Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance

Ravi Sankar Pasupuleti; Deevena Charitha  Jangam; Sai Manideep Appana; Venkateswarlu Nalluri; Deepthi Thiyyagura

doi:10.30935/cedtech/17628

Ravi Sankar Pasupuleti ¹, Deevena Charitha Jangam ², Sai Manideep Appana ³, Venkateswarlu Nalluri ⁴ ^* , Deepthi Thiyyagura ⁵

More Detail

CONT ED TECHNOLOGY, Volume 18, Issue 1, Article No: ep621

https://doi.org/10.30935/cedtech/17628

Submitted: 22 November 2024, Published Online: 22 December 2025

OPEN ACCESS 99 Views 49 Downloads

Download Full Text (PDF)

Abstract

The advent of artificial intelligence (AI) has had a profound impact on the education sector, resulting in a transformative change in higher education worldwide. One such change is the usage of AI tools by teachers to enhance their teaching practices, including content creation, sharing, and personalized learning. Those certain obstacles persist for teachers while fully exploring the potential of AI and its adoption in teaching practices. An extensive review of the literature revealed a significant research gap in developing a comprehensive study to examine the influence of AI relevance and its readiness, performance expectancy (PE), and effort expectancy (EE) in shaping behavioral intention (BI) for AI adoption in teaching. Therefore, drawing cues from the unified theory of acceptance and use of technology a research framework was developed to examine these intricate relationships. We gathered data by administering a survey to higher education teachers across various educational organizations in India. Structural equation modeling (SEM) was employed to analyze the collected data and test the hypothesized relationships. The results uncovered a positive association between teacher’s perceptions of AI’s relevance and their readiness to adopt AI, with both factors positively influencing their BI. Furthermore, this study found that EE exhibited a significant positive effect on both BI and PE. This study discusses theoretical and practical implications, underscoring the importance of raising awareness about AI’s relevance, and lays the groundwork for further exploration in this emerging area, intending to inform strategies and interventions to support successful AI adoption in educational organizations.

Keywords: artificial intelligence, AI adoption, relevance, readiness, SEM, higher education

