Understanding students’ perceptions of generative AI: Implications for pedagogy and graduate employability
DOI:
https://doi.org/10.21153/jtlge2025vol16no1art2084Keywords:
GenAI, Technology Acceptance Model, personal innovativeness in IT, employability, AI in education, workforce transformation, digital competencies, higher education policyAbstract
As artificial intelligence (AI) transforms workplaces, understanding how future graduates engage with AI technologies is crucial for enhancing employability. This study investigates higher education students’ familiarity with and perceptions of generative artificial intelligence (GenAI) in their learning. Using the Technology Acceptance Model (TAM) and incorporating personal innovativeness in information technology, we examined factors influencing students’ adoption of GenAI. An online survey was conducted between April 30 and May 11, 2024, with 233 students from a college in northern Israel completing the questionnaire. Results revealed significant positive correlations, supporting the study’s theoretical framework. Personal innovativeness was strongly related to TAM variables. Perceived usefulness, perceived ease of use, attitude toward use and behavioural intention to use the technology were each significant predictors of actual GenAI use. Gender and field of study influenced adoption, with both males and students studying information systems and economics showing higher usage rates. To the best of our knowledge, this study is the first to integrate TAM with personal innovativeness and demographic factors to assess student engagement with GenAI. The findings provide a theoretical and empirical foundation for understanding student responses to new technologies in higher education. The identified gender gap and field-based differences suggest that tailored approaches are necessary to enhance student engagement with GenAI tools. Overall, the findings imply that teaching practices should include scaffolded, inclusive strategies that foster GenAI literacy, adaptability and ethical awareness. Such approaches may strengthen students’ preparedness for AI-enhanced workplaces and support higher education’s role in assuring graduate employability.
Metrics
References
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in information technology. Information Systems Research, 9(2), 204–215. https://doi.org/10.1287/isre.9.2.204
Akour, I. A., Al-Maroof, R. S., Alfaisal, R., & Salloum, S. A. (2022). A conceptual framework for determining metaverse adoption in higher institutions of gulf area: An empirical study using hybrid SEM-ANN approach. Computers and Education: Artificial Intelligence, 3, 100052. https://doi.org/10.1016/j.caeai.2022.100052
Al-Adwan, A. S. (2020). Investigating the drivers and barriers to MOOC adoption: The perspective of TAM. Education and Information Technologies, 25(6), 5771–5795. https://doi.org/10.1007/s10639-020-10250-z
Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381–15413. https://doi.org/10.1007/s10639-023-11816-3
Al-Emran, M., Granić, A., Al-Sharafi, M. A., Ameen, N., & Sarrab, M. (2021). Examining the roles of students’ beliefs and security concerns for using smartwatches in higher education. Journal of Enterprise Information Management, 34(4), 1229–1251. https://doi.org/10.1108/JEIM-02-2020-0052
Al Zaidy, A. (2024). The impact of generative ai on student engagement and ethics in higher education. Journal of Information Technology, Cybersecurity, and Artificial Intelligence, 1(1), 30–38. https://doi.org/10.70715/jitcai.2024.v1.i1.004
Ali, A. H., Alajanbi, M., Yaseen, M. G., & Abed, S. A. (2023). ChatGPT4, DALL· E, Bard, Claude, BERT: Open possibilities. Babylonian Journal of Machine Learning, 17–18. https://doi.org/10.58496/BJML/2023/003
Ali, J. K. M., Shamsan, M. A. A., Hezam, T. A., & Mohammed, A. A. (2023). Impact of ChatGPT on learning motivation: Teachers and students’ voices. Journal of English Studies in Arabia Felix, 2(1), 41–49. https://doi.org/10.56540/jesaf.v2i1.51
Alhammadi, Y., & Alhazmi, A. K. (2025). Towards effective AI adoption in higher education: A comprehensive conceptual model. Journal of Science and Technology, 30(3). https://doi.org/10.20428/jst.v30i3.2758
