reliance within existing frameworks to comprehend the intersection of personality, emotions, and technology. The I-PACE model highlights that individual traits\u0026mdash;like perfectionism or impulsivity\u0026mdash;interact with emotional states and cognitive functions, affecting maladaptive technology use [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This model describes how some individuals react to stress or academic pressure by consistently looking for digital shortcuts. At the same time, Basic Psychological Needs (BPN) theory suggests that autonomy, competence, and relatedness are crucial for well-being and motivation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. If AI reliably addresses unmet needs\u0026mdash;like providing quick responses that protect learners from the unease of ambiguity\u0026mdash;an excessive dependence might develop, reducing the intrinsic motivation essential for authentic learning and developmental progress [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Simultaneously, determining which aspects of AI design\u0026mdash;such as user interface, adaptive feedback, or customization features\u0026mdash;enhance or reduce dependency could assist developers in creating more ethically responsible platforms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdverse academic emotions\u0026mdash;like anxiety, frustration, or despair\u0026mdash;can greatly heighten reliance on AI, hindering students\u0026rsquo; motivation and their capacity to manage their own learning [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. As unaddressed psychological needs build up, students might seek emotional support or affirmation from AI [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Performance expectations further amplify this dependence: when students think AI significantly enhances their grades or educational results, they might overrate the technology\u0026rsquo;s necessity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This corresponds with more extensive research on performance expectancy, an element demonstrated to be essential in the adoption and ongoing utilization of new technologies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Eventually, this persistent trend of pursuing immediate solutions or emotional comforts causes learners to neglect crucial cognitive activities like integrating information or contemplating mistakes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employed a qualitative research approach to explore the factors influencing AI dependency and the consequences of AI dependency. The approach included semi-structured interviews and focus group sessions. Each of these methods was chosen to gather rich insights into faculty members\u0026rsquo; experiences, perspectives and how using AI tools in their practices and how affect them [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e],\u003c/p\u003e\n\u003ch3\u003eSemi-Structured Interviews\u003c/h3\u003e\n\u003cp\u003eSemi-structured interviews were utilized in this study to explore the factors influencing AI dependency and its consequences [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A total of twenty-two participants were interviewed using a purposive sampling approach to ensure diversity and representation. The researchers developed interview protocol based on the theoretical framework of the study (Appendix A). Participants were chosen to reflect a range of experiences with Generative AI tools, from those who were new to AI to those with substantial expertise, allowing for a comprehensive understanding of different approaches to integrating AI in teaching and scientific research in higher education. Furthermore, the study emphasized disciplinary diversity, involving faculty members from various academic fields to explore the motivations behind AI dependency, its consequences, and strategies for balancing its use. To ensure a broad and perspective, participants were drawn from multiple universities, capturing a variety of institutional policies and resources related to AI in education. This careful selection process ensured that the study provided a rich and detailed account of faculty members' experiences with AI, particularly regarding how they incorporate AI considerations into their teaching. All interviews were recorded and each one was 25\u0026ndash;35 minutes.\u003c/p\u003e\n\u003ch3\u003eFocus Group Sessions\u003c/h3\u003e\n\u003cp\u003eFocus groups serve as a valuable qualitative research method, particularly effective for examining how group interactions and social dynamics influence participants\u0026rsquo; perspectives and experiences [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Their interactive format encourages open discussions, allowing participants to share experiences, build on each other\u0026rsquo;s ideas, and engage in collective reflection. This setting facilitates the exchange of diverse viewpoints, the co-construction of knowledge, and an in-depth exploration of complex issues. In this study, focus groups provided an opportunity to examine how faculty members integrate Generative AI (Gen AI) into their teaching and research practices. Unlike individual interviews, focus groups generated rich data as participants not only responded to the facilitator\u0026rsquo;s prompts but also engaged with and expanded upon their peers\u0026rsquo; insights, fostering a dynamic and reflective conversation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Four focus group sessions were held, each consisting of eight participants, selected through a purposive sampling approach while ensuring that individuals who took part in the semi-structured interviews were excluded. This brought the total number of focus group participants to 24. Participants for the focus group sessions were selected using a purposive sampling approach, ensuring a diverse range of experiences and perspectives on AI dependency in higher education. Participants were required to be faculty members actively engaged in teaching, research, or administrative roles at higher education institutions. To ensure a balanced discussion, participants were chosen based on their experience with Generative AI (Gen AI) tools, ranging from minimal exposure to extensive integration in their professional activities. Furthermore, disciplinary diversity was considered, incorporating faculty from various academic fields to explore AI use across different domains. To maintain fresh perspectives and avoid redundancy, only individuals who had not participated in the semi-structured interviews were included in the focus groups. Moreover, participants were selected from many universities, allowing for a broader understanding of how institutional policies, technological resources, and AI adoption strategies influence dependency. Finally, participants were required to demonstrate willingness to share their experiences, engage in discussions, and reflect on their AI usage, ensuring dynamic and insightful interactions. The researchers used generated prompts from the findings of the semi-structured interviews data analysis (Appendix B). Therefore, the total number of participants across the semi-structured interviews and the four focus group sessions was 46 participants. Table\u0026nbsp;1 below provides demographic information about the participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic information about the participants in the semi-structured interviews and focus group sessions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eFrequency of Gen AI Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDaily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccasionally\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDiscipline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedical Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumanities and Educational Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngineering Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural Sciences (Physics, Math, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness and Communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.4\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\u003eData Collection\u003c/h3\u003e\n\u003cp\u003e Individual semi-structured interviews were conducted with 22 participants, with each session lasting between 25 and 35 minutes. To ensure accuracy, all interviews were audio-recorded.\u003c/p\u003e \u003cp\u003eThe interviews began with participants being asked to share their experiences with Generative AI (Gen AI) technology, focusing on how frequently they use AI in their academic work and the extent to which they rely on it for teaching, research, and administrative tasks. This approach allowed participants to talk about their experiences in integrating AI into their professional practices and adapting to the increasing presence of AI in higher education. Follow-up questions delved deeper into their experiences, particularly regarding their patterns of AI use, the tasks they delegate to AI tools, and their level of dependence on AI-generated outputs.