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GenAI in Indian higher education: faculty at the crossroads of adoption

Razi-ur-Rahim, Mohammad, Uddin, Furquan, Chauhan, Jahangir, Faisal, K.P.·2026·Asian Education and Development Studies
DOI 10.1108/aeds-04-2025-0194
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Abstract

Purpose: The study examines the key factors influencing faculty members' intention to adopt Generative Artificial Intelligence (GenAI) in teaching within Indian higher education institutions. It focuses on faculty readiness, digital competence, and perceived usefulness in effective GenAI integration and development of future-ready classrooms. Design/methodology/approach: Adopting a quantitative, cross-sectional design within a positivist paradigm, the study collected data from 455 faculty members using a structured and validated questionnaire. Data analysis employed IBM SPSS (Version 29.0) and IBM SPSS Amos (Version 26.0), with structural equation modeling applied to test the proposed theoretical framework. Findings: Perceived usefulness, ease of use, and output quality primarily drive faculty members' adoption of GenAI, while social influence plays a minimal role. The study confirms the applicability of the Technology Acceptance Model (TAM) in this context and underscores the importance of experiential learning and institutional support in shaping adoption behavior. Practical implications: The findings highlight the need for hands-on training, robust institutional frameworks, and inclusive policies that promote GenAI adoption in higher education. These efforts can improve instructional efficiency, reduce faculty workload, and encourage pedagogical innovation. Originality/value: The study contributes a novel, faculty-focused perspective on GenAI adoption, shifting attention from student-centric research in developed countries to educators in India. Addressing geographic and thematic gaps offers context-sensitive insights essential for sustainable and inclusive GenAI integration in resource-constrained educational environments.

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  1. Al Darayseh, A. (2023). Acceptance of arti cial intelligence in teaching science: Science teachers' perspective. Computers in Education: Arti cial Intelligence, 4, 1-20. https://doi.org/10.1016/j.caeai.2023.100132 · DOI
  2. Al-Emran, M., & Malik, S. I. (2021). Exploring the role of the Technology Acceptance Model in the adoption of arti cial intelligence in higher education. Computers & Education, 161, 104059. https://doi.org/10.1016/j.compedu.2020.104059 · DOI
  3. Alshammari, S. H., & Alkhwaldi, A. F. (2025). An integrated approach using social support theory and the technology acceptance model to investigate the sustainable use of digital learning technologies. Scienti c Reports, 15(342). https://doi.org/10.1038/s41598-024-83450-z · DOI
  4. Al-Zahrani, A. M. (2024). From traditionalism to algorithms: Embracing arti cial intelligence for effective university teaching and learning. Educational Technology at IgMin, 2(2), 102-112. https://doi.org/10.61927/igmin151 · DOI
  5. An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023). Modeling English teachers' behavioral intention to use arti cial intelligence in middle schools. Education and Information Technologies, 28(5), 5187-5208. https://doi.org/10.1007/s10639-022-11286-z . · DOI
  6. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411. https://doi.org/10.1037/0033-2909.103.3.411 · DOI · Paper page
  7. Baker, R. S., & Siemens, G. (2023). Arti cial intelligence in education: Adoption, acceptance, and impact of emerging technologies. Journal of Educational Data Mining, 15(1), 45-63. https://www.jedm.education/journal/2023/03/arti cial-intelligence-in-education
  8. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Arti cial intelligence in FinTech: Understanding robo- advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411-1430. https://doi.org/10.1108/IMDS-06-2018-0251 · DOI
  9. Blut, M., Chong, A., Tsiga, Z., & Venkatesh, V. (2022). Meta-analysis of the Uni ed Theory of Acceptance and Use of Technology (UTAUT): Challenging its validity and charting a research agenda in the red ocean. Journal of the Association for Information Systems. https://doi.org/10.17705/1jais.00700 · DOI