References

Aburagaga, I., Agoyi, M., & Elgedawy, I. (2020). Assessing faculty’s use of social network tools in Libyan higher education via a technology acceptance model. IEEE Access, 8, 116415-116430. https://doi.org/10.1109/ACCESS.2020.3004200
Aburbeian, M., Owda, Y., & Owda, M. (2022). A technology acceptance model survey of the metaverse prospects. AI, 3(2), 285-302. https://doi.org/10.3390/ai3020018
Acquah, B. Y. S., Arthur, F., Salifu, I., Quayson, E., & Nortey, S. A. (2024). Preservice teachers’ behavioural intention to use artificial intelligence in lesson planning: A dual-staged PLS-SEM-ANN approach. Computers and Education: Artificial Intelligence, 7, Article 100307. https://doi.org/10.1016/j.caeai.2024.100307
Al-Amri, N. A., & Al-Abdullatif, A. M. (2024). Drivers of chatbot adoption among K-12 teachers in Saudi Arabia. Education Sciences , 14(9), Article 1034. https://doi.org/10.3390/educsci14091034
AlGerafi, M. A., Zhou, Y., Alfadda, H., & Wijaya, T. T. (2023). Understanding the factors influencing higher education students’ intention to adopt artificial intelligence-based robots. IEEE Access, 11, 99752-99764. https://doi.org/10.1109/ACCESS.2023.3314499
AlKanaan, H. (2022). Awareness regarding the implication of artificial intelligence in science education among pre-service science teachers. International Journal of Instruction, 15(3), Article 895912. https://doi.org/10.29333/iji.2022.15348a
Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673-174686. https://doi.org/10.1109/ACCESS.2019.2957206
Al-Rahmi, A. M., Shamsuddin, A., & Alismaiel, O. A. (2020). Unified theory of acceptance and use of technology (UTAUT) theory: The factors affecting students’ academic performance in higher education. Journal of Educational Psychology, 57, 2839-2848.
Alshehri, A., J Rutter, M., & Smith, S. (2020). The effects of UTAUT and usability qualities on students’ use of learning management systems in Saudi tertiary education. Journal of Information Technology Education: Research, 19. https://doi.org/10.28945/4659
Anjali, P., & Sreerekha, S. (2024). Navigating the artificial intelligence (AI) revolution: The paradigm shift in higher education. Educational Administration: Theory and Practice, 30(5), 12742-12745. https://doi.org/10.53555/kuey.v30i5.5301
Appana, S. M., Pasupuleti, R. S., Tulimelli, J., Dokku, Y., Chen, W. K., & Nalluri, V. (2025). Future agenda on higher education policy using artificial intelligence tools: A review and bibliometric analysis. Engineering Proceedings, 98(1), 12. https://doi.org/10.3390/engproc2025098012
Aruleba, K., Jere, N., & Matarirano, O. (2022). Technology adoption readiness in disadvantaged universities during COVID-19 pandemic in South Africa. International Journal of Higher Education, 11(2), Article 172. https://doi.org/10.5430/ijhe.v11n2p172
Asiri, M., & El Aasar, S. (2022). Employing technology acceptance model to assess the reality of using augmented reality applications in teaching from teachers’ point of view in Najran. Journal of Positive School Psychology, 6(2), 5241-5255.
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, Article 100099. https://doi.org/10.1016/j.caeai.2022.100099
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17, 1-12. https://doi.org/10.1186/s41239-020-00218-x
Bervell, B., & Umar, I. N. (2017). Validation of the UTAUT model: Re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research. Eurasia Journal of Mathematics, Science and Technology Education, 13(10), 6471-6490. https://doi.org/10.12973/ejmste/78076
Birch, A., & Irvine, V. (2009). Preservice teachers’ acceptance of ICT integration in the classroom: Applying the UTAUT model. Educational Media International, 46(4), 295-315. https://doi.org/10.1080/09523980903387506
Chakraborty, M., & Al Rashdi, S. (2018). Venkatesh et al.’s unified theory of acceptance and use of technology (UTAUT) (2003). In Technology adoption and social issues: Concepts, methodologies, tools, and applications (pp. 1657-1674). IGI Global. https://doi.org/10.4018/978-1-5225-5201-7.ch077
Cheok, M., Wong, S., Ayub, A., & Mahmud, R. (2017). Teachers’ perceptions of elearning in Malaysian secondary schools- frog VLE. Malaysian Online Journal of Educational Technology, 5(2), 20-33.
Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii-xvi.
Chocarro, R., Cortinas, M., & Matas, G. (2021). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 49(2), 295-313. https://doi.org/10.1080/03055698.2020.1850426
Cingillioglu, I., Gal, U., & Prokhorov, A. (2024). AI-experiments in education: An AI-driven randomized controlled trial for higher education research. Education and Information Technologies, 29, 19649-19677. https://doi.org/10.1007/s10639-024-12633-y
Cukurova, M., Khan-Galaria, M., Millan, E., & Luckin, R. (2021). A learning analytics approach to monitoring the quality of online one-to-one tutoring. Journal of Learning Analytics, 9(2), 105-120. https://doi.org/10.35542/osf.io/qfh7z
D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145-157. https://doi.org/10.1016/j.learninstruc.2011.10.001
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Vighio, M. S., Alblehai, F., Soomro, R. B., & Shutaleva, A. (2024). Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions. Education and Information Technologies, 29, 18695-18744. https://doi.org/10.1007/s10639-024-12599-x
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319-339. https://doi.org/10.2307/249008
De Smet, C. (2024). A qualitative evaluation study of introducing game-based learning methods during pre-service teachers’ internship. Studies in Educational Evaluation, 83, Article 101388. https://doi.org/10.1016/j.stueduc.2024.101388
Eltabakh, H., & Ismail, A. (2019). Design of e-learning environment based on formative adaptive tests model and their impact on the development of cognitive achievement of computer and data security course and self-efficacy skills for computer teacher students. EAECJ, 7(2), 1-64.
Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, Article 100198. https://doi.org/10.1016/j.caeai.2023.100198
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), Article 382. https://doi.org/10.2307/3150980
