Faculty AI Use Generational Tech Adoption In Education

The AI Wave in Higher Education

Generative Artificial Intelligence (AI) tools, particularly ChatGPT, are rapidly becoming commonplace in higher education classrooms. This technological shift brings with it a pressing need to understand how faculty members perceive and decide to use these tools in their teaching practices. As AI continues to reshape the educational landscape, grasping the factors that drive or hinder its adoption by educators is crucial for institutions aiming to leverage its full potential while addressing potential challenges.

Understanding Faculty Perspectives on ChatGPT

A recent study delved into this very issue, aiming to shed light on the complex interplay of factors influencing faculty decisions regarding ChatGPT. The research specifically explored differences across generational cohorts, drawing on Generation Cohort Theory. Furthermore, it investigated the roles of self-efficacy—an individual's belief in their capacity to execute behaviors necessary to produce specific performance attainments—and innovativeness—an individual's willingness to embrace new ideas and technologies. These aspects were examined through the lenses of Social Cognitive Theory and Diffusions of Innovation theory, respectively.

Key Factors Influencing ChatGPT Adoption

The study highlights that the decision to integrate tools like ChatGPT is not uniform across all faculty members. It suggests that a combination of demographic characteristics, personal beliefs, and inherent traits significantly predicts how educators approach generative AI. Understanding these predictors can help institutions tailor support and training for their staff.

Study Methodology at a Glance

To gather insights, the researchers recruited 150 faculty participants from various higher education institutions located in Texas. A convenience sampling approach was utilized for this recruitment. Data collected from these participants were then analyzed to identify patterns and correlations related to ChatGPT use.

Generational Divides in AI Usage

Chi-square analysis of the collected data yielded interesting findings regarding generational differences. Faculty members belonging to Generation Y (Millennials) and Generation X were found to be more likely to use generative AI tools like ChatGPT compared to other generational cohorts (e.g., Baby Boomers).

Specifically, Generation Y faculty stood out as the group most inclined to incorporate ChatGPT into their assignments and classroom activities. This suggests that younger generations of educators might be more adaptable or see more immediate value in integrating such technologies into their pedagogical approaches.

The Role of Self-Efficacy and Innovativeness

Beyond generational factors, the study's regression analysis confirmed that personal attributes play a significant role. Self-efficacy emerged as a positive predictor of ChatGPT use in the classroom. This implies that faculty who feel more confident in their abilities to understand and utilize new technologies are more likely to adopt them.

Similarly, innovativeness was also found to be a positive predictor. Educators who are naturally more open to new ideas and experimentation are more inclined to explore and implement generative AI tools like ChatGPT in their teaching. These findings underscore the importance of fostering a supportive environment that builds confidence and encourages an innovative mindset among faculty.

Implications for Higher Education Stakeholders

The findings of this study offer valuable implications for various stakeholders in higher education, including administrators, instructional designers, and faculty development professionals.

1. Targeted Professional Development: Understanding that Gen X and Gen Y are more inclined to use AI can help in designing peer-mentoring programs. Training should also focus on building self-efficacy, especially for those less confident with new technologies.
2. Fostering an Innovative Culture: Institutions should encourage a culture that values innovation and provides safe spaces for faculty to experiment with new tools like ChatGPT without fear of failure.
3. Policy Development: Clear guidelines and ethical considerations for AI use in education are needed. These policies should be developed with faculty input and address concerns while highlighting potential benefits.
4. Resource Allocation: Providing adequate resources, including access to tools, technical support, and time for learning, is essential for successful AI adoption.

By considering these factors, higher education institutions can better navigate the integration of generative AI, ensuring it enhances teaching and learning experiences for both faculty and students.