Alotaibi, N. S. (2024). The impact of AI and LMS integration on the future of higher education: Opportunities, challenges, and strategies for transformation. Sustainability, 16(23), 10357. https://doi.org/10.3390/su162310357
Alqahtani, N., & Wafula, Z. (2025). Artificial intelligence integration: Pedagogical strategies and policies at leading universities. Innovative Higher Education, 50(2), 665–684. https://doi.org/10.1007/s10755-024-09749-x
Al-Qaysi, N., Granić, A., Al-Emran, M., Ramayah, T., Garces, E., & Daim, T. U. (2023). Social media adoption in education: A systematic review of disciplines, applications, and influential factors. Technology in Society, 102249. https://doi.org/10.1016/j.techsoc.2023.102249
Al-Rahmi, A. M., Al-Rahmi, W. M., Alturki, U., Aldraiweesh, A., Almutairy, S., & Al-Adwan, A. S. (2022). Acceptance of mobile technologies and m-learning by university students: An empirical investigation in higher education. Education and Information Technologies, pp. 27, 7805–7826. https://doi.org/10.1007/s10639-022-10934-8
Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use e-learning systems. IEEE Access, 7, 26797–26809. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8643360
Alshammari, S. H., & Rosli, M. S. (2020). A review of technology acceptance models and theories. Innovative Teaching and Learning Journal, 4(2), 12–22. https://itlj.utm.my/index.php/itlj/article/view/51
Arpaci, I., Durdu, P. O., & Mutlu, A. (2019). The role of self-efficacy and perceived enjoyment in predicting computer engineering students’ continuous use intention of Scratch. International Journal of E-Adoption, 11(2), 1–12. https://doi.org/10.4018/IJEA.2019070101
Babashahi, L., Barbosa, C. E., Lima, Y., Lyra, A., Salazar, H., Argôlo, M., de Almeida, M. A., & Souza, J. M. D. (2024). AI in the workplace: A systematic review of skill transformation in the industry. Administrative Sciences, 14(6), 127. https://doi.org/10.3390/admsci14060127
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. http://dx.doi.org/10.2139/ssrn.4337484
Barakat, M., Salim, N. A., & Sallam, M. (2024). Perspectives of university educators regarding ChatGPT: A validation study based on the technology acceptance model. Preprint available at Research Square. https://doi.org/10.21203/rs.3.rs-3919524/v1
Barrientos, A., Del Mundo, M. A., Inoferio, H. V., Adjid, M. A., Hajan, H. B., Ullong, M. M., Alih, D. R. S., Abdulmajid, B. P., & Espartero, M. M. (2024). Discourse analysis on academic integrity generative AI: Perspectives from science and mathematics students in higher education. Environment & Social Psychology, 9(9). https://doi.org/10.59429/esp.v9i9.2927
Baytak, A. (2023). The acceptance and diffusion of generative artificial intelligence in education: A literature review. Current Perspectives in Educational Research, 6(1), 7–18. https://doi.org/10.46303/cuper.2023.2
Benriyene, S., Ismaili, B. A., & Bakkali, S. (2024, October). Artificial Intelligence and Employability: A Literature Review of Engineer’s Competencies. In International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 215-224). Cham: Springer Nature Switzerland.
Bera, S., Pillai, S., & Pathak, D. P. (2024, June 2). Navigating the AI Impact: Job Markets and Evolving Skillsets. [Paper presentation]. In Proceedings of the International Conference on Innovative Computing & Communication (ICICC), New Delhi, India. https://dx.doi.org/10.2139/ssrn.4851647
Biggs, J. (1999). What the student does: Teaching for enhanced learning. Higher Education Research & Development, 18(1), 57–75. https://doi.org/10.1080/0729436990180105
Borji, A. (2022). Generated faces in the wild: Quantitative comparison of Stable Diffusion, Midjourney and DALL-E 2. arXiv preprint. http://arxiv.org/abs/2210.00586
Bozkurt, A., Junhong, X., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., & RomeroHall, E. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. https://doi.org/10.5281/zenodo.7636568
Brislin, R. W. (1980). Translation and content analysis of oral and written materials. In H. C. Triandis & J. W. Berry (Eds.), Handbook of cross-cultural psychology: Vol. 2. Methodology (pp. 137–164). Allyn and Bacon.
