Main Article Content
Abstract
The rapid advancement of artificial intelligence (AI) has led to the development of a wide array of tools which are transforming the education industry. The study investigates the adoption and use of AI tools by teachers within higher education institutions (HEIs), using the context of India. By employing an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study empirically examines the influence of two technological attributes (i.e. performance expectancy and effort expectancy), two contextual factors (i.e. social influence and facilitating conditions) and two personal characteristics (i.e. personal innovativeness and computer self-efficacy) on teachers’ behavioural intention to use AI tools for research work. The primary data were collected from 331 teachers working with HEIs in the Delhi-National Capital Region (NCR) of India. PLS-SEM technique was used to analyze the data. The findings indicate that teachers’ intention to adopt AI tools for research work is positively influenced by performance expectancy, effort expectancy, social influence, computer self-efficacy and personal innovativeness. Further, their actual use of AI tools is influenced by their behavioural intention and facilitating conditions. The study provides further verification of the effectiveness of the UTAUT framework in the context of using emerging technologies in the education sector. Findings from this study provide beneficial insights for HEIs and developers of AI tools.
Keywords
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The author declares that the submitted to Journal of e-Learning and Knowledge Society (Je-LKS) is original and that is has neither been published previously nor is currently being considered for publication elsewhere.
The author agrees that SIe-L (Italian Society of e-Learning) has the right to publish the material sent for inclusion in the journal Je-LKS.
The author agree that articles may be published in digital format (on the Internet or on any digital support and media) and in printed format, including future re-editions, in any language and in any license including proprietary licenses, creative commons license or open access license. SIe-L may also use parts of the work to advertise and promote the publication.
The author declares s/he has all the necessary rights to authorize the editor and SIe-L to publish the work.
The author assures that the publication of the work in no way infringes the rights of third parties, nor violates any penal norms and absolves SIe-L from all damages and costs which may result from publication.
The author declares further s/he has received written permission without limits of time, territory, or language from the rights holders for the free use of all images and parts of works still covered by copyright, without any cost or expenses to SIe-L.
For all the information please check the Ethical Code of Je-LKS, available at http://www.je-lks.org/index.php/ethical-code
References
- Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 9(2), 204-215. https://doi.org/10.1287/isre.9.2.204
- Alharbi, H., Ab Jalil, H., Omar, M. K., & Puad, M. H. M. (2022). The Roles of Mediators and Moderators in the Adoption of Madrasati (M) LMS among Teachers in Riyadh. International Journal of Learning, Teaching and Educational Research, 21(9), 95-119. https://doi.org/10.26803/ijlter.21.9.6
- Aljuaid, H. (2024). The Impact of Artificial Intelligence Tools on Academic Writing Instruction in Higher Education: A Systematic Review. Arab World English Journal (AWEJ) Special Issue on ChatGPT, https://ssrn.com/abstract=4814342
- Al-Mughairi, H., & Bhaskar, P. (2024). Exploring the factors affecting the adoption AI techniques in higher education: insights from teachers’ perspectives on ChatGPT. Journal of Research in Innovative Teaching & Learning. https://doi.org/10.1108/JRIT-09-2023-0129
- Atiqah, S. N., Hanafiah, M. H., Ismail, H., & Balasubramaniam, K. (2024). Preparing instructors to transition to online distance learning: a pandemic panacea?. Innoeduca. International Journal of Technology and Educational Innovation, 10(1), 5-28. https://doi.org/10.24310/ijtei.101.2024.16820
- Atkinson, C. F. (2024). Cheap, quick, and rigorous: Artificial intelligence and the systematic literature review. Social Science Computer Review, 42(2), 376-393. https://doi.org/10.1177/08944393231196281
- Bornstein, M. H., Jager, J., & Putnick, D. L. (2013). Sampling in developmental science: Situations, shortcomings, solutions, and standards. Developmental review, 33(4), 357-370. https://doi.org/10.1016/j.dr.2013.08.003
- Brunetti, F., Bonfanti, A., Chiarini, A., & Vannucci, V. (2023). Digitalization and academic research: knowing of and using digital services and software to develop scientific papers. The TQM Journal, 35(5), 1135-1155. https://doi.org/10.1108/TQM-02-2022-0050
- Buabeng-Andoh, C., & Baah, C. (2020). Determinants of students’ actual use of the learning management system (LMS): An empirical analysis of a research model. Advances in Science, Technology and Engineering Systems Journal, 5(2), 614-620. https://www.astesj.com/v05/i02/p77/
