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22 SES 03 C: AI in HE: challenges and risks
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22. Research in Higher Education
Paper Higher Education Teachers’ Job Resources, Challenges, and Hindrances Related to Generative Artificial Intelligence University of Turku, Finland Presenting Author:Generative artificial intelligence [GenAI] has appeared to be part of higher education. Higher education institutions needed to make fast decisions, as the GenAI tools were released for the public. At first there were no clear guidelines on how to use it, and some teachers moved back to supervised exams (Scarfe et al., 2024; Sweeney, 2023). Now the GenAI tools are allowed to be used in multiple higher education institutions, and teachers and students may use them to assist in their work. Previous studies demonstrate how students and teachers consume GenAI; assisting themselves to make notes or materials, personalizing learning experiences, or utilizing it to write essays as an essay mill (Meng et al., 2025; Sweeney, 2023). It may be used as a tool for cheating (Sweeney, 2023) or to build artificial intelligence literacy (Chiu et al., 2025). However, there is a research gap on how GenAI is perceived as job demands or resources. From teachers’ perspective the GenAI tools might help at work, but teachers need to consider also how their students have used GenAI, which might increase work amount. In this study, we researched how generative artificial intelligence increases burden or assists teachers to cope with their work. The job demands and resources model [JD-R] was used as a theoretical background for the research (Demerouti et al., 2001). The JD-R model examines the job strain in two different ways: in job demands and job resources. Job demands are all aspects of a job, which increase physical, psychological, social, and mental efforts. Too many job demands appear to lead to exhaustion, more frequent and longer absences, and finally to burnout (Bakker et al., 2003; Demerouti et al., 2001; Schaufeli & Bakker, 2004). The job resources are all positive aspects of work, which decrease the job demands’ effect, increase the engagement, enable personal growth, and assist to achieve work goals and flow (Bakker et al., 2003; Demerouti et al., 2001; Schaufeli & Bakker, 2004). Despite the division between demands and resources is simple to comprehend, not all demands are similar. The job demands can be divided into two categories: job hindrances, and job challenges (Bakker & Demerouti, 2024; Xanthopoulou et al., 2009). Job hindrances are those demands that deplete resources and do not increase motivation, such as decreased time for work, interpersonal conflicts, or deficiency of autonomy. However, job challenges are demands, which may increase motivation even though they deplete resources. These job challenges may include that the task is challenging enough, learning new skills takes time, but could be rewarding. With this study, we aim to light up the situation where higher education teachers are and provide practical solutions for teachers and higher education institutions. Therefore, our research questions are following: RQ1) What job resources, challenges and hindrances higher education teachers have perceived related to GenAI in their work? RQ2) How is pedagogical training related to perceived job resources, challenges, and demands? Even though the research is done in one country, similar events are happening in other European countries. Therefore, this presentation generates opportunities for building comprehension about higher education teachers’ experiences related to GenAI. Methodology, Methods, Research Instruments or Sources Used The data were gathered using Webropol questionnaire, which included closed and open-ended questions about generative artificial intelligence. The total number of answers was over 300. The participants were higher education teachers from four different institutions. All participants obtained privacy policy and information from the study, and their consents to participate were asked. The data were analyzed using theory-driven content analysis (Elo & Kyngäs, 2008), using the JD-R model as a framework. In the analysis process, two researchers discussed initially the theory, and wrote down the codebook, where they made framework for four different categories; 1) teaching work job resources, challenges and hindrances; 2) interaction and communication job resources, challenges and hindrances; 3) developmental job resources, challenges and hindrances; and 4) organizational job resources, challenges and hindrances. After that, they independently categorized all answers for questions considering job resources, challenges, and hindrances. Despite using theory-driven analysis, researchers created one more category based on the data; 5) research, academic integrity and ethical job resources, challenges and hindrances. The disagreements were discussed together and finally all the answers were categorized and quantified. They calculated percental agreement, kappa, and pre-adjusted bias adjusted kappa for enhanced reliability of the study (Sim & Wright, 2005). Quantified data were analyzed with the SPSS-program