Adoption of Artificial Intelligence: Influence of User Experience,Perceived Versatility and Educational Impact on Overall Satisfaction and Future Intentions
Restricted accessResearch articleFirst published online 2026
Adoption of Artificial Intelligence: Influence of User Experience,Perceived Versatility and Educational Impact on Overall Satisfaction and Future Intentions
Artificial intelligence (AI) is rapidly developing as a game changer in higher education across geographies, and India is no exception. The main objective of this research is to investigate the adoption of generative AI tools such as ChatGPT in higher education and how management students perceive and adopt the same in order to meet their education needs. Primary data were gathered from 313 students from India pursuing management education. A structured questionnaire with previously validated and contextually adapted scale encompassing five elements, including user experience (UE), perceived versatility (VER), educational impact (EI), overall satisfaction (OAS) and intentions for future usage (FI), was used. The confirmatory factor analysis shows a strong association among UE, VER and EI as explanatory variables, and OAS and FI as outcome variables. This research is novel as it presents and confirms the VER construct as a strong and independent predictor. The research highlights the importance of AI tools that can support the varied learning styles and changing learning needs of management students. The results support the mediating nature of OAS between the perception linked to the user and future behavioural intentions. The study further confirms its centrality in technology adoption models in higher education settings. The results of the study provide valuable insights to educators, institutional leaders and developers of AI tools who endeavour to integrate generative AI tools in management education.
AjiC., & KhanJ. (2019). The impact of active learning on students’ academic performance. Open Journal of Social Sciences, 7, 204–211. https://doi.org/10.4236/jss.2019.73017
2.
AlmohsenD., & KamalA. (2025). The predictors of behavioural intention to use ChatGPT for academic purposes: Evidence from higher education in Somalia. Cogent Education, 12(1), 2460250. https://doi.org/10.1080/2331186X.2025.2460250
3.
Al-MughairiH., & BhaskarP. (2024). Exploring the factors affecting the adoption of AI techniques in higher education: Insights from teachers’ perspectives on ChatGPT. Journal of Research in Innovative Teaching & Learning, 18(2), 232–247. https://doi.org/10.1108/JRIT-09-2023-0129
4.
Al-QaysiN., Al-EmranM., Al-SharafiM.A., & YaseenZ.M. (2024). Generative AI and educational sustainability: Examining the role of knowledge management factors and AI attributes using a deep learning–based hybrid SEM-ANN approach. Computer Standards and Interfaces, 93, 103964. https://doi.org/10.1016/j.jedutech.2024
5.
AlraimiK. M., ZoH., & CiganekA. P. (2015). Understanding the continuance of MOOCs: The role of openness and reputation. Computers & Education, 80, 28–38. https://doi.org/10.1016/j.compedu.2014.08.006
6.
BhutoriaA. (2022). Personalised education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-the-loop model. Computers and Education: Artificial Intelligence, 3, 100068.
7.
CarufelR. (2023). New research finds men adopting AI tools at higher rates than women. https://www.agilitypr.com/pr-news/public-relations/new-research-finds-men-adopting-ai-tools-at-higher-rates-than-women/
8.
ChattiM. A., JarkeM., & SpechtM. (2010). The 3P learning model. Journal of Educational Technology & Society, 13(4), 74–85.
9.
CheungG. W., & RensvoldR. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modelling, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5
10.
CrawfordK. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. https://doi.org/10.12987/9780300252392
11.
DavisF. D. (1989). Perceived usefulness, ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
12.
DwivediY. K., KshetriN., HughesL., SladeE. L., JeyarajA., KarA. K., BaabdullahA. M., KoohangA., RaghavanV., AhujaM., AlbannaH., AlbashrawiM. A., Al-BusaidiA. S., BalakrishnanJ., BarletteY., BasuS., BoseI., BrooksL., BuhalisD., CarterL., … ZhouY. (2023). ‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
13.
FabusJ., GarbarovaM., VartiakL., & MitkovaL. (2025). The use of AI tools in managerial education. Management Theory and Studies for Rural Business and Infrastructure Development, 47(2), 251–259. https://doi.org/10.15544/mts.2025.19
14.
FreemanS., EddyS., McDonoughM., SmithM., OkoroaforN., JordtH., & WenderothM. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences of the United States of America, 111(23), 8410–8415. https://doi.org/10.1073/pnas.1319030111
García-PeñalvoF. J. (2023). Generative artificial intelligence: New scenarios in teaching, learning, and communication. Keynote at VIII Congreso Internacional de Estudios sobre Medios de Comunicación, Universidad Complutense de Madrid. Published via Zenodo. https://doi.org/10.5281/zenodo.8311708
17.
GuhaA., BressgottT., GrewalD., MahrD., WetzelsM., & SchweigerE. (2023). How artificiality and intelligence affect evaluations of voice assistants. Journal of the Academy of Marketing Science, 51(4), 843–866.
18.
GuptaP., MahajanR., BadheraU., & KushwahaP. S. (2024). Integrating generative AI in management education: A mixed-methods study using social construction of technology theory. The International Journal of Management Education, 22(3), 101017.
19.
HolmesW., BialikM., & FadelC. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Centre for Curriculum Redesign. (BOOK).
20.
HwangG.-J., & TuY.-F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
21.