- Budhathoki, T., Zirar, A., Njoya, E. T., & Timsina, A. (2024). ChatGPT adoption and anxiety: a cross-country analysis utilising the unified theory of acceptance and use of technology (UTAUT). Studies in Higher Education, 1-16. https://doi.org/10.1080/03075079.2024.2333937
- Casal, J. E., & Kessler, M. (2023). Can linguists distinguish between ChatGPT/AI and human writing?: A study of research ethics and academic publishing. Research Methods in Applied Linguistics, 2(3), 100068. https://doi.org/10.1016/j.rmal.2023.100068
- Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
- Clifford, P. L. R. (2024). AI in Higher Education: Faculty Perspective Towards Artificial Intelligence through UTAUT Approach. Ho Chi Minh City Open University Journal of Science-Social Sciences, 14(4). https://journalofscience.ou.edu.vn/index.php/soci-en/article/view/2851
- Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211. https://doi.org/10.2307/249688
- Edvardsen, Dag Fjeld, Finn R. Førsund, and Sverre AC Kittelsen (2017). Productivity development of Norwegian institutions of higher education 2004–2013. Journal of the Operational Research Society 68, 399-415. https://doi.org/10.1057/s41274-017-0183-x
- El Alfy, S., & Kehal, M. (2024). Investigating the factors affecting educators’ adoption of learning analytics using the UTAUT model. The International Journal of Information and Learning Technology, 41(3), 280-303. https://doi.org/10.1108/IJILT-06-2023-0102
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
- Gökçearslan, Ş., Yildiz Durak, H., & Atman Uslu, N. (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
- Greco, S., & Cinganotto, L. (2023). Re-thinking Education in the Age of AI. Journal of e-Learning and Knowledge Society, 19(3), I-IV. https://doi.org/10.20368/1971-8829/1135873
- Guillén-Gámez, F. D., Ruiz-Palmero, J., & García, M. G. (2023). Digital competence of teachers in the use of ICT for research work: development of an instrument from a PLS-SEM approach. Education and Information Technologies, 28(12), 16509-16529. https://doi.org/10.1007/s10639-023-11895-2
- Gupta, K. P., & Bhaskar, P. (2023). Teachers’ intention to adopt virtual reality technology in management education. International Journal of Learning and Change, 15(1), 28-50. https://doi.org/10.1504/IJLC.2023.127719
- Hair, Jr, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European business review, 28(1), 63-76. https://doi.org/10.1108/EBR-09-2015-0094
- 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, 115-135. https://doi.org/10.1007/s11747-014-0403-8
- Hu, S., Laxman, K., & Lee, K. (2020). Exploring factors affecting academics’ adoption of emerging mobile technologies-an extended UTAUT perspective. Education and Information Technologies, 25, 4615-4635. https://doi.org/10.1007/s10639-020-10171-x
- Jakkaew, P., & Hemrungrote, S. (2017). The use of UTAUT2 model for understanding student perceptions using Google classroom: A case study of introduction to information technology course. In 2017 international conference on digital arts, media and technology (ICDAMT) (pp. 205-209). IEEE. 10.1109/ICDAMT.2017.7904962
- Joo, Y. J., Park, S., & Lim, E. (2018). Factors influencing preservice teachers’ intention to use technology: TPACK, teacher self-efficacy, and technology acceptance model. Journal of Educational Technology & Society, 21(3), 48-59. https://www.jstor.org/stable/26458506
- Kocaleva, M., Stojanovic, I., & Zdravev, Z. (2015). Model of e-learning acceptance and use for teaching staff in Higher Education Institutions. International Journal of Modern Education and Computer Science (IJMECS), 7(4), 23-31. https://eprints.ugd.edu.mk/id/eprint/11980
- Lakhal, S., & Khechine, H. (2021). Technological factors of students’ persistence in online courses in higher education: The moderating role of gender, age and prior online course experience. Education and Information Technologies, 26(3), 3347-3373. https://doi.org/10.1007/s10639-020-10407-w
- Lin, H. C., Ho, C. F., & Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: an empirical study of UTAUT model. International Journal of Mobile Learning and Organisation, 16(1), 74-94. https://doi.org/10.1504/IJMLO.2022.119966
- Lin, H. C., Ho, C. F., & Yang, H. (2022). Understanding adoption of artificial intelligence-enabled language e-learning system: An empirical study of UTAUT model. International Journal of Mobile Learning and Organisation, 16(1), 74-94. 10.1504/IJMLO.2022.119966
- Loogma, K., Kruusvall, J., & Ümarik, M. (2012). E-learning as innovation: Exploring innovativeness of the VET teachers’ community in Estonia. Computers & Education, 58(2), 808-817. https://doi.org/10.1016/j.compedu.2011.10.005
- Lopez, V., & Whitehead, D. (2013). Sampling data and data collection in qualitative research. Nursing & midwifery research: Methods and appraisal for evidence-based practice, 123, 140. https://researchnow.flinders.edu.au/en/publications/sampling-data-and-data-collection-in-qualitative-research
- Lopez-Perez, V. A., Ramirez-Correa, P. E., & Grandon, E. E. (2019). Innovativeness and factors that affect the information technology adoption in the classroom by primary teachers in Chile. Informatics in Education, 18(1), 165-181. https://www.ceeol.com/search/article-detail?id=761646