to see what relation teachers' pedagogical training and the technological acceptance had with job demands and resources. Conclusions, Expected Outcomes or Findings The initial findings appear to disclose that higher education teachers are perceiving GenAI as job resources, challenges, and hindrances differently. GenAI tools may affect the burden in teaching work, which is the first category. Teachers are using it in making materials, researching references, assisting in developing assignments, and evaluating students’ work. However, GenAI is not only providing ease to teachers. They expressed that GenAI is making them use more time in their work as the initial GenAI made text needed heavy modifications to be useful, the references needed to be checked for hallucinations, and students used it without explaining how. The second category revealed that GenAI tools may assist teachers to communicate with students in their own mother tongue, as it may provide quick translations. They endorsed the ability to make subtitles to videos. However, teachers did not believe everything which was made by GenAI. They needed to proofread texts carefully, and some of the materials were unusable. In the third and fourth categories, the other survey questions about how and where teachers have developed their GenAI skills, revealed that they have significant differences. Some were waiting for organizations to teach and train them, while others were taking the initiative and learning themselves. Teachers’ expectations were significantly different. The final category revealed that some teachers think about how they can teach AI-literacy to their students, how to enhance academic integrity, and ethical and environmental questions regarding GenAI tools. Teachers balance between the resources that GenAI tools provide, and the demands it may increase. These themes are presented in the conference, and how they are related to pedagogical training and other background variables. Presentation also leads to discussion about what higher education teachers, students, and organizations may do to boost personal growth, assisting achieving work goals, and developing GenAI skills. References Bakker, A. B., & Demerouti, E. (2024). Job demands–resources theory: Frequently asked questions. Journal of Occupational Health Psychology, 29(3), 188–200. https://doi.org/10.1037/ocp0000376 Bakker, A. B., Demerouti, E., de Boer, E., & Schaufeli, W. B. (2003). Job demands and job resources as predictors of absence duration and frequency. Journal of Vocational Behavior, 62(2), 341–356. https://doi.org/10.1016/S0001-8791(02)00030-1 Chiu, T. K. F., Çoban, M., Sanusi, I. T., & Ayanwale, M. A. (2025). Validating student AI competency self-efficacy (SAICS) scale and its framework. Educational Technology Research and Development. https://doi.org/10.1007/s11423-025-10512-y Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499 Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x Meng, N., Mat Deli, M., & Abdul Rauf, U. A. (2025). Educational Technology and AI: Bridging Cognitive Load and Learner Engagement for Effective Learning. Sage Open, 15(4), 21582440251395930. https://doi.org/10.1177/21582440251395930 Scarfe, P., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing Test” case study. PLOS ONE, 19(6), e0305354. https://doi.org/10.1371/journal.pone.0305354 Schaufeli, W. B., & Bakker, A. B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25(3), 293–315. https://doi.org/10.1002/job.248 Sim, J., & Wright, C. C. (2005). The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements. Physical Therapy, 85(3), 257–268. https://doi.org/10.1093/ptj/85.3.257 Sweeney, S. (2023). Who wrote this? Essay mills and assessment – Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education, 21(2), 100818. https://doi.org/10.1016/j.ijme.2023.100818 Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Reciprocal relationships between job resources, personal resources, and work engagement. Journal of Vocational Behavior, 74(3), 235–244. https://doi.org/10.1016/j.jvb.2008.11.003 22. Research in Higher Education
Paper Transformational Leadership and Teachers’ AI Competence Self-Efficacy in Chinese Higher Education: The Mediating Roles of Organisational Support and Teacher Collaboration 1: China University of Mining and Technology, China, People's Republic of; 2: University of Groningen Presenting Author:The accelerated integration of generative artificial intelligence (AI) into higher education has created an urgent need for teachers who can effectively leverage AI to create individualised and responsive learning experiences (Abbasi et al., 2024). However, despite significant institutional investments in professional development (PD), evidence across Spain (Galindo-Domínguez et al., 2024), Hungary (Dringó-Horváth et al., 2025), and the UK (Atkinson-Toal & Guo, 2024) suggests that the majority of in-service teachers lack confidence in and ability of integrating AI into pedagogical practices. This persistent gap suggests that PD programmes alone may be insufficient, pointing to the need for research examining how broader workplace conditions, such as leadership practices, collegial relationships, and institutional support