.png)
Cognitive and Technological Factors Shaping Students’ Sustained Use of ChatGPT in Higher Education
Abstract
This study examines the cognitive and technological factors shaping students' sustained use of ChatGPT in Indonesian higher education. Despite the rapid adoption of generative Artificial Intelligence (AI) in education, a clear understanding of the factors sustaining continued engagement with such systems remains limited. While continuance intention has been widely examined, the application of the Expectation–Confirmation Model (ECM) in generative AI contexts remains underexplored. This gap is especially evident when considering the role of AI-specific system attributes in shaping post-adoption evaluations. Although ECM has been extended with various constructs in prior studies, the specific integration of AI characteristics, particularly perceived intelligence and anthropomorphism, has not been explored in generative AI use in education, especially within Indonesian higher education. To address this gap, a multi-theoretic framework integrating ECM and AI characteristics was developed. Data from 322 Indonesian students were analyzed using Partial Least Squares-Structural Equation Modeling. All ten hypotheses were supported, and the model explains 43.3% of the variance in continuance intention (R² = 0.433). Perceived Intelligence strongly influences Perceived Anthropomorphism with a path coefficient of 0.591, representing the strongest relationship in the model, while other paths demonstrate moderate or modest effects. The findings confirm ECM's robustness in generative AI settings and highlight the pivotal role of AI characteristics in shaping post-adoption evaluations and sustained use. These results contribute to the growing body of research on generative AI adoption in education by demonstrating how system intelligence and human-like interaction jointly influence continuance intention. The findings also offer practical guidance for AI developers to enhance system intelligence and natural interaction. Future research could explore how students experience AI over time and what shapes their sustained use using different research methods.
Keywords
Full Text:
PDFReferences
P. P. Ray, “ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet of Things and Cyber-Physical Systems, vol. 3, no. March, pp. 121–154, 2023, doi: 10.1016/j.iotcps.2023.04.003.
I. M. García-López, C. S. González González, M. S. Ramírez-Montoya, and J. M. Molina-Espinosa, “Challenges of implementing ChatGPT on education: Systematic literature review,” International Journal of Educational Research Open, vol. 8, no. October 2024, 2025, doi: 10.1016/j.ijedro.2024.100401.
B. Memarian and T. Doleck, “ChatGPT in education: Methods, potentials, and limitations,” Computers in Human Behavior: Artificial Humans, vol. 1, no. 2, p. 100022, 2023, doi: 10.1016/j.chbah.2023.100022.
H. Albayati, “Investigating undergraduate students’ perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study,” Computers and Education: Artificial Intelligence, vol. 6, no. January, p. 100203, 2024, doi: 10.1016/j.caeai.2024.100203.
R. A. M. Abdalla, “Examining awareness, social influence, and perceived enjoyment in the TAM framework as determinants of ChatGPT. Personalization as a moderator,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 10, no. 3, p. 100327, 2024, doi: 10.1016/j.joitmc.2024.100327.
M. A. Almulla, “Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective,” Heliyon, vol. 10, no. 11, p. e32220, 2024, doi: 10.1016/j.heliyon.2024.e32220.
A. Bhattacherjee, “UNDERSTANDING INFORMATION SYSTEMS CONTINUANCE: AN EXPECTATION-CONFIRMATION MODEL1 Motivation for the Study,” MIS Quarterly, vol. 25, no. 3, pp. 351–370, 2001.
C. D. Duong, T. H. Nguyen, T. V. N. Ngo, V. T. Dao, N. D. Do, and T. Van Pham, “Exploring higher education students’ continuance usage intention of ChatGPT: amalgamation of the information system success model and the stimulus-organism-response paradigm,” International Journal of Information and Learning Technology, 2024, doi: 10.1108/IJILT-01-2024-0006.
C. D. Duong, “Modeling the determinants of HEI students’ continuance intention to use ChatGPT for learning: a stimulus–organism–response approach,” Journal of Research in Innovative Teaching and Learning, vol. 17, no. 2, pp. 391–407, 2024, doi: 10.1108/JRIT-01-2024-0006.
F. B. Franque, T. Oliveira, and C. Tam, “Understanding the factors of mobile payment continuance intention: empirical test in an African context,” Heliyon, vol. 7, no. 8, p. e07807, 2021, doi: 10.1016/j.heliyon.2021.e07807.
J. C. Lee, Y. Tang, and S. Q. Jiang, “Understanding continuance intention of artificial intelligence (AI)-enabled mobile banking applications: an extension of AI characteristics to an expectation confirmation model,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, pp. 1–12, 2023, doi: 10.1057/s41599-023-01845-1.