- Marsh, D. (2023). AI and the contemporary educational landscape: a personal view. Journal of e-Learning and Knowledge Society, 19(3), 1-5. https://doi.org/10.20368/1971-8829/1135874
- Mazman Akar, S. G. (2019). Does it matter being innovative: Teachers’ technology acceptance. Education and Information Technologies, 24(6), 3415-3432. https://doi.org/10.1007/s10639-019-09933-z
- Nikolopoulou, K., Gialamas, V., & Lavidas, K. (2020). Acceptance of mobile phone by university students for their studies: An investigation applying UTAUT2 model. Education and Information Technologies, 25, 4139-4155. https://doi.org/10.1007/s10639-020-10157-9
- Oguguo, B., Ezechukwu, R., Nannim, F., & Offor, K. (2023). Analysis of teachers in the use of digital resources in online teaching and assessment in COVID times. Innoeduca. International Journal of Technology and Educational Innovation, 9(1), 81-96. https://doi.org/10.24310/innoeduca.2023.v9i1.15419
- Perkins, M., & Roe, J. (2024). Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies. arXiv preprint arXiv:2408.06872. https://www.arxiv.org/pdf/2408.06872
- 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
- Raza, S. A., Qazi, W., Khan, K. A., & Salam, J. (2021). Social isolation and acceptance of the learning management system (LMS) in the time of COVID-19 pandemic: an expansion of the UTAUT model. Journal of Educational Computing Research, 59(2), 183-208. https://doi.org/10.1177/0735633120960421
- Rodríguez-Gil, M. E. (2024). Factors Influencing 360-Degree Video Adoption in e-Learning: a UTAUT2 Case Study with Pre-service Primary Education Teachers in Spain. Journal of e-Learning and Knowledge Society, 20(1), 27-36. https://doi.org/10.20368/1971-8829/1135881
- Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., & García-Peñalvo, F. J. (2020). Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students. International Journal of Interactive Multimedia and Artificial Intelligence, 6(4), 80. https://doi.org/10.9781/ijimai.2020.11.009
- Saunders, M. N. (2012). Choosing research participants. Qualitative organizational research: Core methods and current challenges, 35-52. https://www.torrossa.com/en/resources/an/4913040#page=54
- Shtykalo, O., & Yamnenko, I. (2024, February). ChatGPT and Other AI Tools for Academic Research and Education. In IEEE lnternational Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (pp. 605-630). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-61221-3_29
- Siyam, N. (2019). Factors impacting special education teachers’ acceptance and actual use of technology. Education and Information Technologies, 24(3), 2035-2057. https://doi.org/10.1007/s10639-018-09859-y
- Sok, S., & Heng, K. (2023). ChatGPT for education and research: A review of benefits and risks. Cambodian Journal of Educational Research, 3(1), 110-121.
- Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1-14. https://doi.org/10.1080/10494820.2023.2209881
- Šumak, B., & Šorgo, A. (2016). The acceptance and use of interactive whiteboards among teachers: Differences in UTAUT determinants between pre-and post-adopters. Computers in Human Behavior, 64, 602-620. https://doi.org/10.1016/j.chb.2016.07.037
- Sun, Y., & Jeyaraj, A. (2013). Information technology adoption and continuance: A longitudinal study of individuals’ behavioral intentions. Information & Management, 50(7), 457-465. https://doi.org/10.1016/j.im.2013.07.005
- Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & education, 52(2), 302-312. https://doi.org/10.1016/j.compedu.2008.08.006
- Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008
- Tian, W., Ge, J., Zhao, Y., & Zheng, X. (2024). AI Chatbots in Chinese higher education: adoption, perception, and influence among graduate students—an integrated analysis utilizing UTAUT and ECM models. Frontiers in Psychology, 15, 1268549. https://doi.org/10.3389/fpsyg.2024.1268549
- Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139. https://doi.org/10.2307/3250981
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. https://doi.org/10.2307/30036540
- Wang, C. S., Jeng, Y. L., & Huang, Y. M. (2017). What influences teachers to continue using cloud services? The role of facilitating conditions and social influence. The Electronic Library, 35(3), 520-533. https://doi.org/10.1108/EL-02-2016-0046
- Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring factors of the willingness to accept AI-assisted learning environments: an empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 13, 870777. https://doi.org/10.3389/fpsyg.2022.870777
- Zhao, C., & Zhao, L. (2021). Digital nativity, computer self-efficacy, and technology adoption: a study among university faculties in China. Frontiers in Psychology, 12, 746292. https://doi.org/10.3389/fpsyg.2021.746292
- Zhao, F., Ahmed, F., Iqbal, M. K., Mughal, M. F., Qin, Y. J., Faraz, N. A., & Hunt, V. J. (2020). Shaping behaviors through institutional support in British higher educational institutions: Focusing on employees for sustainable technological change. Frontiers in Psychology, 11, 584857. https://doi.org/10.3389/fpsyg.2020.584857