structures, shape teachers’ beliefs and practices. Social Cognitive Theory (SCT) provides a useful lens for understanding part of this phenomenon. According to Bandura (1986), self-efficacy beliefs develop through the reciprocal interaction of personal cognition, environmental factors, and behavioural experiences. Prior research has established that transformational leadership positively predicts a range of teacher outcomes, including knowledge sharing (Hoang & Le, 2024), engagement with learning cultures (Long & Xia, 2025), and collaborative behaviours (Schmitz et al., 2025). In particular, Liu and Hallinger (2024) noted that middle leaders may influence teachers more directly than principals do in higher educational contexts. However, the interplay of these factors within the nascent context of AI integration in higher education has yet to be fully elucidated. Moreover, existing research has largely assumed that teacher collaboration uniformly benefits professional outcomes, yet this assumption warrants empirical scrutiny in the context of emerging technologies where collaborative norms may not be established. This research addresses these gaps by examining a mediation model in which transformational leadership enacted by academic associate deans predicts teachers’ AI competence self-efficacy, with perceived organisational support and teacher collaboration as potential mediators. This research is conducted in China, where the digital transformation reform of higher education has required teachers to integrate AI into routine work in the past five years. Characterised by hierarchical administrative structures and collectivist cultural values, the Chinese context offers a distinctive setting that may reveal mechanisms obscured in Western contexts and provide a comparative reference point for European educators. Using an explanatory sequential mixed-methods design, we first collected survey data from 405 teachers across five universities in one Chinese province. Squares Structural Equation Modelling (PLS-SEM) revealed that transformational leadership (TL) was a strong predictor of perceived organisational support (OS) (β=0.681, p<.001), teacher collaboration (TC) (β=0.310, p<.001), and teachers’ AI competence self-efficacy (AI_SE) (β=0.308, p<.001). Mediation analysis confirmed OS as a significant indirect pathway (β=0.127). However, contrary to expectations, TC demonstrated weak mediating effects (VAF=0.121), suggesting a potential disconnect between collaborative activity and self-efficacy development, a pattern we term the “collaborative paradox”. Given that the distinct pedagogical environments of STEM versus non-STEM disciplines may moderate the influence of TL, a multi-group analysis was conducted to rigorously examine divergences in the structural paths between TL and key outcomes. STEM teachers showed significantly stronger associations between TL and all outcome factors when compared to non-STEM teachers (TL to OS: βSTEM=0.836 vs. βNon-STEM=0.423, p<.001; TL to TC: βSTEM =0.604 vs. βNon-STEM=0.183, p<.001; TL to AI_SE: βSTEM=0.589 vs. βNon-STEM=0.171, p=.012). In contrast, TC predicted AI_SE only among non-STEM teachers (βNon-STEM=0.352 vs. βSTEM=-0.020, pdiff=.025). The qualitative analysis identified four themes that clarify mechanisms underlying these patterns: middle leaders’ roles in facilitating cognitive reframing and resource gatekeeping, an interaction-transformation gap in collaboration, and discipline-based cognitive logic filtering. These findings suggest that STEM teachers’ self-efficacy derives primarily from mastery experiences and vertical institutional support, whereas non-STEM teachers rely more heavily on horizontal peer relationships for emotional scaffolding against technology-related anxiety. Methodology, Methods, Research Instruments or Sources Used This study employed an explanatory sequential mixed-methods design, in which quantitative findings guided subsequent qualitative inquiry. Three research questions guided the investigation: RQ1: To what extent does transformational leadership predict teachers’ AI competence self-efficacy in Chinese universities? RQ2: Do perceived organisational support and teacher collaboration mediate the relationship between transformational leadership and teachers’ AI competence self-efficacy? RQ3: Do these relationships vary across disciplinary contexts (STEM vs. non-STEM)? In the quantitative phase, survey data were collected from 405 teachers from five comprehensive universities located within one province in eastern China. Participants were recruited through institutional contacts, yielding a convenience sample. While this sampling strategy limits generalisability beyond the study context, it enables access to a sufficient sample size for the planned analyses while controlling for regional policy variation. The study included four scales: the Teachers AI Competence Self-efficacy Scales (Chiu et al., 2024); the Global Transformational Leadership Scale (Carless et al., 2000); the Perceived Organisational Support Scale (Eisenberger et al., 1997); and the Teacher Collaboration Scale (Geijsel et al., 2009). Data were analysed using PLS-SEM, which was chosen for its suitability for predictive modelling and