\u003c/p\u003e \u003cp\u003eParticipants were also encouraged to reflect on their evolving relationship with AI, discussing how their reliance on these technologies has changed over time. They described specific scenarios in which they felt AI enhanced their efficiency or decision-making and instances where they recognized a growing dependency on AI-driven solutions. The goal of the interviews was to gather comprehensive insights into participants\u0026rsquo; AI usage patterns, their motivations for using AI, and the challenges they face in maintaining a balance between employing AI and retaining their own professional agency.\u003c/p\u003e \u003cp\u003eFour focus group sessions, each lasting approximately one hour, were conducted to further explore faculty members' perspectives on their use of Generative AI (Gen AI) and the extent to which they rely on AI tools in their academic work. Two sessions were held in person, while the other two took place via a video conferencing platform. The discussions were facilitated by two researchers who guided the conversations using prompts derived from the interview data (Appendix B).\u003c/p\u003e \u003cp\u003eThe focus groups aimed to provide a deeper understanding of how frequently faculty members use AI, the specific tasks for which they rely on AI, and their perceptions of AI dependency in their teaching, research, and administrative responsibilities. Participants reflected on their patterns of AI usage, their motivations for integrating AI into their workflows, and the challenges of maintaining a balance between AI assistance and professional autonomy.\u003c/p\u003e \u003cp\u003e With participants' consent, all sessions were audio-recorded for accurate documentation. Data from these discussions offered fresh insights into faculty members' evolving relationships with AI, broadening the perspective on how AI influences decision-making, problem-solving, and academic practices. These insights enriched and complemented the semi-structured interview findings, contributing to a more comprehensive understanding of the opportunities and risks associated with frequent AI use in higher education.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eAn inductive thematic analysis was conducted to analyze data from both semi-structured interviews and focus group discussions, following the six-step framework outlined by Braun and Clarke (2006). The dataset included 12.25 hours of interview recordings and 3.25 hours of focus group discussions, which were transcribed and validated by participants for accuracy. Using NVivo software, the researchers systematically coded the data, identifying subthemes that were further organized into overarching themes, informed by relevant literature. This approach ensured that themes emerged organically while remaining aligned with the study\u0026rsquo;s objectives.\u003c/p\u003e \u003cp\u003eTo enhance methodological rigor and credibility, a triangulation process was employed. Individual interview data were coded to gain detailed insights into participants\u0026rsquo; experiences, then cross-referenced with focus group data to confirm or refine emerging themes. Artifacts related to AI usage were analyzed as a third data source, offering contextual validation. By comparing findings across multiple sources, the study ensured that identified themes were robust and reflective of diverse perspectives on AI dependency.\u003c/p\u003e \u003cp\u003eParticipant responses were categorized using standardized quantitative terms. 'Most' referred to responses from over 75% of participants, 'many' represented 50\u0026ndash;75%, and 'some' indicated less than 50%. When participants expressed multiple viewpoints, responses were coded under all relevant themes to capture the complexity of AI integration in their academic work. This structured approach provided a clear and transparent representation of the key findings, ensuring that the analysis accurately reflected faculty members' experiences with AI in higher education.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTrustworthiness\u003c/h3\u003e\n\u003cp\u003eTo ensure the trustworthiness of this study, the researchers emphasized confirmability, credibility, dependability, and transferability throughout the research process. Credibility was reinforced by employing data collection methods, including semi-structured interviews and focus groups, allowing for triangulation to validate the findings. Confirmability was ensured through a systematic documentation of the research process and data analysis, maintaining transparency and minimizing researcher bias. To enhance accuracy, transcribed interviews were shared with participants for member checking, allowing them to review and refine their statements.\u003c/p\u003e \u003cp\u003eThe interview protocol was developed based on the study\u0026rsquo;s research questions, a pilot interview, and expert feedback from scholars in AI and educational technology. To maintain dependability, the researchers adopted a code-recode strategy, independently coding the data three times and comparing results for consistency. Since the interviews and focus group discussions were conducted in Arabic, a backward translation process was employed to ensure linguistic and contextual accuracy, with an interrater reliability score of 89%, reflecting strong agreement among coders.\u003c/p\u003e \u003cp\u003eFor transferability, purposive sampling was used to select participants from various academic disciplines and institutions, ensuring that findings remain relevant and applicable to similar higher education contexts. These methodological steps strengthened the credibility and reliability of the study\u0026rsquo;s findings, providing a robust foundation for understanding AI dependency among educators.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e IRB approval was obtained from the Institution Review Board (IRB) at AN Najah National University, under approval number Edu. Dec. 2024/18. Informed consent to participate in this study was obtained from all of the participants in the study. At the start of each session, participants were given a clear explanation of the study\u0026rsquo;s purpose, with an emphasis on the voluntary nature of participation and assurances that all responses would remain confidential and anonymous. They were informed that proceeding with the session would indicate their consent, and they were reminded of their right to withdraw at any time without consequences. These measures ensured ethical compliance and upheld participant autonomy throughout the research process.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eResearch question #1: What are the key factors contributing to AI dependency among educators in higher education?\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe first research question aimed to explore the factors influencing AI dependency among educators with experience using Generative AI (Gen AI) tools. The analysis revealed that AI reliance is shaped by institutional, psychological, cognitive, technological, and individual factors. Table\u0026nbsp;(2) presents the coding book for the research first question.\u003c/p\u003e \u003cp\u003eTabl 2. Coding book for the reasons for AI dependancy research question\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample Quotation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInstitutional Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy Workload and Time Constraints\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI helps me save time, especially for grading and administrative tasks. Without it, I would be overwhelmed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitutional expectation of using AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\"Our university expects us to use AI, but there\u0026rsquo;s little guidance on how to do it effectively.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLack of Institutional Guidance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThere are no clear guidelines on AI use in our institution, so I end up relying on it more than I probably should.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePsychological Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety and Performance Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI worry that if I don\u0026rsquo;t use AI, my teaching methods will become outdated compared to my colleagues.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerfectionism and Fear of Errors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI use AI for generating content because I want everything to be perfect before presenting it to students.