Buck, A. (2024). Transdisciplinary employability practitioners: Engaging with skills for the future and redefining professional identity. Journal of Teaching and Learning for Graduate Employability, 15(2), 41–44. https://doi.org/10.21153/jtlge2024vol15no2art2017
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(43). https://doi.org/10.1186/s41239-023-00411-8
Chaurasia, D., & Veeriah, P. (2023). Skill development opportunities and its influence on employability of students. Indian Journal of Vocational Education, 35(1), 1–15. https://ejournals.ncert.gov.in/index.php/ijve/article/view/4628
Chen, K., Tallant, A. C. and Selig, I. (2025). Exploring generative AI literacy in higher education: Student adoption, interaction, evaluation and ethical perceptions. Information and Learning Sciences, 126(1/2), 132–148. https://doi.org/10.1108/ILS-10-2023-0160
Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis. Journal of Medical Internet Research, 25, e47184. https://doi.org/10.2196/47184
Chowdhury, R. R., & Singha, A. K. (2023). Importance of integration modern technology in higher education. Knowledgeable Research: A Multidisciplinary Journal, 1(09), 71–82. https://doi.org/10.57067/kr.v1i09.78
Chu, H. C., Chen, J. M., Kuo, F. R., & Yang, S. M. (2021). Development of an adaptive game-based diagnostic and remedial learning system based on the concept-effect model for improving learning achievements in mathematics. Educational Technology & Society, 24(4), 36–53. https://www.jstor.org/stable/10.2307/48629243
Crawford, J., Allen, K. A., Pani, B., & Cowling, M. (2024). When artificial intelligence substitutes humans in higher education: The cost of loneliness, student success, and retention. Studies in Higher Education, 49(5), 883–897. https://doi.org/10.1080/03075079.2024.2326956
Cynthia, S. T., & Roy, B. (2025). An empirical study on the impact of gender diversity on code quality in AI systems. arXiv preprint arXiv:2505.03082. https://doi.org/10.48550/arXiv.2505.03082
Daher, W., & Hussein, A. (2024). Higher education students’ perceptions of GenAI tools for learning. Information, 15(7), 416. https://doi.org/10.3390/info15070416
Dajani, D., & Abu Hegleh, A. S. (2019). Behavior intention of animation usage among university students. Heliyon, 5(10), e02536. https://doi.org/10.1016/j.heliyon.2019.e02536
Damaševičius, R. (2024). Commentary on artificial intelligence and graduate employability: What should we teach Generation AI? Journal of Applied Learning and Teaching, 7(2). https://doi.org/10.37074/jalt.2024.7.2.39
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. [Doctoral thesis]. Sloan School of Management, Massachusetts Institute of Technology. http://hdl.handle.net/1721.1/15192
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dehouche, N. (2021). Plagiarism in the age of massive generative pre-trained transformers (GPT-3). Ethics in Science and Environmental Politics, 21, 17–23. https://doi.org/10.3354/esep00195
Dowling‐Hetherington, L., Glowatz, M., McDonald, E., & Dempsey, A. (2020). Business students’ experiences of technology tools and applications in higher education. International Journal of Training and Development, 24(1), 22–39. https://doi.org/10.1111/ijtd.12168
Draxler, F., Buschek, D., Tavast, M., Hämäläinen, P., Schmidt, A., Kulshrestha, J., & Welsch, R. (2023). Gender, age, and technology education influence the adoption and appropriation of LLMs. arXiv preprint. arXiv:2310.06556. https://doi.org/10.48550/arXiv.2310.06556
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Carter, L.… & Wright, R. (2023). “So, what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Dwyer, N., Dempsey, N., Brown, D., & Watt, A. (2025). Employer perspectives on the importance of help-seeking as a key skill of higher education graduates. Journal of Teaching and Learning for Graduate Employability, 16(1), 1–17. https://doi.org/10.21153/jtlge2025vol16no1art2015
Ejjami, R. (2024). AI’s impact on vocational training and employability: Innovation, challenges, and perspectives. International Journal for Multidisciplinary Research, 6(4), 24967. https://doi.org/gt43r9