IsmailN., & JoshyL. M. (2025). Exploring the role of perceived usefulness and AI anxiety in technology-driven self-directed learning: A paradox mindset perspective. Management and Labour Studies, 50(3), 448–465. https://doi.org/10.1177/0258042X251357415
22.
JoH. (2024). From concerns to benefits: A comprehensive study of ChatGPT usage in education. International Journal of Educational Technology in Higher Education, 21, 35. https://doi.org/10.1186/s41239-024-00471-4
23.
KasneciE., SesslerK., KüchemannS., BannertM., DementievaD., FischerF., GasserU., GrohG., GünnemannS., HüllermeierE., KruscheS., KutyniokG., MichaeliT., NerdelC., PfefferJ., PoquetO., SailerM., SchmidtA., SeidelT., StadlerM., WellerJ., KuhnJ., & KasneciG. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
24.
KimJ., KimJ. H. (Sam)KimS., KooC., & ChungN. (2025). Is a shorter reaction time always better? Empirical investigation of the impact of response speed on ChatGPT recommendations. International Journal of Hospitality Management, 130, Article 104239. https://doi.org/10.1016/j.ijhm.2025.104239
25.
LiuM., RenY., NyagogaL., StonierF., WuZ., & YuL. (2023). Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools. Future in Educational Research, 1(1), 72–101. https://doi.org/10.1002/fer3.10
26.
LundB., & WangT. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries?Library Hi Tech News, 40(3), 26–29. https://doi.org/10.1108/LHTN-01-2023-0009
27.
MenonD., & ShilpaK. (2023). Chatting with ChatGPT: Analysing the factors influencing users’ intention to use OpenAI’s ChatGPT using the UTAUT model. Heliyon, 9(11), e20962. https://doi.org/10.1016/j.heliyon.2023.e20962
28.
MogajiE., BalakrishnanJ., NwobaA., & NguyenN. P. (2021). Emerging-market consumers’ interactions with banking chatbots. Telematics and Informatics, 65, 101711. https://doi.org/10.1016/j.tele.2021.101711
29.
MogaviR. H., DengC., KimJ. U., ZhouP., KwonY. D., MetwallyA. H. S., TliliA., BassanelliS., BucchiaroneA., GujarS., NackeL. E., & HuiP. (2023). Exploring user perspectives on ChatGPT: Applications, perceptions, and implications for AI-integrated education. arXiv. https://doi.org/10.48550/arXiv.2305.13114
30.
NouraldeenR. M. (2023). The impact of technology readiness and perceptions of technology use on students’ adoption of artificial intelligence: the moderating role of gender. Development and Learning in Organisations, 37(3), 7–10.
31.
Ofosu-AmpongK. (2023). Gender differences in perception of artificial intelligence- based tools. Journal of Digital Art & Humanities, 4, 52–56. https://doi.org/10.33847/2712-8149.4.2_6
32.
PhilipJ. (2009). Management education in India. In PhilipJ. (Ed.). Business education in India: A ringside view (pp. 25–32). Xavier Institute of Management and Entrepreneurship (XIME).
33.
PriyaS. S., JainV., PriyaM. S., DixitS. K., & JoshiG. (2023). Modelling the factors in the adoption of artificial intelligence in Indian management institutes. Foresight, 25(1), 20–40. https://doi.org/10.1108/FS-09-2021-0181
34.
RaniN. M., & RaoR. S. (2019). Expectations and perceptions regarding management education: An assessment of students in Bengaluru, India. South Asian Journal of Management, 26(2), 157–170.
35.
RouabhiaD. (2024). Artificial intelligence-driven course generation: A case study using ChatGPT. https://arxiv.org/abs/2411.01369
36.
SaifS. A., & AliF. (2024). Investigating the influence of ChatGPT adoption on students’ academic performance using the technology acceptance model. Computers & Education, 193, 104739. https://doi.org/10.1016/j.compedu.2024.104739
37.
SajjaR., SermetY., CikmazM., CwiertnyD., & DemirI. (2023). Artificial intelligence-enabled intelligent assistant for personalized and adaptive learning in higher education. arXiv. https://arxiv.org/abs/2309.10892
38.
SkjuveM., FølstadA., & BrandtzaegP. B. (2023). The user experience of ChatGPT: Findings from a questionnaire study of early users. InProceedings of the 5th International Conference on Conversational User Interfaces (CUI ’23). ACM. https://doi.org/10.1145/3571884.3597144
39.
VenkateshV., MorrisM. G., DavisG. B., & DavisF. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
40.
WangY., & ZhaoY. (2024). TRAM: Benchmarking temporal reasoning for large language models. In Findings of the Association for Computational Linguistics: ACL 2024, 6389–6415. Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findingsacl.382
41.
WoodD., & MossS. H. (2024). Evaluating the impact of students’ generative AI use in educational contexts. Journal of Research in Innovative Teaching & Learning, 17(2), 152–167. https://doi.org/10.1108/JRIT-06-2024-0151
42.
YilmazR., & YilmazF. G. K. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, Article 100147.
43.
Zawacki-RichterO., MarínV. I., BondM., & GouverneurF. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators?International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0
44.
ZhouL., & LiJ. J. (2023). The impact of ChatGPT on learning motivation: A study based on self-determination theory. Education Sciences & Management, 1(1), 19–29. https://doi.org/10.56578/esm010103