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCognitive Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive Offloading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSometimes, I let AI generate responses instead of thinking through problems myself\u0026mdash;it\u0026rsquo;s just quicker and easier.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of Pedagogical Creativity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI-generated lesson plans make it easy, but I feel like I\u0026rsquo;m losing my creative touch in designing courses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTechnological Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEase of Access and Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe convenience of AI makes it tempting to use for everything\u0026mdash;I don\u0026rsquo;t even think about whether I should do it myself anymore.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLack of AI Literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI don\u0026rsquo;t always understand how AI works, so I trust it more than I probably should.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFeeling Powerful and Capable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI makes me feel more capable and in control of my workload.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Self-Efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI feel more confident in my academic abilities when AI helps me complete tasks efficiently.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIndividual Factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Reputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUsing AI-generated research summaries enhances my credibility and saves me time.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh Performance Expectations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThere\u0026rsquo;s a lot of pressure to produce high-quality work, so I use AI to meet expectations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow Academic Confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI lack confidence in my academic writing, so I rely on AI to refine my work.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLack of Scientific Research Engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI rarely engage in deep scientific research anymore because AI provides quick summaries and insights.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInstitutional Factors\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eHeavy Workload and Time Constraints\u003c/h2\u003e \u003cp\u003eMany participants reported that AI significantly reduces their workload, allowing them to focus on other academic responsibilities. They highlighted how AI assists with grading, content generation, and administrative tasks, particularly in time-sensitive situations. Some educators pointed out that institutional demands for research productivity and teaching effectiveness push them to rely on AI to manage multiple responsibilities.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Without AI, I would spend hours grading assignments and drafting course materials. Now, I can do it in minutes, which helps me keep up with my workload.\"(E5)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, a few participants expressed concerns that using AI could increase there worload in the filed of social sciences. A fellow up questions was aslked, there response was because they were not familiar with this new tool.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"It\u0026rsquo;s a double-edged sword\u0026mdash;AI helps me finish tasks faster, but I sometimes feel need more time to understand which increase the time that I need to finish writing the tasks \u0026ldquo; (E19).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLack of Institutional Guidance\u003c/h2\u003e \u003cp\u003eMost participants mentioned that their institutions have not provided clear policies or training on AI use, leaving them uncertain about the extent to which they should rely on it. Some educators stated that this lack of guidance makes them more dependent on AI, as they have no structured support to develop balanced AI integration strategies.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"There are no clear policies on how much AI use is appropriate, so I just use it however I think best.\" (E8).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, a few participants reported that their institutions may eventually regulate AI use more strictly, limiting its benefits.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I depend on AI now, but if my university decides to impose restrictions, I might struggle to adjust back to traditional methods.\" (E5)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePsychological Factors\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eAnxiety and Performance Pressure\u003c/h2\u003e \u003cp\u003eSeveral participants pointed out that AI use helps them manage anxiety related to teaching and research expectations. Some expressed that they feel pressure to stay updated with AI trends, fearing that not integrating AI into their work could make them appear outdated compared to their colleagues.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Everyone around me is using AI, and I don\u0026rsquo;t want to be left behind. I feel like I need to use it to stay relevant.\" (E2).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eNevertheless, a few participants mentioned that AI reliance sometimes increases stress, particularly when they feel uncertain about its accuracy.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I depend on AI, but at times, I worry that I might be using incorrect or biased information without realizing it.\" (E9).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePerfectionism and Fear of Errors\u003c/h2\u003e \u003cp\u003eSome participants described themselves as perfectionists, stating that AI helps them ensure accuracy in research, lesson planning, and assessment design. They mentioned that AI allows them to refine content and eliminate errors, making them more confident in their work.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I use AI to check everything. I don\u0026rsquo;t want to submit anything that isn\u0026rsquo;t polished and error-free.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBut, a few educators expressed concerns that this dependency could prevent them from developing confidence in their own academic skills.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I feel like I\u0026rsquo;ve lost my ability to draft a paper from scratch. I always turn to AI first.\" (E 6)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCognitive Factors\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eCognitive Offloading\u003c/h2\u003e \u003cp\u003eMost participants acknowledged that AI has become their default tool for information retrieval and content creation, reducing the effort required for critical thinking and problem-solving.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I used to brainstorm lesson plans myself. Now, I ask AI, and it gives me something within seconds.\" (E15)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, some educators expressed concern that relying on AI too frequently might weaken their cognitive engagement with their work.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I wonder if I\u0026rsquo;m losing my ability to think critically because AI does the thinking for me.\" (E 10).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLoss of Pedagogical Creativity\u003c/h2\u003e \u003cp\u003eA few participants pointed out that AI-generated content makes teaching more convenient but limits their creativity in designing course materials.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I used to create my own case studies and interactive activities. Now, I just modify what AI generates.\" (E17)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, some educators mentioned that AI enhances their creativity by providing new perspectives and ideas they wouldn\u0026rsquo;t have considered otherwise.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI gives me different angles to approach a topic, which actually improves my lesson planning.\" (E22)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTechnological Factors\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003eEase of Access and Automation\u003c/h2\u003e \u003cp\u003eMany participants expressed that AI tools are readily available and easy to use, making them an attractive option for daily academic tasks. They mentioned that AI helps them save time, generate ideas, and organize content efficiently.