Understanding Learner Intentions in AI-Enhanced Education: The Impact of Motivation and System Features
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AI-based learning systems have transformed education by offering personalized, flexible, and interactive learning experiences. However, understanding the behavioral tendencies of learners in such environments requires an integrated theoretical approach. This study applies the theory of planned behavior (TPB), the technology acceptance model (TAM), and motivation theories to examine how system characteristics—including perceived ease of use (PEOU), feedback capability, and self-control capability—influence learners' intrinsic and extrinsic motivations, which subsequently shape their behavioral intentions to engage with AI-based learning. To test the proposed model, data were collected from 242 college students who had participated in AI-based learning. Using SEM, the findings reveal that intrinsic and extrinsic motivations significantly influence learners' behavioral intentions, with intrinsic motivation having a stronger impact. PEOU is identified as a critical factor, positively affecting both intrinsic and extrinsic motivation. Additionally, feedback capability enhances intrinsic motivation more than extrinsic motivation, underscoring the importance of adaptive learning support. In contrast, self-control capability negatively affects intrinsic motivation, likely due to students' limited experience with the system and initial challenges in navigating AI-driven learning environments. By integrating psychological (TPB, motivation theories) and system-related (TAM) factors, this study advances the theoretical understanding of AI-based learning adoption and bridges the gap between system usability and learner engagement. The findings offer practical insights for educators, developers, and policymakers to design AI-based learning platforms that enhance motivation, user experience, and long-term participation.
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References
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control: From cognition to behavior (pp. 11-39). Springer.
Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human behavior and emerging technologies, 2(4), 314-324.
Alam, A. (2023). Harnessing the power of AI to create intelligent tutoring systems for enhanced classroom experience and improved learning outcomes. In Intelligent Communication Technologies and Virtual Mobile Networks (pp. 571-591). Springer.
Alamri, H., Lowell, V., Watson, W., & Watson, S. L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322-352.
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.
Aljawarneh, S. A. (2020). Reviewing and exploring innovative ubiquitous learning tools in higher education. Journal of computing in higher education, 32(1), 57-73.
Allouch, M., Azaria, A., & Azoulay, R. (2021). Conversational agents: Goals, technologies, vision and challenges. Sensors, 21(24), 8448.
Arun Kumar, U., Mahendran, G., & Gobhinath, S. (2022). A review on artificial intelligence based E-learning system. Pervasive Computing and Social Networking: Proceedings of ICPCSN 2022, 659-671.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94. https://doi.org/10.1007/BF02723327
Baker, R. S., Martin, T., & Rossi, L. M. (2016). Educational data mining and learning analytics. The Wiley handbook of cognition and assessment: Frameworks, methodologies, and applications, 379-396.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational behavior and human decision processes, 50(2), 248-287.
Banik, B. G., & Gullapelly, A. (2025). AI-Powered Gamification and Interactive Learning Tools for Enhancing Student Engagement. In Driving Quality Education Through AI and Data Science (pp. 283-310). IGI Global Scientific Publishing.
Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International journal of Research in Marketing, 13(2), 139-161.
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2), 238.
Boulay, B. d. (2019). Escape From the Skinner Box: The Case for Contemporary Intelligent Learning Environments. British Journal of Educational Technology, 50(6), 2902-2919. https://doi.org/10.1111/bjet.12860
Bozkurt, A., Karadeniz, A., Bañeres, D., & Rodríguez, M. E. (2021). Artificial Intelligence and Reflections From Educational Landscape: A Review of AI Studies in Half a Century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800
Cha, S., Loeser, M., & Seo, K. (2024). The Impact of AI-Based Course-Recommender System on Students’ Course-Selection Decision-Making Process. Applied Sciences, 14(9), 3672. https://doi.org/10.3390/app14093672
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161-182.
Chen, Z. (2023). Artificial intelligence-virtual trainer: Innovative didactics aimed at personalized training needs. Journal of the Knowledge Economy, 14(2), 2007-2025.
Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. MIS quarterly, 237-246.
Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 1-17.
Chou, C.-M., Shen, T.-C., Shen, T.-C., & Shen, C.-H. (2022). Influencing factors on students’ learning effectiveness of AI-based technology application: Mediation variable of the human-computer interaction experience. Education and information technologies, 27(6), 8723-8750.
Cudeck, R., Jöreskog, K. G., Sörbom, D., & Du Toit, S. (2001). Structural equation modeling: Present and future: A Festschrift in honor of Karl Jöreskog. Scientific Software International.
Dai, C.-P., & Ke, F. (2022). Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, 100087.