  10. Brynjolfsson, E., & McElheran, K. (2023). The impact of AI on education and business: Recent advancements and future trends. Harvard Business Review, 101(3), 52-64. https://hbr.org/2023/03/the-impact-of-ai-on-education-and-business 12. Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M. I., & Rivas-Manzano, M. d. R. d. (2024). Acceptance of educational arti cial intelligence by teachers and its relationship with some variables and pedagogical beliefs. Education Science, 14, 740. https://doi.org/10.3390/educsci14070740 · DOI
  11. Carvalho, L., Martinez-Maldonado, R., Tsai, Y.-S., Markauskaite, L., & De Laat, M. (2022). How can we design for learning in an AI world? Computers and Education: Arti cial Intelligence, 3, 100053. https://doi.org/10.1016/j.caeai.2022.100053 · DOI · Paper page
  12. Chen, C., & Huang, X. (2022). Current trends and gaps in research on teachers' AI competence and its impact on classroom practice. Educational Technology Research and Development, 70(4), 1557- 1573. https://doi.org/10.1007/s11423-022-10123-7 · DOI
  13. Chen, X., & Zhang, L. (2023). Revolutionizing content delivery: AI-driven systems for adapting to students' learning styles and gaps. Educational Technology Research and Development, 71(2), 275- 295. https://doi.org/10.1007/s11423-023-10304-3 · DOI
  14. Cheng, Y. S., & Chen, C. H. (2022). Understanding the acceptance of arti cial intelligence in education: Applying the Technology Acceptance Model (TAM). Journal of Educational Technology & Society, 25(3), 76-89. https://www.jstor.org/stable/26865216
  15. Cox, J., & McCarthy, M. (2022). AI-powered professional development: Enhancing teaching skills through personalized feedback and coaching tools. Journal of Educational Technology Systems, 51(3), 353-372. https://doi.org/10.1177/00472395221108097 · DOI
  16. Crawford, W., & Lamarre Jean, E. (2021). Structural equation modelling. Oxford Research Encyclopedia of Business and Management. https://doi.org/10.1093/acrefore/9780190224851.001.0001/acrefore-9780190224851-e-232 · DOI
  17. Delcker, J., Heil, J., Ifenthaler, D., Seufert, S., & Spirgi, L. (2024). First-year students' AI-competence as a predictor for intended and de facto use of AI tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education, 21, 18. https://doi.org/10.1186/s41239-024-00452-7 · DOI
  18. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748 · DOI · Paper page
  19. Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2021). The role of teacher competence in effective deployment of AI tools in instructional design. Journal of Educational Technology, 17(2), 103-120. https://doi.org/10.1007/s11423-021-09989-5 · DOI
  20. Fachrurrozie, F., Nurkhin, A., Santoso, J. T. B., Mukhibad, H., & Wolor, C. W. (2025). Exploring the use of arti cial intelligence in Indonesian accounting classes. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2024.2448053 · DOI
  21. Fishbein, M., & Ajzen, J. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley Publication Company.
  22. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
  23. Ge, Z., & Hu, Y. (2020). Innovative application of arti cial intelligence (AI) in the management of higher education and teaching. In 2020 International Conference on Arti cial Intelligence and Information Technology, ICAIIT 2020. Institute of Physics Publishing. https://doi.org/10.1088/1742- 6596/1533/3/032089 · DOI
  24. González-Mayorga, H., Rodríguez-Esteban, A., & Vidal, J. (2024). The use of OpenAI's GPT model for the analysis of open texts in educational research. Pixel-Bit Journal of Media Education, 69, 227- 253.
  25. Hutcheson, G. D., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. SAGE Publications Ltd.
  26. Hwang, G. J., & Chang, C. Y. (2023). Emerging trends and future directions of arti cial intelligence in education: Enhancing personalization and engagement. Educational Technology Research and Development, 71(3), 445-463. https://doi.org/10.1007/s11423-023-10302-5 · DOI
  27. Ifenthaler, D., & Seufert, S. (Eds.). (2022). Arti cial intelligence education in the context of work. Springer. https://doi.org/10.1007/978-3-031-14489-9 · DOI