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Why spend hours writing something when AI can give me a great draft in seconds?\" (E 21)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, a few educators pointed out that the ease of access makes it tempting to rely on AI for everything, leading to unintentional dependency.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"The more I use AI, the harder it becomes to work without it. It\u0026rsquo;s like having a calculator for everything.\" (E 20)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLack of AI Literacy\u003c/h2\u003e \u003cp\u003eSome participants admitted that they lack a deep understanding of AI\u0026rsquo;s inner workings, leading them to trust its outputs without fully questioning them.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I know AI isn\u0026rsquo;t perfect, but I don\u0026rsquo;t always know how to verify the accuracy of what it generates.\" (E 18)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA few educators expressed concerns that this lack of AI literacy might make them overly dependent on AI-generated insights.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I sometimes use AI without thinking critically because I assume it knows better than I do.\" (E1)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eFeeling Powerful and Capable\u003c/h2\u003e \u003cp\u003eSome educators pointed out that AI gives them a sense of empowerment, allowing them to produce high-quality content more efficiently.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"With AI, I can complete tasks that would have taken me hours in just a few minutes. It makes me feel more capable.\" (E3)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMoreover, a few participants expressed concerns that this sense of control is misleading, as they might be overestimating AI\u0026rsquo;s reliability.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI makes me feel smarter, but I sometimes wonder if I\u0026rsquo;m just relying on it too much without questioning its accuracy.\" (E 3).\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eIndividual Factors\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBased on the coding book (Table\u0026nbsp;2) in the supplementary files, individual factors include academic reputation, high performance expectation, low academic self-efficacy, and lack of scientific research engagement.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eAcademic reputation\u003c/h2\u003e \u003cp\u003eThe majority of participants reported that their academic reputation is a crucial factor influencing their reliance on AI, particularly in scientific research. One faculty member noted that he is a well-known researcher who frequently publishes scientific work. However, due to his busy schedule with teaching and other responsibilities, he uses AI to assist in writing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eAcademic Self-Efficacy\u003c/h2\u003e \u003cp\u003eSome participants mentioned that using AI enhances their confidence in their academic abilities.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI helps me refine my ideas, making my work more professional and well-structured.\" (E6)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOther participants expressed concern that relying on AI might affect their academic reputation, as AI-generated content could be perceived as lacking originality.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I worry that using AI too much might make others question the authenticity of my work.\" (E7)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLow Academic Confidence\u003c/h2\u003e \u003cp\u003eA few participants admitted that AI serves as a tool for procrastination, allowing them to delay tasks while still producing quick results when needed.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I put off writing papers because I know AI can help me generate content at the last minute.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, some educators expressed that AI use compensates for their low academic confidence, making them feel more capable in completing their work.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I don\u0026rsquo;t always trust my writing skills, so AI gives me the reassurance I need to finalize my work.\"\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMotivational Decline\u003c/h3\u003e\n\u003cp\u003eFor the purpose of this study, the researchers consider motivation decline referes to the gradual reduction in an individual's drive, enthusiasm, or willingness to engage in various academic activities. Many subthemes reported by the participants fail within motivation decline category including procrastination, increased laziness, erode intrinsic motivation, reduce individual initiatives, and deeply depends on AI in simple tasks.\u003c/p\u003e \u003cp\u003eSeveral participants mentioned that AI has encouraged procrastination, as they know they can complete tasks quickly with AI assistance.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Before AI, I would start preparing my lectures well in advance. Now, I just rely on AI to generate materials the night before.\" (EFG28).\u003c/em\u003e However, a few educators pointed out that while AI helps them meet deadlines, it sometimes leads to rushed work that lacks depth.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I meet my deadlines, but sometimes I feel like my work isn\u0026rsquo;t as thoughtful as it used to be because I rely on AI to speed things up.\" (E15)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA few educators mentioned that AI has made them more efficient but acknowledged that it can be tempting to take shortcuts.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I\u0026rsquo;m more productive with AI, but I also recognize that I use it to avoid thinking through certain problems myself.\" (EFG13)\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003ePedagogical Erosion\u003c/h2\u003e \u003cp\u003eIn this study, the researchers defined pedagogical erosion as the gradual decline in the effectiveness, relevance, and innovation of teaching practices over time. Therefore, the researchers found many subthemes categorized under this theme including perceived professional inadequacy, erosion pedagogy autonomy, reduce active engagement with students, weakened academic mentorship, and reduce self-confidence.\u003c/p\u003e \u003cp\u003eSome participants pointed out that AI helps them design better materials by providing fresh perspectives and structuring information effectively.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI gives me a strong starting point for my lessons. I still personalize them, but it definitely saves me time.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFew participants mentioned that their interactions with students have become less personal since integrating AI into their workflow.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I used to spend more time giving individualized feedback. Now, I use AI-generated comments, and I feel like I\u0026rsquo;m not engaging with my students as much.\"(E11)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, some educators argued that AI allows them to focus on more meaningful discussions rather than repetitive grading tasks.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI handles the routine work, so I can dedicate more time to having deeper discussions with my students.\" (E20)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMany participants expressed concerns that AI has made them doubt their own expertise, as they often feel AI-generated content is more structured or insightful than what they can produce on their own.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"Sometimes I feel like AI writes better than I do. It\u0026rsquo;s unsettling to think that I might not be as good as I used to be.\"(EFG35)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, a few educators pointed out that AI boosts their confidence by helping them refine their work. \u003cem\u003e\"AI isn\u0026rsquo;t replacing my skills\u0026mdash;it\u0026rsquo;s just giving me an extra layer of support to make my work better.\" (EFG14)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSome participants mentioned that they feel less capable without AI, particularly when completing research or complex writing tasks.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"If AI were suddenly unavailable, I don\u0026rsquo;t know how I\u0026rsquo;d manage my workload. I\u0026rsquo;ve started to rely on it too much.\"\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, a few educators expressed that AI dependency is a matter of perspective and that it can be used wisely without losing professional competence.