Daugherty, P. R., & Wilson, H. J. (2018). Human+ machine: Reimagining work in the age of AI. Harvard Business Press.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Delcker, J., Heil, J., Ifenthaler, D., Seufert, S., & Spirgi, L. (2024). First-Year Students AI-competence as a Predictor for Intended and De Facto Use of AI-tools for Supporting Learning Processes in Higher Education. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00452-7
Digel, S., Krause, T., & Biel, C. (2023). Enabling Individualized and Adaptive Learning–The Value of an AI-Based Recommender System for Users of Adult and Continuing Education Platforms. International Conference on Artificial Intelligence in Education,
Dimitriadou, E., & Lanitis, A. (2023). A Critical Evaluation, Challenges, and Future Perspectives of Using Artificial Intelligence and Emerging Technologies in Smart Classrooms. Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00231-3
Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15, 1-16.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. In: Sage publications Sage CA: Los Angeles, CA.
Govea, J., Ocampo Edye, E., Revelo-Tapia, S., & Villegas-Ch, W. (2023). Optimization and Scalability of Educational Platforms: Integration of Artificial Intelligence and Cloud Computing. Computers, 12(11), 223.
Gulfraz, M. B., Sufyan, M., Mustak, M., Salminen, J., & Srivastava, D. K. (2022). Understanding the impact of online customers’ shopping experience on online impulsive buying: A study on two leading E-commerce platforms. Journal of Retailing and Consumer Services, 68, 103000.
Hair, B., & Babin, A. (2022). Tatham.(2006). Multivariate data analysis. Aufl. Upper Saddle River, NJ.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis 6th Edition. In: Pearson Prentice Hall. New Jersey. humans: Critique and reformulation ….
Hair Joseph, F., Black William, C., Babin Barry, J., & Anderson Rolph, E. (2010). Multivariate data analysis: A global perspective. In: Upper Saddle River, NJ: Pearson/Prentice Hall.
Hecht, C. A., Grande, M. R., & Harackiewicz, J. M. (2021). The role of utility value in promoting interest development. Motivation science, 7(1), 1.
Hidayat-ur-Rehman, I. (2024). Examining AI Competence, Chatbot Use and Perceived Autonomy as Drivers of Students' Engagement In informal Digital Learning. Journal of Research in Innovative Teaching & Learning, 17(2), 196-212. https://doi.org/10.1108/jrit-05-2024-0136
Hirvonen, N., Jylhä, V., Lao, Y., & Larsson, S. (2023). Artificial intelligence in the information ecosystem: Affordances for everyday information seeking. Journal of the Association for Information Science and Technology.
Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning—Are they really useful? A systematic review of chatbot‐supported language learning. Journal of Computer Assisted Learning, 38(1), 237-257.
Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of management information systems, 11(4), 87-114.
Jafari, F., & Keykha, A. (2023). Identifying the Opportunities And challenges of Artificial Intelligence in Higher Education: A qualitative Study. Journal of Applied Research in Higher Education, 16(4), 1228-1245. https://doi.org/10.1108/jarhe-09-2023-0426
Jaiswal, A., Arun, C. J., & Varma, A. (2023). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. In Artificial Intelligence and International HRM (pp. 114-143). Routledge.
Jiao, J., & Cao, X. (2024). Research on designers’ behavioral intention toward Artificial Intelligence-Aided Design: integrating the Theory of Planned Behavior and the Technology Acceptance Model. Frontiers in Psychology, 15, 1450717.
Kumar, P., Vrontis, D., & Pallonetto, F. (2023). Cognitive Engagement With AI‐enabled Technologies and Value Creation in Healthcare. Journal of Consumer Behaviour, 23(2), 389-404. https://doi.org/10.1002/cb.2196
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, 100178.
Liao, X., Zhang, X., Wang, Z., & Luo, H. (2024). Design and implementation of an AI‐enabled visual report tool as formative assessment to promote learning achievement and self‐regulated learning: An experimental study. British Journal of Educational Technology, 55(3), 1253-1276.
Lou, C., Kang, H., & Tse, C. H. (2022). Bots vs. humans: how schema congruity, contingency-based interactivity, and sympathy influence consumer perceptions and patronage intentions. International Journal of Advertising, 41(4), 655-684.
Mallik, S., & Gangopadhyay, A. (2023). Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1151391
Malodia, S., Islam, N., Kaur, P., & Dhir, A. (2021). Why do people use Artificial Intelligence (AI)-enabled voice assistants? IEEE Transactions on engineering management, 71, 491-505.