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020). Investigating the impacting factors for healthcare professionals to adopt artificial intelligence-based medical diagnosis support systems (AIMDSS). Annals of Operations Research, 294(1), 567–592. https://doi.org/10.1007/s10479-018-2818-y
Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies. Interactive Technology and Smart Education, 14(4), 329–348. https://doi.org/10.1108/ITSE-06-2016-0015
Fasbender, U. (2022). Age and technology use: A dual pathway model of motivation and capability. In Academy of Management Proceedings (Vol. 2022, No. 1). Academy of Management. https://doi.org/10.5465/AMBPP.2022.10532abstract
Flensburg, S., & Lomborg, S. (2023). Datafication research: Mapping the field for a future agenda. New Media & Society, 25(6), 1451–1469. https://doi.org/10.1177/146144482110466
García-Peñalvo, F. J., & Vázquez-Ingelmo, A. (2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in Generative AI. International Journal of Interactive Multimedia and Artificial Intelligence, 8(4), 7-16. http://dx.doi.org/10.9781/ijimai.2023.07.006
Gonsalves, C., & Acar, O. A. (2025). Identifying discourses of generative AI in higher education. In K. Pulk & R. Koris (Eds.), Generative AI in Higher Education (pp. 28-44). Edward Elgar Publishing. https://doi.org/10.4337/9781035326020.00012
Granić, A. (2023). Technology acceptance and adoption in education. In O. Zawacki-Richter & I. Jung (Eds.), Handbook of open, distance and digital education (pp. 183-197). Springer, Singapore. https://doi.org/10.1007/978-981-19-2080-6_11
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Greenwood, P. M., & Baldwin, C. L. (2022). Preferred sources of information, knowledge, and acceptance of automated vehicle systems: Effects of gender and age. Frontiers in Psychology, 13, 806552. https://doi.org/10.3389/fpsyg.2022.806552
Greenwood, S. (2025). Using student conferences as a form of authentic assessment to develop and enhance transferable communication skills in quantitative and data-driven disciplines: Evidence from a postgraduate public health program. Journal of Teaching and Learning for Graduate Employability, 16(1), 18–38. http://dx.doi.org/10.21153/jtlge2025vol16no1art2100
Harris-Reeves, B. E., Pearson, A. G., Vanderlelie, J. J., & Massa, H. M. (2023). The value of employability-focused assessment: Student perceptions of career readiness. Journal of Teaching and Learning for Graduate Employability, 14(1), 186–204. https://doi.org/10.21153/jtlge2024vol15no1art1903
Haverila, M., & Barkhi, R. (2009). The influence of experience, ability, and interest on e-learning effectiveness. The European Journal of Open, Distance and E-Learning, 1. https://eric.ed.gov/?id=EJ911761
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huang, X., Zou, D., Cheng, G., Chen, X., & Xie, H. (2023). Trends, research issues and applications of artificial intelligence in language education. Educational Technology & Society, 26(1), 112–131. http://index.j-ets.net/Published/26_1/ETS_26_1_09.pdf
Hupfer, S. (2020, March 3rd). Talent and workforce effects in the age of AI: Insights from Deloitte’s State of AI in the Enterprise, 2nd Edition survey. Deloitte Insights. https://tinyurl.com/473352we
Hwang, G. J., & Chen, N. S. (2023). Editorial position paper: Exploring the potential of generative artificial intelligence in education: Applications, challenges, and future research directions. Educational Technology & Society, 26(2), 18. http://index.j-ets.net/Published/26_1/ETS_26_1_09.pdf
Idrisov, B., & Schlippe, T. (2024). Program code generation with generative AIs. Algorithms, 17(2), 62. https://doi.org/10.3390/a17020062
Ismail, A., Aliu, A., Ibrahim, M., & Sulaiman, A. (2024). Preparing teachers of the future in the era of artificial intelligence. Journal of Artificial Intelligence, Machine Learning and Neural Network, 4(04), 31–41. https://doi.org/10.55529/jaimlnn.44.31.41
Jacques, P. H., Moss, H. K., & Garger, J. (2024). A synthesis of AI in higher education: Shaping the future. Journal of Behavioral and Applied Management, 24(2), 103–111. https://doi.org/10.21818/001c.122146
Jacoby, D., Savage, S., & Coady, Y. (2024, May). Remote possibilities: Where there is a WIL, is there a way? AI education for remote learners in a new era of work-integrated-learning. In Proceedings of the AAAI Symposium Series (Vol. 3, No. 1, pp. 478-485). https://doi.org/10.1609/aaaiss.v3i1.31261