  28. Jolliffe, I. T. (2003). Principal component analysis. Technometrics, 45(3), 276.
  29. Karaca, A., & Kilcan, B. (2023). The adventure of arti cial intelligence technology in education: Comprehensive scienti c mapping analysis. Participatory Educational Research, 10(4), 144-165. https://doi.org/10.17275/per.23.64.10.4 · DOI
  30. Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of arti cial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tele.2022.101925 · DOI
  31. Kerr, B., & Marsh, D. (2023). Personalized learning through AI algorithms: Adapting coursework to individual learning patterns. Computers & Education, 195, 104320. https://doi.org/10.1016/j.compedu.2023.104320 · DOI
  32. Kim, J., & Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. Journal of Interactive Marketing, 22(2), 45-59. https://doi.org/10.1002/dir.20115 · DOI
  33. Kong, S.-C., & Yang, Y. (2024). A human-centered learning and teaching framework using generative arti cial intelligence for self-regulated learning development through domain knowledge learning in K-12 settings. IEEE Transactions on Learning Technologies, 17, 1562-1573. https://doi.org/10.1109/TLT.2024.3392830 · DOI
  34. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Arti cial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Arti cial Intelligence, 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101 · DOI · Paper page
  35. Lee, W., Lin, C., & Shih, K. (2018). A technology acceptance model for the perception of restaurant service robots for trust, interactivity, and output quality. International Journal of Mobile Communications, 16, 361-376.
  36. Legris, T., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.
  37. Li, C., & Wang, H. (2021). Investigating the adoption of AI in higher education: A technology acceptance model perspective. Computers in Human Behavior, 115, 106614. https://doi.org/10.1016/j.chb.2020.106614 · DOI
  38. Lillian-Yee-Kiaw, Lew, S. L., L., S. H., & Leow, M. C. W. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), e01788. https://doi.org/10.1016/j.heliyon.2019.e01788 · DOI
  39. Liu, G., & Ma, C. (2023). Measuring EFL learners' use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 18(2), 125-138. https://doi.org/10.1080/17501229.2023.2240316 · DOI · Paper page
  40. Ma, M. (2024). Exploring the acceptance of generative arti cial intelligence for language learning among EFL postgraduate students: An extended TAM approach. International Journal of Applied Linguistics, 1-18. https://doi.org/10.1111/ijal.12603 · DOI
  41. Memon, M. A., Ting, H., Cheah, J. H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), 1-20. https://jasemjournal.com/wp-content/uploads/2020/08/Memon-et-al_JASEM_- Editorial_V4_Iss2_June2020.pdf
  42. Mole , R. R., & Ayanwale, M. A. (2023). Using composite structural equation modeling to examine high school teachers' acceptance of E-learning after COVID-19. New Trends in Issues Proceedings on Humanities and Social Sciences, 10(1), 01-11. https://doi.org/10.18844/prosoc.v10i1.8837 · DOI
  43. Müller, W., & Leyer, M. (2023). Understanding intention and use of digital elements in higher education teaching. Education and Information Technologies, 28(12), 15571-15597. https://doi.org/10.1007/s10639-023-11798-2 · DOI
  44. Nguyen, T. H., & Nguyen, T. T. (2020). Applying the Technology Acceptance Model to predict the adoption of AI-based educational tools. Educational Technology Research and Development, 68(4), 1939-1956. https://doi.org/10.1007/s11423-020-09717-4 · DOI
  45. Nguyen, T., & Nguyen, P. (2023). Exploring the role of output quality in chatbot acceptance: Impacts on PU and PEOU. International Journal of Technology Management, 45(3), 56-72. Available on arXiv. https://arxiv.org/abs/2302.04093 · arXiv
  46. Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative arti cial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.02.005 · DOI
  47. Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531-544.