S. S. Gill and R. Kaur, “ChatGPT: Vision and challenges,” Internet of Things and Cyber-Physical Systems, vol. 3, no. May, pp. 262–271, 2023, doi: 10.1016/j.iotcps.2023.05.004.
A. Zeb, R. Ullah, and R. Karim, “Exploring the role of ChatGPT in higher education: opportunities, challenges and ethical considerations,” International Journal of Information and Learning Technology, vol. 41, no. 1, pp. 99–111, 2024, doi: 10.1108/IJILT-04-2023-0046.
M. I. Baig and E. Yadegaridehkordi, “ChatGPT in the higher education: A systematic literature review and research challenges,” Int. J. Educ. Res., vol. 127, no. July, p. 102411, 2024, doi: 10.1016/j.ijer.2024.102411.
M. Javaid, A. Haleem, R. P. Singh, S. Khan, and I. H. Khan, “Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system,” BenchCouncil Transactions on Benchmarks, Standards and Evaluations, vol. 3, no. 2, p. 100115, 2023, doi: 10.1016/j.tbench.2023.100115.
J. Balakrishnan and Y. K. Dwivedi, “Conversational commerce: entering the next stage of AI-powered digital assistants,” Ann. Oper. Res., vol. 333, no. 2–3, pp. 653–687, Feb. 2024, doi: 10.1007/s10479-021-04049-5.
A. S. Almogren, W. M. Al-Rahmi, and N. A. Dahri, “Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective,” Heliyon, vol. 10, no. 11, p. e31887, 2024, doi: 10.1016/j.heliyon.2024.e31887.
C. Yu, J. Yan, and N. Cai, “ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention,” Front. Educ. (Lausanne)., vol. 9, no. May, pp. 1–11, 2024, doi: 10.3389/feduc.2024.1354929.
S. H. Alshammari and R. A. Alshammari, “An integration of expectation confirmation model and information systems success model to explore the factors affecting the continuous intention to utilise virtual classrooms,” Sci. Rep., vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-69401-8.
R. Pereira and C. Tam, “Impact of enjoyment on the usage continuance intention of video-on-demand services,” Information and Management, vol. 58, no. 7, p. 103501, 2021, doi: 10.1016/j.im.2021.103501.
D. Nan, S. Sun, S. Zhang, X. Zhao, and J. H. Kim, “Analyzing behavioral intentions toward Generative Artificial Intelligence: the case of ChatGPT,” Univers. Access Inf. Soc., vol. 24, no. 1, pp. 885–895, Mar. 2025, doi: 10.1007/s10209-024-01116-z.
R. S. Pasupuleti and D. Thiyyagura, “An empirical evidence on the continuance and recommendation intention of ChatGPT among higher education students in India: An extended technology continuance theory,” Educ. Inf. Technol. (Dordr)., vol. 29, no. 14, pp. 17965–17985, Oct. 2024, doi: 10.1007/s10639-024-12573-7.
C. N. L. Tan, M. Tee, and K. Y. Koay, “Discovering students’ continuous intentions to use ChatGPT in higher education: a tale of two theories,” Asian Education and Development Studies, vol. 13, no. 4, pp. 356–372, Aug. 2024, doi: 10.1108/AEDS-04-2024-0096.
S. R. Tummalapenta, R. S. Pasupuleti, R. M. Chebolu, T. V. Banala, and D. Thiyyagura, “Factors driving ChatGPT continuance intention among higher education students: integrating motivation, social dynamics, and technology adoption,” Journal of Computers in Education, 2024, doi: 10.1007/s40692-024-00343-w.
J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” European Business Review, vol. 31, no. 1, pp. 2–24, 2019, doi: 10.1108/EBR-11-2018-0203.
J. P. Andersen et al., “Generative Artificial Intelligence (GenAI) in the research process – A survey of researchers’ practices and perceptions,” Technol. Soc., vol. 81, Jun. 2025, doi: 10.1016/j.techsoc.2025.102813.
M. Khalifa and M. Albadawy, “Using artificial intelligence in academic writing and research: An essential productivity tool,” Jan. 01, 2024, Elsevier B.V. doi: 10.1016/j.cmpbup.2024.100145.
S. Moussawi, M. Koufaris, and R. Benbunan-Fich, “How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents,” Electronic Markets, vol. 31, no. 2, pp. 343–364, Jun. 2021, doi: 10.1007/s12525-020-00411-w.
S. Moussawi and M. Koufaris, “Perceived Intelligence and Perceived Anthropomorphism of Personal Intelligent Agents: Scale Development and Validation,” in Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS) 2019, Jan. 2019, pp. 115–124. [Online]. Available: https://hdl.handle.net/10125/59452
G. Malhotra and M. Ramalingam, “Perceived anthropomorphism and purchase intention using artificial intelligence technology: examining the moderated effect of trust,” Journal of Enterprise Information Management, vol. 38, no. 2, pp. 401–423, Feb. 2025, doi: 10.1108/JEIM-09-2022-0316.