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI is a tool, not a replacement. As long as we use it strategically, we can avoid becoming too dependent.\" (E16)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eEthical and Integrity Risks\u003c/h2\u003e \u003cp\u003eIn this study, the researchers defined ethical and integrity risks as the challenges that arise when AI influences academic practices. Several subthemes emerged under this category, including increased plagiarism rate, increased copyright infringement, academic misconduct, undermining human responsibility, and increased misleading information.\u003c/p\u003e \u003cp\u003eSome participants expressed concerns that AI facilitates plagiarism and reduces students\u0026rsquo; accountability for their work. \"\u003cem\u003eIt\u0026rsquo;s so easy for students to copy-paste AI-generated responses without truly engaging with the material.\" (E7)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eOthers pointed out that AI tools blur the lines of originality and authorship, making it difficult to differentiate between human and machine-generated work. \u003cem\u003e\"I worry that students are submitting AI-generated essays without understanding the concepts themselves.\" (E12)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eA few educators highlighted that AI-generated content sometimes lacks accountability, as it can produce misleading or biased information. \"\u003cem\u003eAI sometimes generates convincing but factually incorrect statements, and students don\u0026rsquo;t always verify them.\" (EFG21)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eOn the other hand, some faculty members believe AI can be leveraged ethically if students are taught responsible AI use. \u003cem\u003e\"We should integrate AI literacy into our curriculum so students learn how to use these tools without compromising integrity.\" (E26)\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eSocial Fragmentation\u003c/h2\u003e \u003cp\u003eThe researchers identified social fragmentation as the weakening of human connections and interactions due to AI integration in education. Subthemes under this category include negatively impacting students\u0026rsquo; emotional state, diminishing social development, reducing human interaction, and decreasing collaboration among humans.\u003c/p\u003e \u003cp\u003eMany participants noted that students are becoming more isolated as AI tools replace traditional peer interactions in collaborative assignments. \u003cem\u003e\"Group discussions are not as engaging anymore because students rely on AI instead of exchanging ideas with their peers.\" (E9)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eHowever, some faculty members suggested that AI can be used to enhance social learning if implemented thoughtfully. \u003cem\u003e\"If we integrate AI strategically\u0026mdash;such as using it to facilitate discussions rather than replace them\u0026mdash;it can actually strengthen collaboration.\" (E30)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eCreativity Suppression\u003c/h2\u003e \u003cp\u003eCreativity suppression refers to the risk that AI may homogenize knowledge production, restrict creativity, and limit students\u0026rsquo; ability to seek diverse information. Several subthemes emerged, including homogenization of knowledge production, restricted creativity, and restricted information seeking.\u003c/p\u003e \u003cp\u003eMany participants reported that AI-generated content tends to produce generic, standardized responses, leading to a decline in original thought. \u003cem\u003e\"Students\u0026rsquo; assignments are starting to look the same because they rely on AI-generated structures.\" (EFG15)\u003c/em\u003e\u003c/p\u003e \u003cp\u003eSome educators emphasized that AI discourages deep research, as students may settle for AI-generated summaries instead of exploring diverse sources. \u003cem\u003e\"Before AI, students would read multiple papers. Now, they just use AI to summarize, and I feel like they\u0026rsquo;re missing out on critical engagement.\" (E14)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsequences of AI dependency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain Theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubtheme\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eSkills Atrophy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of critical thinking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakening problem-solving abilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiminishing research skills\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiminishing writing skills\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced analytical skills\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecreased thinking capacity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoss of decision-making abilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce ability to synthesize independently\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakened judgment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eMotivational Decline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOver-reliance on AI for simple tasks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProcrastination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced motivation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased laziness\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErode intrinsic motivation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce individual initiatives\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePedagogical Erosion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErosion of pedagogical autonomy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduced active engagement with students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeakened academic mentorship\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce self-confidence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived professional inadequacy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEthical and Integrity risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased plagiarism rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased copyright infringement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic misconduct\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndermine human responsibility\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased misleading information\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSocial Fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegatively impact students' emotional state\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiminish social development\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReduce human interaction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecreased collaboration among humans\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCreativity Suppression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHomogenization of knowledge production\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRestricted creativity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRestricted information seeking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eEstablishing clear boundaries for AI use\u003c/h3\u003e\n\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003eSelective Use of AI for Instructional Tasks\u003c/h2\u003e \u003cp\u003eMost participants reported that they use AI selectively, ensuring that it serves as a supporting tool rather than a replacement for their instructional role. They emphasized that while AI can streamline certain tasks, fundamental teaching activities such as mentoring, fostering discussions, and assessing student progress should remain human-led.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI helps with structuring lesson plans and summarizing content, but I make sure that my role as an educator remains central in guiding discussions and engaging with students.\" (E4)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSome participants pointed out that AI is particularly useful for administrative tasks and content organization but should not dictate teaching methods.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I let AI handle routine tasks like scheduling and summarizing, but when it comes to interactive teaching, I rely on my own expertise and instincts.\" (EFG33)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, a few participants expressed concerns that without defined limits, educators might unconsciously begin to depend too much on AI-generated content.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I initially used AI just for support, but over time, I found myself relying on it more than I intended. Now, I consciously limit my AI use to avoid dependency.