Adoption of AI Chat Bot like Chat GPT in Higher Education in India: a SEM Analysis Approach
DOI:
https://doi.org/10.36683/2306-1758/2023-4-46/130-149Keywords:
artificial intelligence, higher education, chat GPT, SEM model, chat botAbstract
Applications of artificial intelligence have grown to be one of the most important and well-known targets for nations in the modern era, particularly in the education sector. This is because these technologies have the potential to boost productivity and help the sector develop quickly by presenting scientific information to students in an appealing manner. To explore the link between latent variables, structural equation modeling is done using the partial least square technique structural equation model (PLS-SEM) with the help of Smart PLS. The intent of this investigation is to offer empirical support and explain the variables that may influence the adoption of artificial intelligence in higher education. The finding suggests that the hedonic, gamification, and motivational factors, as well as the convenience and efficiency factors, all have a significant impact on the adoption of artificial intelligence in India, like Chat GPT.Downloads
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Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2022). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments, 1-20.
Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. https://digitalcommons.uri.edu/cba_facpubs/548.
Bacon, L. D. (1999). Using LISREL and PLS to Measure Customer Satisfaction. Sawtooth Software Conference Proceedings, La Jolla, California, Feb 2-5, 305-306.
Bagozzi, R.P. & Yi, Y. (1988). On the evaluation of structural equation models. J. Acad. Mark. Sci., 16, 74-94.
Banik, S., & Gao, Y. (2023). Exploring the hedonic factors affecting customer experiences in phygital retailing. Journal of Retailing and Consumer Services, 70, 103147.
Bentler, P.M. & Bonett, D.G. (1980) Significance tests and goodness–of–fit in the analysis of covariance structures. Psychol. Bull., 88, 588-600.
Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press.
Brown, L., & Davis, M. (2018). Enhancing Student Learning through AI-powered Educational Platforms. International Journal of Educational Technology, 32(4), 567-589.
Brown, T. B., et al. (2020). Language models are few-shot learners. ArXiv abs/2005.14165.
Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent technologies for web-based education. International Journal of Artificial Intelligence in Education, 13(2-4), 159-172.
Bulger, M. E., & Mayer, R. E. (2019). The influence of artificial intelligence on learning and instruction. Journal of Educational Psychology, 111(5), 701-712.
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.
Cho, V., & Lai, Y. (2018). The impact of personalized recommendation in E-learning systems: A review. Computers & Education, 128, 398-407.
Clark, D. B., & Martinez-Garza, M. (2019). Learning analytics for 21st-century competencies. Journal of Learning Analytics, 6(2), 1-9.
Clark, R. E. (2010). The impact of motivation on cognitive engagement. Journal of Computer Assisted Learning, 26(4), 289-298.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computer in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. doi:10.1111/j.1559-1816.1992.tb00945.x.
Dicheva, D., Dichev, C., Agre, G., & Angelova, G. (2015). Gamification in education: A systematic mapping study. Educational technology & society, 18(3), 75-88.
Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The Evolution of Research on Computer-Supported Collaborative Learning. 10.1007/978-1-4020-9827-7_1.
Duan, Y., Li, H., Whinston, A. B., & Zhang, X. (2009). Do online reviews matter? An empirical investigation of panel data. Decision Support Systems, 47(1), 133-141.
Fornell, C. & Larcker, D.F. (1981) Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res., 18, 39-50.
Garcia, M., & Lee, S. (2017). Enhancing student learning through AI-based analytics: A systematic review. Educational Technology Research and Development, 63(4), 567-589.
Hair Jr., J.F., et al. (2014) Partial Least Squares Structural Equation Modeling (PLS-SEM): An Emerging Tool in Business Research. European Business Review, 26, 106-121. https://doi.org/10.1108/EBR-10-2013-0128.
Hair, J.F.; Ringle, C.M.; Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract., 19, 139-152.
Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil transactions on benchmarks, standards and evaluations, 2(4), 100089.
Hemachandran, K., Verma, P., Pareek, P., Arora, N., Rajesh Kumar, K. V., Ahanger, T. A., ... & Ratna, R. (2022). Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education. Computational Intelligence and Neuroscience, 2022, 1410448. https://doi.org/10.1155/2022/1410448.
Henseler, J. & Chin, W.W. (2010). A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Struct. Equ. Modeling A Multidiscip. J., 17, 82-109.
Henseler, J.; Dijkstra, T.K.; Sarstedt, M.; Ringle, C.M.; Diamantopoulos, A.; Straub, D.W.; Ketchen, D.J.; Hair, J.F.; Hult, G.T.M.; Calantone, R.J. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann. Organ. Res. Methods, 17, 182-209.
Hirschi, A., & Herrmann, A. (2020). Artificial intelligence in career counseling and coaching: A critical review. Frontiers in Psychology, 11, 1-16.
Holzinger, A., Nischelwitzer, A., & Meisenberger, M. (2005). Lifelong-learning support by m-learning: Example scenarios. Journal of Universal Computer Science, 11(7), 1116-1134.
Huang, C. H. (2021). Using PLS-SEM model to explore the influencing factors of learning satisfaction in blended learning. Education Sciences, 11(5), 249.