its robustness with complex models involving multiple mediators (Hair et al., 2019). Multi-group analysis examined whether path coefficients differed significantly between STEM and non-STEM teachers. The qualitative phase sought to explain the mechanisms underlying the quantitative findings particularly the unexpected weakness of the collaboration pathway (Cohen et al., 2002). Using purposive sampling informed by maximum variation principles, we recruited 11 teachers representing diverse disciplines, career stages, and levels of AI engagement. Participants were initially identified through survey respondents who indicated willingness to be interviewed. Additional participants were recruited through snowball referrals to ensure disciplinary diversity. Semi-structured interviews, lasting 30 to 60 minutes each, explored participants’ experiences with leadership, collaboration, and AI integration. Data were analysed using constructivist grounded theory methods (Charmaz, 2014), proceeding through initial, focused, and theoretical coding. This iterative process yielded four core themes that illuminated the quantitative patterns. This sequential design strengthens the study’s validity by enabling triangulation: qualitative findings contextualise and explain statistical relationships, while quantitative results ensure that qualitative interpretations are grounded in broader patterns (Cohen et al., 2002). Conclusions, Expected Outcomes or Findings This study offers three principal contributions to understanding how organisational conditions relate to teachers’ AI competence self-efficacy. First, the findings highlight TL as a robust predictor operating through multiple pathways, with perceived OS serving as the primary mediating mechanism. Interpreted through SCT, this suggests that middle leaders shape teachers’ self-efficacy beliefs not only through direct encouragement but also by cultivating environments perceived as supportive of professional risk-taking and experimentation with new technologies. Second, the weak mediating role of TC challenges prevailing assumptions that collegial interaction benefits technology integration. The qualitative findings suggest that collaboration around AI remains largely superficial, characterised by information exchange rather than deep pedagogical inquiry. Thus, it fails to provide the vicarious experiences or social persuasion that SCT identifies as sources of self-efficacy. This “collaboration paradox” invites reconsideration of how institution’s structure collaborative opportunities for emerging technology domains. Third, the pronounced disciplinary heterogeneity carries practical implications. For STEM teachers, whose self-efficacy appears grounded in mastery experiences and vertical support structures, institutions might prioritise hands-on experimentation opportunities and clear administrative endorsement. For non-STEM teachers, who benefit more from peer relationships, facilitated learning communities and mentorship programmes may prove more effective. Several limitations warrant acknowledgment. The cross-sectional design precludes causal inference. Longitudinal research is needed to establish temporal precedence in the future. The convenience sample from one Chinese province limits generalisability, and self-reported self-efficacy may not correspond to actual competence or classroom practice. Additionally, the collectivist, high power distance context of Chinese higher education may amplify leadership effects in ways that differ from European settings. Despite these limitations, this study provides a theoretically grounded, empirically supported framework that European educators and policymakers can use as a comparative reference when designing institution-level strategies to support teachers’ AI integration. References Abbasi, B. N., Wu, Y., & Luo, Z. (2024). Exploring the impact of artificial intelligence on curriculum development in global higher education institutions. Education and Information Technologies, 30(1), 547-581. https://doi.org/10.1007/s10639-024-13113-z Atkinson-Toal, A., & Guo, C. (2024). Generative Artificial Intelligence (AI) Education Policies of UK Universities. Enhancing Teaching and Learning in Higher Education, 2, 70-94. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ, 1986(23-28), 2. Carless, S. A., Wearing, A. J., & Mann, L. (2000). A short measure of transformational leadership. Journal of business and psychology, 14(3), 389-405. Charmaz, K. (2014). Constructing grounded theory (introducing qualitative methods series). Constr. grounded theory. Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2024). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667-6685. https://doi.org/10.1007/s10639-024-13094-z Cohen, L., Manion, L., & Morrison, K. (2002). Research methods in education. routledge. Dringó-Horváth, I., Rajki, Z., & T. Nagy, J. (2025). University teachers’ digital competence and AI literacy: Moderating role of gender, age, experience, and discipline. Education Sciences, 15(7), 868. Eisenberger, R., Cummings, J., Armeli, S., & Lynch, P. (1997). Perceived organizational support, discretionary treatment, and job satisfaction. Journal of Applied psychology, 82(5), 812. Galindo-Domínguez, H., Delgado, N., Losada, D., & Etxabe, J.