\" (EFG28)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eEncouraging critical engagement with AI\u003c/h2\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eVerifying AI-Generated Content\u003c/h2\u003e \u003cp\u003eMany participants mentioned that they actively verify and refine AI-generated outputs before integrating them into their teaching materials. They expressed that AI can sometimes produce misleading, biased, or overly simplified information, requiring educators to act as content curators rather than passive users.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I never use AI-generated content without reviewing it first. It can be a great starting point, but I always fact-check and refine the materials to align with my course objectives.\" (EFG31)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSome educators pointed out that they encourage students to critically analyze AI-generated responses rather than accepting them at face value.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I tell my students that AI is a tool, not an authority. They need to critique what it produces, question its assumptions, and think beyond the outputs.\" (EFG32)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, a few participants expressed concern that not all educators take the time to evaluate AI content carefully, which could lead to inaccuracies in teaching.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"One of my worries is that some educators might blindly trust AI-generated materials, which could introduce errors or outdated information into their lessons.\" (EFG17)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003eBalancing AI with human-centered teaching\u003c/h2\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003eHybrid intelligence\u003c/h2\u003e \u003cp\u003eThe majority of participants emphasized the significance of hybrid intelligence in mitigating excessive reliance on AI in academic work and scientific research. When asked to elaborate on the concept of hybrid intelligence, participants defined it as the integration of human intelligence with machine intelligence, ensuring that AI-generated outputs are critically assessed before being adopted. This approach encourages users to engage in deeper reflection and independent analysis rather than passively accepting AI-generated content.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePrioritizing interactive and discussion-based learning\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMost participants expressed that they intentionally design their courses to emphasize live discussions, debates, and student-driven learning to counterbalance AI-generated content. They believe that human interaction remains essential for fostering deep understanding and critical thinking.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI is great for providing structured information, but meaningful learning happens when students engage in discussions, challenge ideas, and interact with their peers and instructors.\" (E15)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSome participants mentioned that they use AI to supplement discussions by generating diverse perspectives, but they ensure that students analyze and debate those perspectives rather than passively accepting them.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I sometimes use AI to provide multiple viewpoints on a topic, but I make sure my students critically evaluate and compare them rather than just absorbing the information.\" (EFG25)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eHowever, a few participants pointed out that some educators struggle to balance AI integration with traditional teaching, leading to a more passive learning environment.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I've seen cases where AI-generated lectures replace interactive teaching, and I worry that students might become passive learners rather than active thinkers.\" (EFG32)\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eMaintaining pedagogical creativity\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eUsing AI as a Spark for Innovation, Not a Substitute for Creativity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMany participants reported that they leverage AI to generate new ideas for lesson planning but ensure that their personal creativity remains the driving force. They view AI as a brainstorming partner rather than a content creator.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I use AI to generate multiple lesson ideas, but I always customize them to fit my teaching style and my students' needs.\" (E3)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSome educators pointed out that AI helps them explore innovative approaches to teaching but emphasized that human creativity is irreplaceable.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI can suggest engaging activities, but it\u0026rsquo;s my job to refine them and add the human element that makes learning meaningful.\" (E22)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, a few participants expressed concerns that excessive AI use might lead to a decline in originality if educators become overly reliant on AI-generated content.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I fear that if we depend too much on AI, we might lose the uniqueness of our teaching styles. That\u0026rsquo;s why I try to balance AI use with my own creative input.\" (EFG26)\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003eFostering student AI literacy and ethical use\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eTeaching Students to Use AI Responsibly\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSome participants expressed that they incorporate AI literacy into their courses to help students use AI effectively while avoiding over-reliance. They believe that educators should guide students in understanding AI\u0026rsquo;s strengths and limitations.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I teach my students how to use AI as a research tool but also emphasize that they must engage critically with the results rather than just accepting AI-generated answers.\" (EFG32)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMost participants reported that they actively discourage students from using AI for academic shortcuts, instead encouraging them to use AI as a means of enhancing learning.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"AI can help students brainstorm ideas, but they need to put in the effort to analyze and expand on those ideas instead of just submitting AI-generated content.\" (EFG34)\u003c/em\u003e \u003c/p\u003e \u003cp\u003e However, a few participants pointed out that some students misuse AI for assignments, and educators must establish clear guidelines on ethical AI use.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"We need to teach students that AI is a tool to assist learning, not a way to bypass intellectual effort.\" (EFG11)\u003c/em\u003e \u003c/p\u003e\n\u003ch3\u003eContinuous professional development and peer collaboration\u003c/h3\u003e\n\u003cp\u003e \u003cem\u003eEngaging in AI training and professional learning communities\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSeveral participants mentioned that they actively seek professional development opportunities to improve their AI literacy and refine their AI integration strategies. They believe that educators must continuously learn to ensure that AI is used responsibly and effectively.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I regularly attend AI workshops to stay updated on best practices. The more I understand AI, the better I can integrate it into my teaching without becoming dependent on it.\" (E5)\u003c/em\u003e \u003c/p\u003e \u003cp\u003e Some educators pointed out that they collaborate with colleagues to share AI integration strategies, discuss ethical concerns, and develop guidelines for responsible AI use.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"We have faculty discussions on AI use where we exchange ideas on how to integrate AI without compromising teaching quality. These discussions help us find the right balance.\" (EFG36)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, a few participants expressed that some institutions lack adequate AI training programs, leaving educators to navigate AI integration on their own.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\"I wish there were more structured AI training for educators. Right now, we mostly figure it out through trial and error.