Jarke, J., & Breiter, A. (2019). Editorial: The datafication of education. Learning, Media and Technology, 44(1), 1–6. https://doi.org/10.1080/17439884.2019.1573833
Jha, M., Jha, S., Thakur, S., & Xu, J. (2022, December 18-20). Student engagement and learning through digital educational technology. In 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), (pp. 1-6). Gold Coast, Australia. https://ieeexplore.ieee.org/document/10089226
Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348. https://doi.org/10.1016/j.caeai.2024.100348
Karaca, O., Çalışkan, S. A., & Demir, K. (2021). Medical artificial intelligence readiness scale for medical students (MAIRS-MS) – development, validity and reliability study. BMC Medical Education, 21(1). https://doi.org/10.1186/s12909-021-02546-6
Khong, H., Celik, I., Le, T. T., Lai, V. T. T., Nguyen, A., & Bui, H. (2023). Examining teachers’ behavioural intention for online teaching after the COVID-19 pandemic: A large-scale survey. Education and Information Technologies, 28(5), 5999–6026. https://doi.org/10.1007/s10639-022-11417-6
Kim, J., Klopfer, M., Grohs, J. R., Eldardiry, H., Weichert, J., Cox II, L. A., & Pike, D. (2025). Examining faculty and student perceptions of generative AI in university courses. Innovative Higher Education, 50, 1–33. http://dx.doi.org/10.1007/s10755-024-09774-w
Korir, M., Slade, S., Holmes, W., Héliot, Y., & Rienties, B. (2023). Investigating the dimensions of students’ privacy concerns in the collection, use and sharing of data for learning analytics. Computers in Human Behavior Reports, 9, 100262. https://doi.org/10.1016/j.chbr.2022.100262
Kourtesis, P., Whear, S. A., Wilson, G., & Parra, M. A. (2022). Factors influencing acceptance of technology across age: Amid the COVID‐19 pandemic. Alzheimer’s & Dementia, 17(Suppl 7). https://doi.org/10.1002/alz.055102
Krause, S., Dalvi, A., & Zaidi, S. K. (2025). Generative AI in education: Student skills and lecturer roles. arXiv preprint arXiv:2504.19673. https://doi.org/10.48550/arXiv.2504.19673
Krusberg, Z. (2025). Where’s the line? A classroom activity on ethical and constructive use of generative AI in physics. arXiv preprint arXiv:2506.00229. https://doi.org/10.48550/arXiv.2506.00229
Lan, Y.-J., & Chen, N.-S. (2024). Teachers’ agency in the era of LLM and generative AI: Designing pedagogical AI agents. Educational Technology & Society, 27(1), I-XVIII. http://index.j-ets.net/Published/27_1/ETS_27_1_PP01.pdf
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
Leão, C.P., & Ferreira, A .C. (2021). Engineering student attitude towards new technologies employed in active teaching. In: M. E. Auer, T. Rüütmann (Eds.), Educating engineers for future industrial revolutions. ICL 2020. Advances in Intelligent Systems and Computing, vol 1329. Springer, Cham. https://doi.org/10.1007/978-3-030-68201-9_63
Lock, S. (2022, December 5). What is the AI chatbot phenomenon ChatGPT, and could it replace humans? The Guardian. https://tinyurl.com/32bjxe24
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu, USA. https://doi.org/10.1145/3313831.3376727
Loosen, W. (2018). Four forms of datafied journalism: Journalism’s response to the datafication of society (Communicative Figurations Working Paper No. 18). University of Bremen, Centre for Media, Communication and Information Research (ZeMKI). https://zemki.uni-bremen.de/en/publikation/working-papers-no-18/
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057–7077. https://doi.org/10.1007/s10639-021-10557-5
Marcus, G., Davis, E., & Aaronson, S. (2022). A very preliminary analysis of DALL-E 2. arXiv preprint arXiv:2204.13807. https://doi.org/10.48550/arXiv.2204.13807
Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., de Laat, M., Buckingham Shum, S., Gašević, D., & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers & Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100056
Mei, B., Brown, G. T., & Teo, T. (2018). Toward an understanding of preservice English as a foreign language teachers’ acceptance of computer-assisted language learning 2.0 in the People’s Republic of China. Journal of Educational Computing Research, 56(1), 74–104. https://doi.org/10.1177/0735633117700144