  48. Pramanik, P., & Jana, R. K. (2025). A consumer acceptance model in the arti cial intelligence era. Management Decision, 63(13). https://www.emerald.com/insight/0025-1747.htm
  49. Prather, J., Leinonen, J., Kiesler, N., Gorson Benario, J., Lau, S., MacNeil, S., Norouzi, N., Opel, S., Pettit, V., Porter, L., Reeves, B. N., Savelka, J., Smith, D. H., Strickroth, S., & Zingaro, D. (2025). Beyond the hype: A comprehensive review of current trends in generative AI research, teaching practices, and tools. In 2024 Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE, 2024) (pp. 300-338). Association for Computing Machinery. https://doi.org/10.1145/3689187.3709614 · DOI
  50. Sathyanarayana, S., & Mohanasundaram, T. (2024). Fit indices in structural equation modeling and con rmatory factor analysis: Reporting guidelines. Asian Journal of Economics, Business and Accountancy, 24, 561-577. https://doi.org/10.9734/ajeba/2024/v24i71430 · DOI
  51. Sayed Al Mnhrawi, D. N. T. A., & Alreshidi, H. A. (2022). A systemic approach for implementing AI methods in education during the COVID-19 pandemic: Higher education in Saudi Arabia. World Journal of Engineering, 20(5), 808-814. https://doi.org/10.1108/WJE-11-2021-0623 · DOI
  52. Shi, D., Lee, T., & Maydeu-Olivares, A. (2018). Understanding the model size effect on SEM t indices. Educational and Psychological Measurement, 79, 310-334. https://doi.org/10.1177/0013164418783530 · DOI
  53. Thongsri, N., Tripak, O., & Bao, Y. (2024). Do learners exhibit a willingness to use ChatGPT? An advanced two-stage SEM-neural network approach for forecasting factors in uencing ChatGPT adoption. Interactive Technology and Smart Education, ahead-of-print. https://doi.org/10.1108/ITSE- 01-2024-0001 . · DOI
  54. Van Niekerk, J., Delport, P. M. J., & Sutherland, I. (2025). Addressing the use of generative AI in academic writing. Computers and Education: Arti cial Intelligence, 8, 100342. https://doi.org/10.1016/j.caeai.2024.100342 · DOI · Paper page
  55. Velander, J., Taiye, M. A., Otero, N., & Milrad, M. (2024). Arti cial intelligence in K-12 education: Eliciting and re ecting on Swedish teachers' understanding of AI and its implications for teaching & learning. Educational Information Technology, 29(4), 4085-4105. https://doi.org/10.1007/s10639- 023-11990-4 . · DOI
  56. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal eld studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 · DOI · Paper page
  57. Wang, Y., Liu, C., & Tu, Y. (2021). Factors affecting the adoption of AI-based applications in higher education: An analysis of teachers' perspectives using structural equation modeling. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/27032860
  58. Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors in uencing university students' behavioral intention to use generative arti cial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human-Computer Interaction, 1-23. https://doi.org/10.1080/10447318.2024.2383033 · DOI
  59. Wang, T., Lund, B., Marengo, A., Marengo, A., Pagano, A., Teel, Z., & Pange, J. (2023). Exploring the potential impact of arti cial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13(11), 6716.
  60. Westland, J. C. (2019). Structural equation models (Vol. 22). Springer International Publishing.
  61. Xia, Y., & Chen, Y. (2024). Driving factors of generative AI adoption in new product development teams from a UTAUT perspective. International Journal of Human-Computer Interaction, 1-22. https://doi.org/10.1080/10447318.2024.2375686 · DOI
  62. Zawacki-Richter, O., & Latchem, C. (2023). The future of arti cial intelligence in education: Transforming teaching and learning. Educational Technology Research and Development, 71(2), 255-272. https://doi.org/10.1007/s11423-023-10315-0 · DOI
  63. Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of arti cial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7 · DOI · Paper page
  64. Zhang, Y., & Zhang, X. (2022). The impact of technology acceptance model on the adoption of AI in education: Evidence from Chinese universities. Education and Information Technologies, 27(5), 6557-6574. https://doi.org/10.1007/s10639-021-10716-w · DOI
  65. Zhu, M., & Liu, H. (2022). Enhancing the quality of higher education with arti cial intelligence: Innovations and applications. Journal of Educational Technology Research and Development, 70(4), 847-865. https://doi.org/10.1007/s11423-022-10071-4 · DOI

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Sorted by citations

Authors
Razi-ur-Rahim, M., Uddin, F., Chauhan, J., & Faisal, K. P.
Affiliations
  • Aligarh Muslim University Department of Business Administration, , ,
  • Aliah University Department of Management and Business Administration, , ,
  • Aligarh Muslim University Department of Commerce, , ,
  • Aligarh Muslim University Department of Law, , ,
ORCID
Venue
Asian Education and Development Studies
Publisher
Emerald
Published
2025-09-19
Volume
15
Issue
1
Pages
41-72
ISSN
  • 2046-3162
  • 2046-3170
DOI
10.1108/aeds-04-2025-0194
Cited by
6
References
11
Language
en

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