\" (EFG35)\u003c/em\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights the factors behind AI dependency, the consequences of AI dependency in higher education, and the strategies to balance the use of AI in teaching and scientific research. Therefore, the findings of provide a crucial understanding of AI dependency among educators in higher education, highlighting institutional, psychological, cognitive, technological, and individual factors contributing AI dependency. Furthermore, the consequences of AI reliance were explored, emphasizing skills atrophy, pedagogical erosion, motivational decline, ethical risks, social fragmentation, and creativity suppression. These findings reflect and extend the existing literature on AI integration in education.\u003c/p\u003e \u003cp\u003eConsistent with prior research [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] this study found that institutional workload pressures drive educators to rely on AI to manage administrative and instructional responsibilities. Similar to the findings of [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], educators in this study reported that AI tools significantly alleviate grading and content creation burdens. However, as [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] pointed out, the lack of institutional guidelines on AI use fosters uncertainty, leading some educators to overuse AI in ways that could compromise their pedagogical agency.\u003c/p\u003e \u003cp\u003ePsychological factors, particularly anxiety and perfectionism, emerged as critical drivers of AI reliance. These results corroborate prior findings by [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], who identified impulsivity and perfectionism as predictors of problematic AI use. Educators in this study expressed concerns about staying current with AI advancements, mirroring similar trends found in student-related studies where fear of falling behind led to increased technology dependence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, self-critical perfectionists reported using AI to ensure accuracy, reflecting the findings of [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] who highlighted the link between perfectionism and digital tool dependency.\u003c/p\u003e \u003cp\u003eCognitive offloading emerged as a prevalent theme, with educators admitting that AI-generated content reduced their engagement in deep critical thinking and pedagogical creativity. These findings resonate with those of [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], who warned that excessive AI reliance may erode analytical skills. The concern that AI use weakens decision-making aligns with research by [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] who found that habitual reliance on digital tools can limit autonomous cognitive processing.\u003c/p\u003e \u003cp\u003eIn terms of technological factors, ease of access and automation were key contributors to AI dependency. Participants noted that AI streamlines academic tasks but simultaneously fosters overreliance, similar to the patterns observed in problematic internet use [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, the study found that limited AI literacy contributes to blind trust in AI outputs, echoing the findings of [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], who emphasized the need for critical evaluation of AI-generated content.\u003c/p\u003e \u003cp\u003eThe results of this study reflect previous research highlighting the impact of academic reputation on technology adoption. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] found that individuals with higher professional expectations tend to integrate technological tools to enhance their efficiency and maintain their academic standing. Similarly, [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] demonstrated that faculty members with strong publication records are more likely to use AI tools to optimize research processes, ensuring timely and high-quality outputs. Furthermore, [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] emphasized that performance pressure in academia drives educators to seek digital assistance, which, while beneficial, may also lead to overreliance on AI-generated content.\u003c/p\u003e \u003cp\u003eDespite these advantages, the study also revealed concerns regarding AI\u0026rsquo;s potential impact on academic integrity and originality. Some participants worried that excessive dependence on AI might blur the line between authentic scholarly contributions and AI-assisted content. These concerns echo findings from [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] who warned that AI-generated texts may compromise academic originality if not critically evaluated and ethically used. Additionally, [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] highlighted that while AI can enhance efficiency, intrinsic motivation and intellectual engagement remain essential in academic work.\u003c/p\u003e \u003cp\u003eMoreover, this study identified skills atrophy as a significant consequence of AI dependency, particularly in research, writing, and decision-making. The findings align with those of [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] who demonstrated that AI reliance reduces engagement in self-directed learning and problem-solving. Educators expressed concerns that frequent AI use undermines their ability to synthesize information independently, similar to what [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] observed in digital learning environments.\u003c/p\u003e \u003cp\u003ePedagogical erosion was another major consequence, with educators reporting diminished instructional autonomy and reduced student engagement. As [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] pointed out, academic emotions play a crucial role in sustaining motivation; when AI replaces human interaction in teaching, it may erode the intrinsic motivation of both educators and students. This aligns with [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] who argued that cognitive shortcuts often reduce long-term engagement in skill-building activities. This study also reinforced concerns about academic integrity, plagiarism, and misinformation in AI-generated content, similar to findings by [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] on technology adoption risks. Educators worried that AI facilitates academic dishonesty, supporting [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] argument that AI tools must be used with ethical safeguards.\u003c/p\u003e \u003cp\u003eSocial fragmentation was another notable theme, with educators highlighting reduced human interaction and student collaboration. These findings are consistent with [39] who found that technology-mediated learning environments often limit meaningful peer engagement. Finally, creativity suppression was reported as a risk, with educators noting that AI-generated content tends to standardize knowledge production. This aligns with research by [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] who emphasized the importance of intrinsic motivation in fostering innovation. When AI generates predefined structures, students and educators may lose opportunities to explore diverse ideas, ultimately limiting creative expression.\u003c/p\u003e \u003cp\u003eThe findings of this study provides several strategies to mitigate AI overreliance, including setting clear boundaries for AI use, verifying AI-generated content, fostering student AI literacy, and prioritizing interactive learning. These strategies align with [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] who recommended structured AI policies to ensure that AI serves as an educational enhancement rather than a substitute. One of the key strategies identified in this study to mitigate excessive reliance on AI is hybrid intelligence. Participants highlighted that hybrid intelligence\u0026mdash;an approach combining human cognitive abilities with machine intelligence\u0026mdash;ensures that AI-generated content is c\u003c/p\u003e \u003cp\u003eThe findings resonate with previous studies emphasizing the role of hybrid intelligence in optimizing AI use while preserving human creativity and decision-making. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] suggested that while AI tools streamline tasks, human oversight remains critical in ensuring the quality and originality of academic work. Similarly, [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] emphasized that hybrid intelligence fosters a symbiotic relationship between AI and users, where AI assists in data processing while human judgment ensures critical reflection and ethical decision-making.