Mohamed, M. S. P. (2024). Exploring ethical dimensions of AI-enhanced language education: A literature perspective. Technology in Language Teaching & Learning, 6(3), 1813–1813. https://doi.org/10.29140/tltl.v6n3.1813
Naveh, G., & Shelef, A. (2021). Analyzing attitudes of students toward the use of technology for learning: Simplicity is the key to successful implementation in higher education. International Journal of Educational Management, 35(2), 382–393. https://doi.org/10.1108/IJEM-04-2020-0204
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
O’Connor, M. (2024). Reconceptualising and supporting graduate employability practitioners for higher degree research candidates. Journal of Teaching and Learning for Graduate Employability, 15(2), 76–79. http://dx.doi.org/10.21153/jtlge2024vol15no2art2043
Otermans, P. C., Aditya, D., & Pereira, M. (2023). A study exploring soft skills in higher education. Journal of Teaching and Learning for Graduate Employability, 14(1), 136–153. https://doi.org/10.21153/jtlge2023vol14no1art1665
Padalia, A., Jamilah, A., Syakhruni, S., Rini, Y. S., & Idkhan, A. M. (2023). E-learning application usage in higher education with technology acceptance model (TAM) for students’ assessment. International Journal on Advanced Science, Engineering and Information Technology, 13(3), 1059–1067. https://doi.org/10.18517/ijaseit.13.3.18691
Pandya, S. S., & Wang, J. (2024). Artificial intelligence in career development: A scoping review. Human Resource Development International, 27(3), 324–344. https://doi.org/10.1080/13678868.2024.2336881
Petropoulos, G. (2018). The impact of artificial intelligence on employment. In J. O’Reilly, F. Ranft, & M. Neufeind (Eds.), Work in the digital age: Challenges of the fourth industrial revolution (pp. 119-132). Rowman & Littlefield International. https://tinyurl.com/nwxbehrp
Raffaghelli, J. E., Manca, S., Stewart, B., Prinsloo, P., & Sangrà, A. (2020). Supporting the development of critical data literacies in higher education: Building blocks for fair data cultures in society. International Journal of Educational Technology in Higher Education, 17(58). https://doi.org/10.1186/s41239-020-00235-w
Ramírez-Montoya, M.S., Oliva-Córdova, L. M., & Patiño, A. (2023). Training teaching personnel in incorporating generative artificial intelligence in higher education: A complex thinking approach. In Proceedings of the 11th International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2023). Braganca, Portugal. https://doi.org/10.1007/978-981-97-1814-6_16
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., ... & Aristovnik, A. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. PloS One, 20(2), e0315011. https://doi.org/10.1371/journal.pone.0315011
Rispler, C., & Luria, G. (2020). Employee perseverance in a “no phone use while driving” organizational road-safety intervention. Accident Analysis and Prevention, 144, 105689. https://doi.org/10.1016/j.aap.2020.105689
Rogers, E. M. (1983). Diffusion of innovations. University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship. https://ssrn.com/abstract=1496176
Rola‐Rubzen, M. F., Paris, T., Hawkins, J., & Sapkota, B. (2020). Improving gender participation in agricultural technology adoption in Asia: From rhetoric to practical action. Applied Economic Perspectives and Policy, 42(1), 113–125. https://doi.org/10.1002/aepp.13011
Russo, D. (2024). Navigating the complexity of generative AI adoption in software engineering. ACM Transactions on Software Engineering and Methodology, 5(33), 1–50. https://doi.org/10.1145/3652154
Saidakhror, G. (2024). The impact of artificial intelligence on higher education and the economics of information technology. International Journal of Law and Policy, 2(3), 1–6. https://doi.org/10.59022/IJLP.125
Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating technology acceptance model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
Saleh, S. S., Nat, M., & Aqel, M. (2022). Sustainable adoption of e-learning from the TAM perspective. Sustainability, 14(6), 3690. https://doi.org/10.3390/su14063690.