Huang, Y., Liu, D., & Cui, G. (2020). The impact of virtual reality on learning: A meta-analysis. Computers & Education, 150, 103858.
Hwang, H., Malhotra, N. K., Kim, Y., Tomiuk, M. A., & Hong, S. (2010). A comparative study on parameter recovery of three approaches to structural equation modeling. Journal of Marketing Research, 47 (Aug), 699-712.
Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC/CoSN Horizon Report: 2016 K-12 Edition. The New Media Consortium.
Johnson, R., & Williams, B. (2019). Exploring the role of AI-based analytics in improving student engagement. Journal of Educational Research, 35(2), 67-89.
Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27(2), 119-132.
Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298-311.
Kujala, S., Roto, V., Väänänen-Vainio-Mattila, K., & Sinnelä, A. (2011, June). Identifying hedonic factors in long-term user experience. In Proceedings of the 2011 Conference on Designing Pleasurable Products and Interfaces (pp. 1-8).
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Lee, S., & Kim, H. (2017). The impact of AI-assisted time management tools on students' self-regulation skills. Computers & Education, 60(1), 234-256.
Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for Web portal context. Information & Management, 42(5), 683-693. doi:10.1016/j.im.2004.04.003.
Lin, J. C., Younessi, D. N., Kurapati, S. S., Tang, O. Y., & Scott, I. U. (2023). Comparison of GPT-3.5, GPT-4, and human user performance on a practice ophthalmology written examination. Eye (London, England), 37(17), 3694-3695. https://doi.org/10.1038/s41433-023-02564-2.
Lu, J., Yao, J. E., & Yu, C. (2005). Personal innova-tiveness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268. doi:10.1016/j.jsis.2005.07.003.
Madan, R., & Ashok, M. (2022). AI adoption and diffusion in public administration: a systematic literature review and future research agenda. Government Information Quarterly, 101774.
Mekler, E. D., Brahlmann, F., Tuch, A. N., & Opwis, K. (2017). Towards understanding the effects of individual gamification elements on intrinsic motivation and performance. Computers in Human Behavior, 71, 525-534.
Melchor, M.Q. & Julián, C.P. (2008). The impact of the human element in the information systems quality for decision making and user satisfaction. J. Comput. Inf. Syst., 48, 44-52.
Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?. Sustainability, 13(11), 5788.
Mijwil, M. M., Aggarwal, K., Mutar, D. S., Mansour, N., & Singh, R. (2022). The position of artificial intelligence in the future of education: an overview. Journal of Applied Sciences, 10(2).
Mollick, E., & Tornatzky, L. G. (2020). Artificial intelligence in career services: A study of student perceptions. Journal of Career Development, 47(2), 171-186.
O'Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938-955.
Owoc, M. L., Sawicka, A., & Weichbroth, P. (2019, August). Artificial intelligence technologies in education: benefits, challenges and strategies of implementation. In IFIP International Workshop on Artificial Intelligence for Knowledge Management (pp. 37-58). Cham: Springer International Publishing.
Pallathadka, H., Sonia, B., Sanchez, D. T., De Vera, J. V., Godinez, J. A. T., & Pepito, M. T. (2022). Investigating the impact of artificial intelligence in education sector by predicting student performance. Materials Today: Proceedings, 51, 2264-2267.
Pavlou, P.A. & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Q., 30, 115-143.
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199-3226.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
Rana, P., Gupta, L. R., Kumar, G., & Dubey, M. K. (2021, April). A taxonomy of various applications of artificial intelligence in education. In 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) (pp. 23-28). IEEE.
Russell, S., & Norvig, P. (2016). Artificial intelligence: A modern approach. Pearson.
Sharma, U., Tomar, P., Bhardwaj, H., & Sakalle, A. (2021). Artificial intelligence and its implications in education. In Impact of AI Technologies on Teaching, Learning, and Research in Higher Education (pp. 222-235). IGI Global.
Siemens, G., & Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Educational Technology & Society, 15(3), 1-2.
Singh Gill, S., Xu, M., Patros, P., Wu, H., Kaur, R., Kaur, K., ... & Buyya, R. (2023). Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots. arXiv e-prints, arXiv-2306.
Smith, J., & Johnson, A. (2018). The impact of AI-powered tools on student learning outcomes. Journal of Educational Technology, 42(3), 123-145.
Smith, J., & Johnson, A. (2020). The impact of AI-assisted time management tools on students' academic performance. Journal of Educational Technology, 45(2), 123-145.
Srivastava, P., Hassija, T., & Goyal, A. P. (2020). Unleashing the Potential of Artificial Intelligence in the Education Sector for Institutional Efficiency. In Transforming Management Using Artificial Intelligence Techniques (pp. 11-22). CRC Press.
Van Dis, et al., (2023). ChatGPT: five priorities for research, Nature, 614 (7947), 224-226.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.
Wang, M., & Han, X. (2018). The effectiveness of personalized learning using learning management systems: A meta-analysis. Journal of Educational Technology & Society, 21(2), 154-168.
Wong, K. K. (2010). Handling small survey sample size and skewed dataset with partial least square path modelling. Vue: The Magazine of the Marketing Research and Intelligence Association, November, 20-23.
Wong, K. K. K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing bulletin, 24(1), 1-32.
Woolf, B. P. (2010). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
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