-M. (2024). An analysis of the use of artificial intelligence in education in Spain: The in-service teacher’s perspective. Journal of digital learning in teacher education, 40(1), 41-56. Geijsel, F. P., Sleegers, P. J., Stoel, R. D., & Krüger, M. L. (2009). The effect of teacher psychological and school organizational and leadership factors on teachers' professional learning in Dutch schools. The elementary school journal, 109(4), 406-427. Hoang, T. N., & Le, P. B. (2024). The influence of transformational leadership on knowledge sharing of teachers: the roles of knowledge-centered culture and perceived organizational support. The Learning Organization, 32(2), 328-349. https://doi.org/10.1108/tlo-08-2023-0144 Liu, S., & Hallinger, P. (2024). The effect of department leadership on teacher professional learning in China: A multilevel moderated mediation model. Educational Management Administration & Leadership. https://doi.org/10.1177/17411432241232541 Long, Y., & Xia, Y. (2025). Leveraging transformational and instructional leadership for teacher professional development: A dual-mediation model of teacher self-efficacy and organisational culture. Teaching and Teacher Education, 164. https://doi.org/10.1016/j.tate.2025.105087 Schmitz, M.-L., Antonietti, C., Consoli, T., Gonon, P., Cattaneo, A., & Petko, D. (2025). Enhancing teacher collaboration for technology integration: the impact of transformational leadership. Computers & Education, 234. https://doi.org/10.1016/j.compedu.2025.105331 22. Research in Higher Education
Paper Reconceptualization of TPACK based on GenAI through Iranian University Faculty Members' Lived Experiences: A Phenomenological Study Bu-Ali Sina University, Iran, Islamic Republic of Presenting Author:The rise of AI tools, such as ChatGPT, has brought significant changes to higher education, as these tools are not only assistants as previous educational technologies but also actively participate in content creation (Denny et al., 2023). These technologies have introduced both opportunities to enhance instructional design, assessment, and student engagement, as well as challenges related to academic integrity, ethical use, and professional identity (Rasul et al., 2023; Yadav, 2024). Research shows that generative AI can facilitate instructional design, feedback, and self-directed learning (Roe & Perkins, 2025). At the same time, concerns have been raised about scientific authenticity, overreliance on students, and changes in academic learning (Khatri & Karki, 2023). As a result, new questions have arisen about their role in teaching and learning, the changing nature of scholarly work, and the evolving responsibilities of faculty members in higher education. One of these questions concerns changes in how university professors operate as the primary agents of education in higher education. As pedagogical models, ethical debates, and technologies continue to develop in this space, university teachers’ experiences of teaching with GenAI have yet to be explored in detail (Ellis, Han, & Cook, 2025). Tillmans et. Al. (2025) reviewed the state of GenAI in higher education, aiming to inform curriculum design and further developments within digital education. Findings revealed themes like mentorship, personalized learning, creativity, emotional intelligence, and higher-order thinking, highlighting the persistent need to align human-centred educational practices with the capabilities of GenAI technologies. Technological Pedagogical Content Knowledge (TPACK) has, for the past two decades, been the defining framework for teachers' knowledge of integrating intelligent and intentional technology into teaching (Mishra, Warr, & Islam, 2023). The TPACK framework provides a valuable lens for examining how faculty integrate GenAI tools, such as ChatGPT, into teaching. Mishra et al. (2023) argue that GenAI fundamentally reshapes the intersection of content, pedagogical, and technological knowledge within this framework, thereby requiring educators to develop new strategies to blend these domains effectively. Faculty must not only understand the subject matter (CK) and appropriate pedagogical methods (PK) but also navigate the unique affordances and limitations of GenAI (TK). Chiu (2025) also emphasizes that the TPAC framework should be reconsidered, given that artificial intelligence is a product of specific technologies. Experiences reported by instructors often reveal tensions, such as reconciling traditional teaching norms with AI-enhanced practices, managing academic integrity, and adapting assessments. Chan et. Al. (2025) examines how generative AI is used in teaching, learning, assessment, and research at Southeast Asian universities. Using hermeneutic phenomenology, the lived experiences of university teachers yielded three main themes: learning anew, disequilibrium and lack of rootedness, and ambiguity about new norms and practices. Teachers in that study reported that GenAI both enhanced efficiency and disrupted established habits, creating personal, fragmented, and evolving ways of working. Many experienced tension between traditional academic norms and the transformative potential of GenAI, raising concerns about