References

• Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152 Article Google Scholar
• Aithal, P. S., & Aithal, S. (2020). Conceptual analysis on higher education strategies for various tech-generations. International Journal of Management, Technology, and Social Sciences, 5(1), 335–353. https://doi.org/10.5281/zenodo.3940590 Article Google Scholar
• AlAfnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2023). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology, 3, 60–68. https://doi.org/10.37965/jait.2023.0184 Article Google Scholar
• Aldahdouh, T. Z., Nokelainen, P., & Korhonen, V. (2020). Technology and social media usage in higher education: The influence of individual innovativeness. SAGE Open, 1(1), 1–20. https://doi.org/10.1177/2158244019899441 Article Google Scholar
• American Psychological Association. (2020). Publication manual of the American Psychological Association 2020: The official guide to APA style (7^th ed.). American Psychological Association
• Bakkabulindi, F. E. (2014). A call for return to Rogers’ innovation diffusion theory. East African School of Higher Education Studies & Development, 6(1), 55–85. https://doi.org/10.4314/majohe.v6il.4 Article Google Scholar
• Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman and Company. Google Scholar
• Bilali, O. (2015). Teachers’ sense of efficacy scale: The study of validity and reliability. European Academic Research, 2(12), 15176–15181. Google Scholar
• Bin-Hady, W. R., Ali, J. K., & Al-humari, M. A. (2024). The effect of ChatGPT on EFL students’ social and emotional learning. Journal of Research in Innovative Teaching & Learning, 17(2), 243–255. https://doi.org/10.1108/JRIT-02-2024-0036 Article Google Scholar
• Bolívar-Cruz, A., & Verano-Tacoronte, D. (2024). Is anxiety affecting the adoption of ChatGPT in university teaching? A gender perspective. Technology, Knowledge and Learning, 29, 803–826. https://doi.org/10.1007/s10758-025-09830-0 Article Google Scholar
• Boluk, K. A., & Miller, M. C. (2021). Intergenerational mentorship in pedagogical design and delivery. SCHOLE: A Journal of Leisure Studies and Recreation Education, 36(1), 26–41. https://doi.org/10.1080/1937156X.2020.1760746 Article Google Scholar
• Bouwma-Gearhart, J., Lenhart, C., Carter, R., Mundorff, K., Cho, H., & Knoch, J. (2021). Inclusively recognizing faculty innovation and entrepreneurship impact within promotion and tenure considerations. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 182. https://doi.org/10.3390/joitmc7030182 Article Google Scholar
• Buric, I., Sliskovic, A., & Soric, I. (2020). Teachers’ emotions and self-efficacy: A test of reciprocal relations. Frontiers in Psychology, 11, 1650. https://doi.org/10.3389/fpsyg.2020.01650 Article Google Scholar
• Cai, Q., Lin, Y., & Yu, Z. (2024). Factors influencing learner attitudes towards ChatGPT-assisted language learning in higher education. International Journal of Human-Computer Interaction, 40(22), 7112–7126. Article Google Scholar
• Çakıroğlu, Ünal, Saylan, E., Çevik, İsak, Mollamehmetoğlu, M. Z., & Timuçin, E. (2022). Faculty adoption of online teaching during the Covid-19 pandemic: A lens of diffusion of innovation theory. Australasian Journal of Educational Technology, 38(3), 87–103. https://doi.org/10.14742/ajet.7307
• Center for Generational Kinetics. (2023). Generation breakdown: Info about all of the generations. https://genhq.com/the-generations-hub/generational-faqs/
• Chaipidech, P., Srisawasdi, N., Kajornmanee, T., & Chaipah, K. (2022). A personalized learning system-supported professional training model for teachers’ TPACK development. Computers and Education: Artificial Intelligence, 3(1), 100064. https://doi.org/10.1016/j.caeai.2022.100064 Article Google Scholar
• Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial gener184ation teachers? Smart Learning Environments. https://doi.org/10.1186/s40561-023-00269-3 Article Google Scholar
• Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3^rd ed.). Routledge. https://doi.org/10.4324/9780203774441
• Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 Article Google Scholar
• Dale, V. H., & McEwan, M. P. (2021). Early adopters versus majority: Characteristics and implications for academic development and institutional change. Journal of Perspectives in Applied Academic Practice, 9(2), 54–67. Article Google Scholar