V. Venkatesh and F. D. Davis, “Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies,” Manage. Sci., vol. 46, no. 2, pp. 186–204, 2000, doi: 10.1287/mnsc.46.2.186.11926.
J. F. Hair, M. Sarstedt, L. Hopkins, and V. G. Kuppelwieser, “Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research,” European Business Review, vol. 26, no. 2, pp. 106–121, 2014, doi: 10.1108/EBR-10-2013-0128.
J. Hair, C. L. Hollingsworth, A. B. Randolph, and A. Y. L. Chong, “An updated and expanded assessment of PLS-SEM in information systems research,” Industrial Management and Data Systems, vol. 117, no. 3, pp. 442–458, 2017, doi: 10.1108/IMDS-04-2016-0130.
J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, Jan. 2015, doi: 10.1007/s11747-014-0403-8.
L. D. Fornell C, “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” Journal of Marketing Research, vol. XVIII, pp. 39–50, Feb. 1981.
J. Hair and A. Alamer, “Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example,” Research Methods in Applied Linguistics, vol. 1, no. 3, Dec. 2022, doi: 10.1016/j.rmal.2022.100027.
G. Do Nguyen and T. H. T. Dao, “Factors influencing continuance intention to use mobile banking: an extended expectation-confirmation model with moderating role of trust,” Humanit. Soc. Sci. Commun., vol. 11, no. 1, Dec. 2024, doi: 10.1057/s41599-024-02778-z.
T. Hariguna, A. Ruangkanjanases, B. Bin Madon, and K. M. Alfawaz, “Assessing Determinants of Continuance Intention Toward Cryptocurrency Usage: Extending Expectation Confirmation Model With Technology Readiness,” Sage Open, vol. 13, no. 1, 2023, doi: 10.1177/21582440231160439.
D. T. Sasongko, P. W. Handayani, and R. Satria, “Analysis of factors affecting continuance use intention of the electronic money application in Indonesia,” Procedia Comput. Sci., vol. 197, no. 2021, pp. 42–50, 2021, doi: 10.1016/j.procs.2021.12.116.
X. Yu, Y. Yang, and S. Li, “Users’ continuance intention towards an AI painting application: An extended expectation confirmation model,” PLoS One, vol. 19, no. 5 May, May 2024, doi: 10.1371/journal.pone.0301821.
C. Pelau, D. C. Dabija, and I. Ene, “What makes an AI device human-like? The role of interaction quality, empathy and perceived psychological anthropomorphic characteristics in the acceptance of artificial intelligence in the service industry,” Comput. Human Behav., vol. 122, Sep. 2021, doi: 10.1016/j.chb.2021.106855.
M. A. Bhuiyan, M. K. Rahman, V. Basile, H. Ping, and A. B. M. Mainul Bari, “Adoption of ChatGPT for students’ learning effectiveness,” International Journal of Management Education, vol. 23, no. 3, Dec. 2025, doi: 10.1016/j.ijme.2025.101255.
M. Chelli et al., “Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews: Comparative Analysis,” J. Med. Internet Res., vol. 26, 2024, doi: 10.2196/53164.
Q. Ma, P. Crosthwaite, D. Sun, and D. Zou, “Exploring ChatGPT literacy in language education: A global perspective and comprehensive approach,” Computers and Education: Artificial Intelligence, vol. 7, Dec. 2024, doi: 10.1016/j.caeai.2024.100278.
C. Wang et al., “Exploring medical students’ intention to use of ChatGPT from a programming course: a grounded theory study in China,” BMC Med. Educ., vol. 25, no. 1, Dec. 2025, doi: 10.1186/s12909-025-06807-6.
A. Cvetkovic, I. Savolainen, M. Koike, and A. Oksanen, “A four-wave longitudinal study on attitudes toward the use of AI in different domains—The self-determination theory and locus of control perspective,” Telematics and Informatics Reports, vol. 18, Jun. 2025, doi: 10.1016/j.teler.2025.100220.
DOI: https://doi.org/10.47738/jads.v7i2.1291
Refbacks
- There are currently no refbacks.
Journal of Applied Data Sciences
| ISSN | : | 2723-6471 (Online) |
| Publisher | : | Bright Publisher |
| Website | : | http://bright-journal.org/JADS |
| : | taqwa@amikompurwokerto.ac.id (principal contact) | |
| support@bright-journal.org (technical issues) |
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0