Fuchs, K., & Aguilos, V. (2023). Integrating artificial intelligence in higher education: Empirical insights from students about using ChatGPT. International Journal of Information and Education Technology, 13(9), 1365-1371. https://doi.org/10.18178/ijiet.2023.13.9.1939
George, B., & Wooden, O. (2023). Managing the strategic transformation of higher education through artificial intelligence. Administrative Sciences, 13(9), Article 196. https://doi.org/10.3390/admsci13090196
Hair Jr, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Prentice Hall
Hair Jr, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Pearson Prentice Hall.
Hair Jr, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12. https://doi.org/10.1016/j.lrp.2013.01.001
Hebert, C., Jenson, J., & Terzopoulos, T. (2021). “Access to technology is the major challenge”: Teacher perspectives on barriers to DGBL in K-12 classrooms. E-Learning and Digital Media, 18(3), 307-324. https://doi.org/10.1177/2042753021995315
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Huang, F., Teo, T., & Zhao, X. (2023). Examining factors influencing Chinese ethnic minority English teachers’ technology adoption: An extension of the UTAUT model. Computer Assisted Language Learning, 38(4), 928-950. https://doi.org/10.1080/09588221.2023.2239304
Huang, T.-Y., Chen, W.-K., Nalluri, V., & Huynh-Cam, T.-T. (2022). Evaluating e-teaching adoption criteria for Indian educational Organizations using fuzzy Delphi-TOPSIS approach. Mathematics, 10(13), Article 2175. https://doi.org/10.3390/math10132175
Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), Article 584. https://doi.org/10.3390/math9060584
Jain, K. K., & Raghuram, J. N. V. (2024). Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach. Education and Information Technologies, 29, 17169-17209. https://doi.org/10.1007/s10639-024-12506-4
Kim, J., & Lee, K. S.-S. (2022). Conceptual model to predict Filipino teachers’ adoption of ICT-based instruction in class: Using the UTAUT model. Asia Pacific Journal of Education, 42(4), 699-713. https://doi.org/10.1080/02188791.2020.1776213
Klimova, B., & Pikhart, M. (2025). Exploring the effects of artificial intelligence on student and academic well-being in higher education: A mini-review. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1498132
Kline, R. B. (2011). Methodology in the social sciences. Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
König, P. D., Wurster, S., & Siewert, M. B. (2023). Sustainability challenges of artificial intelligence and citizens’ regulatory preferences. Government Information Quarterly, 40(4), Article 101863. https://doi.org/10.1016/j.giq.2023.101863
Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of intention to use cloud e-learning application. Heliyon, 5(6), Article e01788. https://doi.org/10.1016/j.heliyon.2019.e01788
Lin, P.-C., Lu, H.-K., & Liu, S.-C. (2013). Towards an education behavioral intention model for e-learning systems: An extension Of UTAUT. Journal of Theoretical & Applied Information Technology, 47(3), Article 1200.
Lin, Y.-X., & Liu, Y. (2024). Let the city heal you: Environment and activity’s distinct roles in leisure restoration and satisfaction. Cities, 154, Article 105336. https://doi.org/10.1016/j.cities.2024.105336
Mahmoud, A. M. (2020). Artificial intelligence applications an introduction to education development in the light of corona virus pandemic (COVID-19) challenges. International Journal of Research in Educational Sciences, 3(4), 171-224. https://doi.org/10.29009/ijres.3.4.4
Mhina, J. R. A., Johar, M. G., & Alkawaz, M. H. (2019). The influence of perceived confidentiality risks and attitude on Tanzania government employees’ intention to adopt web 2.0 and social media for work-related purposes. International Journal of Public Administration, 42(7), 558-571. https://doi.org/10.1080/01900692.2018.1491596
Mohamed, M. J., & Hassan, S. A. (2023). Studying the factors that influence the adoption of educational technology in Mogadishu secondary schools using UTAUT model. International Journal of Information and Education Technology, 13(7), 1070-1077. https://doi.org/10.18178/ijiet.2023.13.7.1906
Molefi, R. R., Ayanwale, M. A., Kurata, L., & Chere-Masopha, J. (2024). Do in-service teachers accept artificial intelligence-driven technology? The mediating role of school support and resources. Computers and Education Open, 6, Article 100191. https://doi.org/10.1016/j.caeo.2024.100191
Nalluri, V., Huynh-Cam, T., Sama, H., & Chen, L. (2023). Decision-making model for the effective e-services adoption in the Indian educational organizations. Decision Science Letters, 12(2), 211-224. https://doi.org/10.5267/j.dsl.2023.2.003
Nikou, S. A., & Economides, A. A. (2019). Factors that influence behavioral intention to use mobile-based assessment: A STEM teachers’ perspective. British Journal of Educational Technology, 50(2), 587-600. https://doi.org/10.1111/bjet.12609
Parameswaran, S., Kishore, R., & Li, P. (2015). Within-study measurement invariance of the UTAUT instrument: An assessment with user technology engagement variables. Information & Management, 52(3), 317-336. https://doi.org/10.1016/j.im.2014.12.007
Pardamean, B., & Susanto, M. (2012). Assessing user acceptance toward blog technology using the UTAUT model. International Journal of Mathematics and Computers in Simulation, 1(6), 203-212.
Park, M.-J., & Lee, J.-K. (2021). Investigation of college students’ intention to accept online education services: An application of the UTAUT model in Korea. The Journal of Asian Finance, Economics and Business, 8(6), 327-336. https://doi.org/10.13106/jafeb.2021.vol8.no6.0327
Pasupuleti, R. S., & Seshadri, U. (2023). Subdimensions of smart servicescape: Empirical evidence using confirmatory factor analysis. IUP Journal of Marketing Management, 22(2).
Pasupuleti, R. S., & Thiyyagura, D. (2024). An empirical evidence on the continuance and recommendation intention of ChatGPT among higher education students in India: An extended technology continuance theory. Education and Information Technologies, 29, 17965-17985. https://doi.org/10.1007/s10639-024-12573-7
Pierce, G. L., & Cleary, P. F. (2016). The K-12 educational technology value chain: Apps for kids, tools for teachers and levers for reform. Education and Information Technologies, 21, 863-880. https://doi.org/10.1007/s10639-014-9357-1