Salloum, S. A., Alhamad, A. Q. M., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. IEEE Access, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467
Saúde, S., Barros, J. P., & Almeida, I. (2024). Impacts of generative artificial intelligence in higher education: Research trends and students’ perceptions. Social Sciences, 13(8), 410. https://doi.org/10.3390/socsci13080410
Schreiber, J. B., Stage, F. K., King, J., Nora, A., & Barlow, E. A. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99(6), 323−337. https://doi.org/10.3200/JOER.99.6.323-338
Segbenya, M., Bervell, B., Frimpong-Manso, E., Otoo, I. C., Andzie, T. A., & Achina, S. (2023). Artificial intelligence in higher education: Modelling the antecedents of artificial intelligence usage and effects on 21st century employability skills among postgraduate students in Ghana. Computers and Education: Artificial Intelligence, 5, 100188. https://doi.org/10.1016/j.caeai.2023.100188
Selwyn, N. (2022). The future of AI and education: Some cautionary notes. European Journal of Education, 57(4), 620–631. https://doi.org/10.1111/ejed.12532
Shen, S., Xu, K., Sotiriadis, M., & Wang, Y. (2022). Exploring the factors influencing the adoption and usage of augmented reality and virtual reality applications in tourism education within the context of the COVID-19 pandemic. Journal of Hospitality, Leisure, Sport & Tourism Education, 30, 100373. https://doi.org/10.1016/j.jhlste.2022.100373
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445. http://dx.doi.org/10.1037/1082-989X.7.4.422
Sitar-Taut, D.-A., & Mican, D. (2021). Mobile learning acceptance and use in higher education during social distancing circumstances: An expansion and customization of UTAUT2. Online Information Review, 45(5), 1000–1019. https://doi.org/10.1108/OIR-01-2021-0017
Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023). Educator and student perspectives on the impact of generative AI on assessments in higher education. In Proceedings of the Tenth Association for Computing Machinery (ACM) Conference on Learning @ Scale (L@S ’23): Copenhagen, Denmark, July 20–22, 2023 (pp. 378–382). https://doi.org/10.1145/3573051.3596191
Stockless, A. (2018). Acceptance of learning management system: The case of secondary school teachers. Education and Information Technologies, 23, 1101–1121. https://doi.org/10.1007/s10639-017-9654-6
Strzelecki, A. (2024). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 32(9), 5142–5155. https://doi.org/10.1080/10494820.2023.2209881
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 1–10. https://doi.org/10.37074/jalt.2023.6.1.17
Surjadi, M. (2024, August 5). Colleges race to ready students for the AI workplace. The Wall Street Journal. https://www.wsj.com/us-news/education/colleges-race-to-ready-students-for-the-ai-workplace-cc936e5b
Szcyrek, S., Stewart, B., & Miklas, E. (2024). Educators’ understandings of digital classroom tools and datafication: Perceptions from higher education faculty. Research in Learning Technology, 32, 3040. https://doi.org/10.25304/rlt.v32.3040
Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning: A second-order meta-analysis and validation study. Review of Educational Research, 81(1), 4–28. https://doi.org/10.3102/0034654310393361
Teo, T., Zhou, M., Fan, A. C. W., and Huang, F. (2019). Factors that influence university students’ intention to use Moodle: A study in Macau. Educational Technology Research and Development, 67, 749–766. https://doi.org/10.1007/s11423-019-09650-x