integrity and professional roles. Participants highlighted the urgent need for clear AI guidelines, AI literacy, and targeted training to promote. Nevertheless, few studies have examined faculty members' perceptions of the use of generative artificial intelligence in teaching and learning. This is because this technology is under development, and therefore, such research seems necessary. This study examines faculty members’ experiences with generative AI in Iranian higher education, focusing on how these technologies influence teaching practices, professional roles, and perceptions of educational responsibility and may result in a reconceptualization of TPACK. Therefore, the main question this study seeks to investigate is: What is TPACK in the GenAI Era, as perceived by university faculty members’ lived experiences? Methodology, Methods, Research Instruments or Sources Used The present study adopted a phenomenological research approach to investigate participants' lived experiences. The participants consisted of faculty members at Bu-Ali Sina University in Hamedan, Iran. They were all selected from the Humanities department. Twelve participants were selected based on theoretical saturation. Data were collected through semi-structured interviews, each lasting approximately one hour. All interviews were audio-recorded and transcribed verbatim. The interview transcripts were analyzed using coding and categorization procedures. The trustworthiness of the findings was enhanced through iterative analysis and member checking. Conclusions, Expected Outcomes or Findings The study's findings showed that faculty members identified opportunities to improve access to educational resources, support customized learning, and increase efficiency in preparing course materials. They also raised challenges, including ethical concerns and scientific credibility, a lack of technical and educational readiness, and the complexity of managing fraud. In addition, their professional perceptions of their role indicate a shift from the traditional knowledge-transfer role to a guidance role. They also believe that it is not just their role that has changed, but also the nature of TPACK. They believed that, since GenAI could choose and even create content and make it pedagogical, they could move beyond the content they presented in the traditional classroom. This move is not only about the amount of content but also about the depth. They believe that the purpose of higher education may change in the future, so university professors should prepare students to face these conditions rather than emphasizing content. They also explained changes in the pedagogical process, noting that the most significant change is in the area of learning assessment. The findings indicate that although generative AI can provide new possibilities for optimizing educational activities, its effective adoption requires teacher training programs, clear ethical frameworks, and a rethinking of lesson design. This article contributes to a better understanding of how faculty interact with generative AI and provides a clear path for future research in this emerging field. References Chan, N. N., Bailey, R. P., Tan, M. H. J., Dipolog, G. F., Tan, G. W. H., Motevalli, S., ... & Ang, C. S. (2025). Generative artificial intelligence in a VUCA world: the ‘Lived Experiences’ of Southeast Asian teachers’ use of AI in higher education—International Journal of Educational Research, 133, 102733. Chiu, T. K. (2025). Developing intelligent-TPACK (I-TPACK) framework from unpacking AI literacy and competency: implementation strategies and future research direction. Interactive Learning Environments, 33(7), 4189-4192. Denny, P., Khosravi, H., Hellas, A., Leinonen, J., & Sarsa, S. (2023). Can we trust AI-generated educational content? comparative analysis of human and AI-generated learning resources. arXiv preprint arXiv:2306.10509. Ellis, R., Han, F., & Cook, H. (2025). Qualitatively different teacher experiences of teaching with generative artificial intelligence. International Journal of Educational Technology in Higher Education, 22(1), 33. Khatri, B. B., & Karki, P. D. (2023). Artificial intelligence (AI) in higher education: Growing academic integrity and ethical concerns. Nepalese Journal of Development and Rural Studies, 20(01), 1-7. Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and Generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235-251. Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W., ... & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning & Teaching, 6(1), 41-56. Roe, J., & Perkins, M. (2025). Generative AI in Self-Directed Learning: a thematic scoping review. Interactive Learning Environments, 1-12. Tillmanns, T., Salomão Filho, A., Rudra, S., Weber, P., Dawitz, J., Wiersma, E., ... & Reynolds, S. (2025). Mapping tomorrow’s teaching and learning spaces: A systematic review on GenAI in higher education. Trends in Higher Education, 4(1), 2. Yadav, D. S. (2024). Navigating the landscape of AI integration in education: opportunities, challenges, and ethical considerations for harnessing the potential of artificial intelligence (AI) for teaching and learning. BSSS Journal of Computer, 15(1), 38-48. | ||