Prestridge, S. (2012). The beliefs behind the teacher that influences their ICT practices. Computers & Education, 58(1), 449-458. https://doi.org/10.1016/j.compedu.2011.08.028
Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182, 104468. https://doi.org/10.1016/j.compedu.2022.104468
Ramadhani, D. P., Rachmawati, I., Dudija, N., Ayuningtias, H. G., Gunawan, A. A., & Dennyra, D. S. (2022). Acceleration of digital innovation & technology towards Society 5.0: Proceedings of the International Conference on Sustainable Collaboration in Business, Information and Innovation. Taylor & Francis. https://doi.org/10.1201/9781003222927
Sanusi, I. T., Ayanwale, M. A., & Chiu, T. K. (2024). Investigating the moderating effects of social good and confidence on teachers’ intention to prepare school students for artificial intelligence education. Education and Information Technologies , 29(1), 273-295. https://doi.org/10.1007/s10639-023-12250-1
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Semerci, A., & Aydin, M. K. (2018). Examining high school teachers’ attitudes towards ICT use in education. International Journal of Progressive Education, 14(2), 93-105. https://doi.org/10.29329/ijpe.2018.139.7
Seshadri, U., & Pasupuleti, R. S. (2023). An empirical exploration of the continuance intention of m-shopping app users through the lens of smart experience co-creation. International Journal of Business Excellence, 1(1), Article 1. https://doi.org/10.1504/IJBEX.2023.10055330
Shaikh, A. A., Glavee-Geo, R., & Karjaluoto, H. (2021). How relevant are risk perceptions, effort, and performance expectancy in mobile banking adoption? In Research anthology on securing mobile technologies and applications (pp. 692-716). IGI Global. https://doi.org/10.4018/978-1-7998-8545-0.ch038
Shin, S., & Shin, H. (2020). A study on the application of artificial intelligence in elementary science education. Journal of Korean Elementary Science Education, 39(1), 117-132. https://doi.org/10.15267/KESES.2020.39.1.117
Singh, S., & Singh, G. A. (2021). Assessing the impact of the digital divide on Indian society: A study of social exclusion. Research in Social Change, 13(1), 181-190. https://doi.org/10.2478/rsc-2021-0018
Singh, S., Kumar, V., Paliwal, M., Singh, S. V. P., & Mahlawat, S. (2023). Explaining the linkage between antecedents’ factors of adopting online classes and perceived learning outcome using extended UTAUT model. Data and Information Management, 7(4), Article 100052. https://doi.org/10.1016/j.dim.2023.100052
Tarhini, A., AlHinai, M., Al-Busaidi, A. S., Govindaluri, S. M., & Al Shaqsi, J. (2024). What drives the adoption of mobile learning services among college students: An application of SEM-neural network modeling. International Journal of Information Management Data Insights, 4(1), Article 100235. https://doi.org/10.1016/j.jjimei.2024.100235
Thomas, T., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development Using ICT, 9(3).
Topal, A. D., Eren, C. D., & Geçer, A. K. (2021). Chatbot application in a 5th grade science course. Education and Information Technologies, 26(5), 6241-6265. https://doi.org/10.1007/s10639-021-10627-8
Trotsko, A. V., Rybalko, L. S., Kirilenko, O. G., & Trush, H. O. (2019). Teachers’ professional self-improvement in the conditions of distance learning implementation in higher education institutions. Information Technologies and Learning Tools, 72(4), 258-272. https://doi.org/10.33407/itlt.v72i4.3088
Tummalapenta, S. R., Pasupuleti, R. S., Chebolu, R. M., Banala, T. V., & Thiyyagura, D. (2024). Factors driving ChatGPT continuance intention among higher education students: Integrating motivation, social dynamics, and technology adoption. Journal of Computers in Education. https://doi.org/10.1007/s40692-024-00343-w
Utomo, P., Kurniasari, F., & Purnamaningsih, P. (2021). The effects of performance expectancy, effort expectancy, facilitating condition, and habit on behavior intention in using mobile healthcare application. International Journal of Community Service & Engagement, 2(4), 183-197. https://doi.org/10.47747/ijcse.v2i4.529
Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: Crafting with text-to-image generative models. Digital Creativity, 34(1), 1-21. https://doi.org/10.1080/14626268.2023.2174557
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Wang, X., Li, L., Tan, S. C., Yang, L., & Lei, J. (2023). Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Computers in Human Behavior, 146, Article 107798. https://doi.org/10.1016/j.chb.2023.107798
Woodruff, K., Hutson, J., & Arnone, K. (2023). Perceptions and barriers to adopting artificial intelligence in K-12 education: A survey of educators in fifty states. IntechOpen. https://doi.org/10.5772/intechopen.1002741
Wozney, L., Venkatesh, V., & Abrami, P. (2006). Implementing computer technologies: Teachers’ perceptions and practices. Journal of Technology and Teacher Education, 14(1), 173-207.
Wu, F., Dang, Y., & Li, M. (2025). A systematic review of responses, attitudes, and utilization behaviors on generative AI for teaching and learning in higher education. Behavioral Sciences, 15(4), Article 467. https://doi.org/10.3390/bs15040467
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. https://doi.org/10.51767/jc1503
Yee, M. L. S., & Abdullah, M. S. (2021). A review of UTAUT and extended model as a conceptual framework in education research. Jurnal Pendidikan Sains dan Matematik Malaysia, 11, 1-20. https://doi.org/10.37134/jpsmm.vol11.sp.1.2021
Yoon, N., & Lee, H.-K. (2021). AI recommendation service acceptance: Assessing the effects of perceived empathy and need for cognition. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1912-1928. https://doi.org/10.3390/jtaer16050107
Zhang, R., Yamashita, N., Liu, Z., Guo, J., Hiruta, Y., Shirakawa, H., & Tanikawa, H. (2023). Paving the way to the future: Mapping historical patterns and future trends of road material stock in Japan. Science of the Total Environment, 903, Article 166632. https://doi.org/10.1016/j.scitotenv.2023.166632
Zhao, L., Chen, L., Liu, Q., Zhang, M., & Copland, H. (2019). Artificial intelligence-based platform for online teaching management systems. Journal of Intelligent & Fuzzy Systems, 37(1), 45-51. https://doi.org/10.3233/JIFS-179062
Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013