Twum, K. K., Ofori, D., Keney, G., & Korang-Yeboah, B. (2022). Using the UTAUT, personal innovativeness and perceived financial cost to examine student’s intention to use e-learning. Journal of Science and Technology Policy Management, 13(3), 713–737. https://doi.org/10.1108/JSTPM-12-2020-0168
Ugwuozor, F. O., & Egenti, M. C. (2024). Artificial intelligence and the future of work: Recent graduates’ perspective. Creative Artist: A Journal of Theatre and Media Studies, 18(1), 1–19. https://www.ajol.info/index.php/cajtms/article/view/267136
Waddill, D. (2023). Applied learning strategy using technology to connect higher education students with workplace opportunities. In ICERI2023 Proceedings: 16th annual International Conference of Education, Research and Innovation, Seville, Spain, 13–15 November 2023 (pp. 7187). IATED. https://doi.org/10.21125/iceri.2023.1786
Wahab, M. H. S., Hosen, M., Islam, M. A., Chowdhury, M. A. M., Jantan, A. H., & Wahab, S. A. (2025). Graduate employability: A bibliometric analysis. Global Business and Organizational Excellence, 44(2), 38–57. https://doi.org/10.1002/joe.22267
Waisberg, E., Ong, J., Masalkhi, M., Zaman, N., Sarker, P., Lee, A. G., & Tavakkoli, A. (2024). Google’s AI chatbot “Bard”: A side-by-side comparison with ChatGPT and its utilization in ophthalmology. Eye, 38(4), 642–645. https://doi.org/10.1038/s41433-023-02760-0
Waring, M., & Evans, C. (2024). Facilitating students’ development of assessment and feedback skills through critical engagement with generative artificial intelligence. In Research Handbook on Innovations in Assessment and Feedback in Higher Education (pp. 330-354). Edward Elgar Publishing. https://doi.org/10.4337/9781800881600.00027
Wasielewski, A. (2023). “Midjourney can’t count”: Questions of representation and meaning for text-to-image generators. The Interdisciplinary Journal of Image Sciences, 37(1), 71–82. https://doi.org/10.1453/1614-0885-1-2023-15454
Wei, X., Wang, L., Lee, L. K., & Liu, R. (2025). The effects of generative AI on collaborative problem-solving and team creativity performance in digital story creation: an experimental study. International Journal of Educational Technology in Higher Education, 22(1), 23. https://doi.org/10.1186/s41239-025-00526-0
Wood, E. A., Ange, B. L., & Miller, D. D. (2021). Are we ready to integrate artificial intelligence literacy into medical school curriculum: Students and faculty survey. Journal of Medical Education and Curricular Development, 8. https://doi.org/10.1177/23821205211024078
World Economic Forum. (2025, January). The Future of Jobs Report 2025. https://www.weforum.org/publications/the-future-of-jobs-report-2025/
Wut, T. M., Chan, E. A. H., Wong, H. S. M., & Chan, J. K. (2025). Perceived artificial intelligence literacy and employability of university students. Education+ Training, 67(2), 258–274. https://doi.org/10.1108/ET-06-2024-0272
Yang, H. D., & Yoo, Y. (2004). It’s all about attitude: Revisiting the technology acceptance model. Decision Support Systems, 38(1), 19–31. https://doi.org/10.1016/S0167-9236(03)00062-9
Zouhaier, S. (2023). The impact of artificial intelligence on higher education: An empirical study. European Journal of Educational Sciences, 10(1), 17–33. https://doi.org/10.19044/ejes.v10no1a17

Downloads
Published
Issue
Section
License
Copyright (c) 2025 Clara Hope Rispler, Michal Mashiach-Eizenberg, Gila Yakov

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.