• Dhamija, A., & Dhamija, D. (2025). Understanding teachers’ perspectives on ChatGPT-generated assignments in higher education. Journal of Interdisciplinary Studies in Education, 14(1), 38–62. Article Google Scholar
• Duffin, L. C., French, B. F., & Patrick, H. (2012). The teachers’ sense of efficacy scale: Confirming the factor structure with beginning pre-service teachers. Teaching and Teacher Education, 28(6), 827–834. https://doi.org/10.1016/j.tate.2012.03.004 Article Google Scholar
• Farrokhi, F., & Mahmoudi-Hamidabad, A. (2012). Rethinking convenience sampling: Defining quality criteria. Theory and Practice in Language Studies, 2(4), 784–792. https://doi.org/10.4304/tpsl.2.4.784-792 Article Google Scholar
• Fodor, Z. A. (2018). The knowledge and perceptions of generational cohorts of K-5 elementary educators who use technology for teaching and learning. All Theses and Dissertations, 175. https://dune.une.edu/theses/175
• Foroughi, B., Iranmanesh, M., Ghobakhloo, M., Senali, M. G., Annamalai, N., Naghmeh-Abbaspour, B., & Rejeb, A. (2025). Determinants of ChatGPT adoption among students in higher education: The moderating effect of trust. The Electronic Library, 43(1), 1–21. Article Google Scholar
• Frei-Landau, R., Muchnik-Rozanov, Y., & Avidov-Ungar, O. (2022). Using Rogers’ diffusion of innovation theory to conceptualize the mobile-learning adoption process in teacher education in the COVID-19 era. Education and Information Technologies, 27(1), 12811–12838. https://doi.org/10.1007/s10639-022-11148-8 Article Google Scholar
• Fried, I. (2023). The technology behind ChatGPT is evolving insanely fast. Axious. https://www.axios.com/2023/11/28/chat-gpt-one-year-anniversary-ai
• Garg, A., & Rajendran, R. (2024). Analyzing the role of generative AI in fostering self-directed learning through structured prompt engineering. In Sifarelas A., Lin, F. (Eds.), Generative Intelligence and Intelligent Tutoring Sytems. ITS 2024. Lecture Notes in Computer Science, 14798. https://doi.org/10.1007/978-3-031-63028-6_18
• Gokcearslan, S., Durak, H. Y., & Uslu, N. A. (2024). Acceptance of educational use of the Internet of Things (IoT) in the context of individual innovativeness and ICT competency of pre-service teachers. Interactive Learning Environments, 32(2), 557–571. https://doi.org/10.1080/10494820.2022.2091612 Article Google Scholar
• Gupta, R., Seetharaman, A., & Maddulety, K. (2020). Critical success factors influencing the adoption of digitalization for teaching and learning by business schools. Education and Information Technologies, 25(1), 3481–3502. https://doi.org/10.1007/s10639-020-10246-9 Article Google Scholar
• Gupta, V., & Nyamapfene, A. (2025). Generative AI in universities: Practices at UCL and other institutions, and the path forward. Internet Reference Services Quarterly, 29(1), 131–151. https://doi.org/10.1080/10875301.2025.2453461 Article Google Scholar
• Hair, J. F., Tatham, R. L., & Anderson, R. E. (1998). Multivariate data analysis. Prentice Hall. Google Scholar
• Heydari, N., Rakhshan, M., Torabizadeh, C., & Salimi, G. (2023). Psychometric properties of Persian version of individual innovativeness scale among nursing students: A cross-sectional study. BMC Medical Education, 23(1), 663. https://doi.org/10.1186/s12909-023-04641-2 Article Google Scholar
• Hin Hong, W. C. (2023). The impact of ChatGPT on foreign language teaching and learning: Opportunities in education and research. Journal of Educational Technology and Innovation, 1, 37–45. https://doi.org/10.61414/jeti.v5i1.103 Article Google Scholar
• Hurt, H. T., Joseph, K., & Cook, C. D. (1977). Scales for the measurement of innovativeness. Human Communication Research, 4(1), 58–65. https://doi.org/10.1111/j.1468-2958.1977.tb00597.x Article Google Scholar
• Ithaka S+R. (2024). How are faculty using generative AI in the classroom? Findings from a national survey. Ithaka S+R. https://sr.ithaka.org/publications/us-instructor-survey-2024/
• Khlaif, Z. N., Mousa, A., & Sanmugam, M. (2024). Immersive extended reality (XR) technology in engineering education: Opportunities and challenges. Technology, Knowledge and Learning, 29, 803–826. https://doi.org/10.1007/s10758-023-09719-w Article Google Scholar