Citation

Pasupuleti, R. S., Jangam, D. C., Appana, S. M., Nalluri, V., & Thiyyagura, D. (2026). Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance. Contemporary Educational Technology, 18(1), ep621. https://doi.org/10.30935/cedtech/17628

Pasupuleti, R. S., Jangam, D. C., Appana, S. M., Nalluri, V., and Thiyyagura, D. (2026). Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance. Contemporary Educational Technology, 18(1), ep621. https://doi.org/10.30935/cedtech/17628

Pasupuleti RS, Jangam DC, Appana SM, Nalluri V, Thiyyagura D. Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance. CONT ED TECHNOLOGY. 2026;18(1), ep621. https://doi.org/10.30935/cedtech/17628

Pasupuleti, Ravi Sankar, Deevena Charitha Jangam, Sai Manideep Appana, Venkateswarlu Nalluri, and Deepthi Thiyyagura. "Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance". Contemporary Educational Technology 2026 18 no. 1 (2026): ep621. https://doi.org/10.30935/cedtech/17628

Pasupuleti, Ravi Sankar et al. "Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance". Contemporary Educational Technology, vol. 18, no. 1, 2026, ep621. https://doi.org/10.30935/cedtech/17628

Pasupuleti RS, Jangam DC, Appana SM, Nalluri V, Thiyyagura D. Understanding artificial intelligence adoption in higher education: An SEM-based evaluation of readiness and relevance. CONT ED TECHNOLOGY. 2026;18(1):ep621. https://doi.org/10.30935/cedtech/17628