\u003c/p\u003e \u003cp\u003eMoreover, research by [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] highlighted that hybrid intelligence encourages metacognitive engagement, requiring individuals to analyze and evaluate AI outputs rather than passively accepting them. This aligns with [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] who argued that deep cognitive involvement is essential for sustained intellectual development and professional growth. In the academic context, hybrid intelligence supports the idea that AI should function as a collaborative tool rather than a replacement for human expertise [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the implementation of hybrid intelligence addresses concerns about skills atrophy, as identified in this study. By requiring educators to interact with AI outputs actively, this approach maintains critical thinking skills and pedagogical autonomy. This supports the findings of [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] who demonstrated that educators who critically engage with AI tools retain stronger analytical abilities compared to those who rely on AI passively. Moreover, continuous professional development was emphasized as a crucial strategy. As [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] noted, educators who engage in AI literacy training are better equipped to integrate AI responsibly while maintaining pedagogical integrity. Peer collaboration and faculty discussions were also suggested as essential components in developing institutional guidelines for responsible AI use.\u003c/p\u003e\n\u003ch3\u003eLimitation and future studies\u003c/h3\u003e\n\u003cp\u003eThis study has several limitations that should be acknowledged. First, the study was conducted within a specific higher education context, which may limit the generalizability of the findings to different educational systems and cultural settings. Future research should explore AI dependency in diverse academic institutions across different regions to provide a broader understanding of the phenomenon.\u003c/p\u003e \u003cp\u003eSecond, the study primarily relied on self-reported data from faculty members, which may be subject to social desirability bias. Participants may have underreported their reliance on AI or overstated their critical engagement with AI tools. Future studies could incorporate observational methods or data analytics to assess actual AI usage patterns and their impact on pedagogical practices.\u003c/p\u003e \u003cp\u003eThird, while this study focused on the perspectives of educators, it did not examine the viewpoints of students regarding AI dependency in learning. Given that AI is increasingly being integrated into student learning processes, future research should explore how AI reliance affects student engagement, cognitive development, and academic performance.\u003c/p\u003e \u003cp\u003eAdditionally, this study explored AI dependency at a general level but did not investigate the potential disciplinary differences in AI use. Certain academic disciplines may have unique AI integration challenges and opportunities. Future studies should examine how AI reliance varies across disciplines such as humanities, social sciences, engineering, and medical sciences.\u003c/p\u003e \u003cp\u003eFuture research should explore long-term consequences of AI dependency on faculty professional identity, academic creativity, and knowledge production. Longitudinal studies would be beneficial in assessing how AI reliance evolves over time and what institutional interventions might mitigate potential risks.\u003c/p\u003e\n\u003ch3\u003eTheoretical and Practical Implications\u003c/h3\u003e\n\u003cp\u003eThe findings of this study contribute to theoretical discussions on technology adoption in higher education by highlighting AI dependency as a complex interplay of institutional, psychological, and cognitive factors. This study expands on existing theoretical frameworks such as the I-PACE model [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] by demonstrating how AI reliance among educators aligns with patterns of problematic technology use. From a practical perspective, these findings emphasize the need for clear institutional policies, professional development programs, and AI literacy initiatives that promote responsible AI integration. By fostering hybrid intelligence, educators can maintain their pedagogical autonomy while leveraging AI's benefits, ensuring a balanced and ethical approach to AI use in academia.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examines AI reliance among educators, analyzing the institutional, psychological, and cognitive factors that influence its integration into academic practices. While AI contributes to efficiency, knowledge management, and academic productivity, excessive dependence may present challenges related to pedagogical autonomy, critical thinking, and professional identity. The findings highlight the value of hybrid intelligence as a means of integrating AI responsibly, ensuring it complements rather than replaces human intellectual engagement. By contextualizing AI reliance within professional agency and academic integrity, this study builds on existing models of AI adoption. However, the findings should be interpreted within the study's scope, as variations in institutional policies, technological access, and disciplinary contexts may influence AI adoption differently. From a practical perspective, the results reinforce the need for institutional policies, faculty training, and AI literacy initiatives to support responsible AI use. Rather than advocating for or against AI in education, this study emphasizes the need for a balanced approach where AI serves as a tool to enhance human capabilities. Future research should further investigate AI\u0026rsquo;s long-term influence on faculty development, student learning, and knowledge production, particularly across diverse academic settings and evolving technological landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Helsinki declaration. In accordance with ethical guidelines, informed consent for participation was obtained from all the participants. This study was approved by the Scientific Research Ethics Committee of An Najah National University reference (Edu. Dec. 2024/18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Contribution:\u003c/p\u003e\n\u003cp\u003eThe author was responsible for all aspects of the research, including conceptualization, design, data collection, data analysis, and manuscript preparation. The author has read and approved the final version of the manuscript\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical Trial\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eUsing Gen AI tool declaration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors use ChatGPT for proofreading of the introduction and organizing ideas in the literature review, all the authors take the responsibility of the accuracy of the content after proofreading and organizing the ideas of the introduction and literature review.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSinghal K, Azizi S, Tu T, Mahdavi SS, Wei J, Chung HW, et al. Large language models encode clinical knowledge. 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Psychol Sci. 2012;23(1):69\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0956797611418676\u003c/span\u003e\u003cspan address=\"10.1177/0956797611418676\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI dependency, Generative AI, procrastination, AI reliance, hybrid intelligence","lastPublishedDoi":"10.21203/rs.3.rs-6127885/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6127885/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRapid AI development has significantly changed education. This study explored factors influencing educators' over-reliance on AI, the consequences of AI dependency, and strategies to balance AI use in higher education. A qualitative approach using semi-structured interviews and focus groups collected data from 46 diverse participants. Thematic analysis revealed factors driving AI dependency—including academic reputation, self-efficacy, and institutional policies—and consequences such as skills atrophy, procrastination, and social fragmentation. Findings suggest hybrid intelligence and balanced AI teaching can be beneficial. Limitations include a small sample, and future research should target larger, more diverse populations.\u003c/p\u003e","manuscriptTitle":"AI Paradox in Higher Education: Understanding Over-Reliance, Its Impact, and Sustainable Integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-15 15:28:04","doi":"10.21203/rs.3.rs-6127885/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4dfff95b-5da6-46fb-adb4-8f61208675f9","owner":[],"postedDate":"April 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T09:09:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-15 15:28:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6127885","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6127885","identity":"rs-6127885","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}