• Kirkyakova, G., & Angelova, N. (2023). ChatGPT – A challenging tool for the university professors in their teaching practice. Education Sciences, 13(10), 1056. https://doi.org/10.3390/educsci13101056 Article Google Scholar
• Klassen, R. M., & Tze, V. M. (2014). Teachers’ self-efficacy, personality, and teaching effectiveness: A meta-analysis. Educational Research Review, 12(1), 59–76. https://doi.org/10.1016/j.edurev.2014.06.001 Article Google Scholar
• Kottmann, A., Schildkam, K., & van der Meulen, B. (2024). Determinants of the innovation behaviour of teachers in higher education. Innovative Higher Education, 49, 397–418. https://doi.org/10.1007/s10755-023-09689-y Article Google Scholar
• Lievens, J. (2023). Artificial intelligence (AI) in higher education: Tool or trickery? Education and Developments, 2, 645–647. Google Scholar
• Lu, J., Zheng, R., Gong, Z., & Xu, H. (2024). Supporting teachers’ professional development with generative AI: The effects on higher order thinking and self-efficacy. IEEE Transactions on Learning Technologies, 17(1), 1279–1289. https://doi.org/10.1109/TLT.2024.3369690 Article Google Scholar
• McDonald, J., Liu, D., Moskal, A., Zeng, R., Blumenstein, M., Gunn, C., Leichtweis, S., & Pardo, A. (2016). Cross-institutional collaboration to support student engagement. In S. Barker, S. Dawson, & C. Colvin (Eds.), Show me the learning. ASCILITE, 1(1), 397–405
• Moore, S., & Lookadoo, K. (2024). Communicating clear guidance: Advice for generative AI policy development in higher education. Business and Professional Communication Quarterly, 1, 1–20. https://doi.org/10.1177/23294906241254786 Article Google Scholar
• Mulder, D. J. (2016). Pre-service teachers and technology integration: International cases and generational attitudes toward technology in education. Handbook of Research on Global Issues in Next-Generation Teacher Education. Edited by J. Keengwe, J. Mbae, and G. Onchwari. Hershey, PA: Information Science Reference
• Nawaz, I. Y. (2020). Characteristics of Millennials and technology adoption in the digital age. In S. Dadwal (Ed.), Handbook of Research on Innovations in Technology and Marketing for the Connected Consumer (pp. 241–262). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-7998-0131-3.ch012
• Ng, P. M. L., Wut, T. M., & Lo, M. F. (2024). Enhancing knowledge sharing behaviour in building academics’ career capital in higher education: The mediating role of innovative climate. Technology, Knowledge and Learning, 29, 91–111. https://doi.org/10.1007/s10758-022-09633-7 Article Google Scholar
• Nyaaba, M., & Zhai, X. (2024). Generative AI professional development needs for teacher educators. Journal of AI, 8(1), 1–13. https://doi.org/10.61969/jai.1385915 Article Google Scholar
• Orakci, S., Goksu, D. Y., & Karagoz, S. (2023). A mixed methods study of the teachers’ self-efficacy views and their ability to improve self-efficacy beliefs during teaching. Frontiers in Psychology, 13, 1. https://doi.org/10.3389/fpsyg.2022.1035829 Article Google Scholar
• Osborne, J., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8(2), 1–5. https://doi.org/10.7275/r222-hv23 Article Google Scholar
• Padayachee, K. (2017). The myths and realities of generational cohort theory on ICT integration in education: A South African perspective. The African Journal of Information Systems, 10(1), 54–84. Google Scholar
• Pallister, J. G., & Foxall, G. R. (1998). Psychometric properties of the Hurt-Joseph-Cook scales for the measurement of innovativeness. Technovation, 18(11), 663–675. https://doi.org/10.1016/S0166-4972(98)00070-4 Article Google Scholar
• Peterson, R. A. (2000). Constructing effective questionnaires. Sage. Book Google Scholar
• Pollock, J. E., Tolone, L. J., & Nunnally, G. S. (2021). How innovative teachers can start teaching innovation. Educational Leadership, 1, 21–25. Google Scholar
• Polly, D., Martin, F., & Guilbaud, T. C. (2021). Examining barriers and desired supports to increase faculty members’ use of digital technologies: Perspectives of faculty, staff and administrators. Journal of Computers in Higher Education, 33(1), 135–156. https://doi.org/10.1007/s12528-020-09259-7 Article Google Scholar
• Polyportis, A. (2024). A longitudinal study on artificial intelligence adoption: Understanding the drivers of ChatGPT usage behavior change in higher education. Frontiers in Artificial Intelligence, 6(1), 1324398. https://doi.org/10.3389/frai.2023.1324398 Article Google Scholar
• Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3(1), 121–154. https://doi.org/10.1016/j.iotcps.2023.04.003 Article Google Scholar
• Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York. Google Scholar
• 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 Article Google Scholar
• Salazar, L., Garcia, N., Diego-Medrano, E., & Castillo, Y. (2019). Personal and professional challenges of Millennial faculty. In M. G. Strawser (Ed.), Leading millennial faculty (pp. 101–120). Lexington Books. https://doi.org/10.5040/9781666998108.ch-7
• Satterly, B. A., Cullen, J., & Dyson, D. A. (2018). The intergenerational mentoring model: An alternative to traditional and reverse models of mentoring. Mentoring & Tutoring: Partnership in Learning, 26(4), 441–454. https://doi.org/10.1080/13611267.2018.1530172 Article Google Scholar
• Shahzad, M. F., Xu, S., & Asif, M. (2024). Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model. British Educational Research Journal
• Shaw, C., Yuan, L., Brennan, D., Martin, S., Janson, N., Fox, K., & Bryant, G. (2023). GenAI in higher education: Fall 2023 update time for class study. Tyton Partners. https://www.tytonpartners.com/time-for-class-2023/GenAI-update
• Strauss, W., & Howe, N. (1991). Generations: The history of America’s future, 1584 to 2069. Morrow. Google Scholar
• Sumandal, A. H. (2023). Teachers’ self-efficacy with artificial intelligence (AI) based educational tools. Ignatian International Journal for Multidisciplinary Research, 1(1), 1–10. https://doi.org/10.17613/7qv1-0g04 Article Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Allyn & Bacon. Google Scholar
• Thap, D., Ho, V. T., Tran, V. D., & Nguyen, V. D. (2023). Examining the factor structure of the teachers’ sense of efficacy scale in the Vietnamese context. International Journal of Education and Practice, 11(1), 47–58. Article Google Scholar
• 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 influencing ChatGPT adoption. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-01-2024-001 Article Google Scholar
• Tschannen-Moran, M., & Hoy, A. W. (2001). Teacher efficacy: Capturing an elusive construct. Teaching and Teacher Education, 17(7), 783–805. https://doi.org/10.1016/S0742-051X(01)00036-1 Article Google Scholar
• Wang, C., Chen, X., Hu, Z., Jin, S., & Gu, X. (2025). Deconstructing university learners’ adoption intention toward AIGC technology: A mixed-methods study using ChatGPT as an example. Journal of Computer Assisted Learning, 41(1), e13117. Article Google Scholar
• Wang, K., Ruan, Q., Zhang, X., Fu, C., & Duan, B. (2024). Pre-service teachers’ GenAI anxiety, technology self-efficacy, and TPACK: Their structural relations with behavioral intention to design GenAI-assisted teaching. Behavioral Sciences, 14(5), 373. https://doi.org/10.3390/bs14050373 Article Google Scholar
• Wang, Y., Liu, C., & Tu, Y.-F. (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. Google Scholar
• Winter, J. D., Dodou, D., & Eisma, Y. B. (2024). Personality and acceptance as predictors of ChatGPT use. Discover Psychology, 4(57), 1–16. https://doi.org/10.1007/s44202-024-00161-2 Article Google Scholar
• Yaakob, M. F., Don, Y., Sufi, I., & Yusof, M. R. (2020). Teachers’ professional development level across cohort of generations in Malaysia. International Journal of Instruction, 13(4), 443–456. Article Google Scholar
• Yu, T., Dai, J., Chen, X., & Wang, C. (2025). Factors influencing continuance intention in blended learning among business school students in China: Based on grounded theory and FsQCA. Interactive Learning Environments, 33(2), 1339–1366. https://doi.org/10.1080/10494820.2024.2370477 Article Google Scholar
• Zhai, X. (2023). ChatGPT user experience: Implications for education. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4312418
• Zhang, R. C., Wu, H. K., & Chien, S. P. (2024). Identifying secondary science teachers’ adoption styles of technology-based assessments and examining the patterns of teachers’ beliefs, attitudes, and intention: A latent class analysis approach. Education and Information Technologies, 29(16), 21719–21749. https://doi.org/10.1007/s10639-024-12721-z Article Google Scholar

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