Meena, R., & Sarabhai, S. (2023). Extrinsic and intrinsic motivators for usage continuance of hedonic mobile apps. Journal of Retailing and Consumer Services, 71, 103228.
Molenaar, I., Horvers, A., & Dijkstra, R. (2019). Young Learners’ Regulation of Practice Behavior in Adaptive Learning Technologies. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.02792
Morris, L. S., Grehl, M. M., Rutter, S. B., Mehta, M., & Westwater, M. L. (2022). On what motivates us: a detailed review of intrinsic v. extrinsic motivation. Psychological medicine, 52(10), 1801-1816.
Ni, A., & Cheung, A. (2023). Understanding secondary students’ continuance intention to adopt AI-powered intelligent tutoring system for English learning. Education and Information Technologies, 28(3), 3191-3216.
Ning, Y., Zhang, C., Xu, B., Zhou, Y., & Wijaya, T. T. (2024). Teachers’ AI-TPACK: Exploring the Relationship Between Knowledge Elements. Sustainability, 16(3), 978. https://doi.org/10.3390/su16030978
Nunnally, J. C. (1978). An overview of psychological measurement. Clinical diagnosis of mental disorders: A handbook, 97-146.
Park, Y. J., & Jones-Jang, S. M. (2023). Surveillance, security, and AI as technological acceptance. AI & society, 38(6), 2667-2678.
Pi, Z., Chai, H., Li, L., Zhang, X., & Li, X. (2023). High Task Motivation Learners Co‐viewing Video Lectures Facilitates Learning. Journal of Computer Assisted Learning, 40(2), 588-599. https://doi.org/10.1111/jcal.12901
Qiao, H., & Zhao, A. (2023). Artificial Intelligence-Based Language Learning: Illuminating the Impact on Speaking Skills and Self-Regulation in Chinese EFL Context. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1255594
Qin, F., Li, K., & Jian-yuan, Y. (2020). Understanding User Trust in Artificial Intelligence‐based Educational Systems: Evidence From China. British Journal of Educational Technology, 51(5), 1693-1710. https://doi.org/10.1111/bjet.12994
Rapanta, C., Botturi, L., Goodyear, P., Ortiz, L. G., & Koole, M. (2020). Online University Teaching During and After the Covid-19 Crisis: Refocusing Teacher Presence and Learning Activity. Postdigital Science and Education, 2(3), 923-945. https://doi.org/10.1007/s42438-020-00155-y
Rigdon, E. E. (1995). A necessary and sufficient identification rule for structural models estimated in practice. Multivariate behavioral research, 30(3), 359-383.
Rotter, J. B. (1975). Some problems and misconceptions related to the construct of internal versus external control of reinforcement. Journal of consulting and clinical psychology, 43(1), 56.
Ryan, R. M., & Deci, E. L. (2024). Self-determination theory. In Encyclopedia of quality of life and well-being research (pp. 6229-6235). Springer.
Saidakhror, G. (2024). The Impact of Artificial Intelligence on Higher Education and the Economics of Information Technology. 2(3), 1-6. https://doi.org/10.59022/ijlp.125
Saqr, R. R., Al-Somali, S. A., & Sarhan, M. Y. (2023). Exploring the acceptance and user satisfaction of AI-driven e-learning platforms (Blackboard, Moodle, Edmodo, Coursera and edX): an integrated technology model. Sustainability, 16(1), 204.
Shahzad, M. F., Xu, S., & Zahid, H. (2024). Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education. Education and Information Technologies, 1-26.
Shi, Y., Zhou, Y., Wen, D., Wu, Y., Jiang, C., & Letaief, K. B. (2023). Task-Oriented Communications for 6G: Vision, Principles, and Technologies. https://doi.org/10.48550/arxiv.2303.10920
Silitonga, L. M., Hawanti, S., Aziez, F., Furqon, M., Zain, D. S. M., Anjarani, S., & Wu, T.-T. (2023). The impact of AI chatbot-based learning on students’ motivation in english writing classroom. International Conference on Innovative Technologies and Learning,
Sweller, J. (2020). Cognitive load theory and educational technology. Educational technology research and development, 68(1), 1-16.
Tahir, M. A., Da, G., Javed, M., Akhtar, M. W., & Wang, X. (2024). Employees’ foe or friend: artificial intelligence and employee outcomes. The Service Industries Journal, 1-32.
Teng, L. S. (2021). Individual Differences in Self-Regulated Learning: Exploring the Nexus of Motivational Beliefs, Self-Efficacy, and SRL Strategies in EFL Writing. Language Teaching Research, 28(2), 366-388. https://doi.org/10.1177/13621688211006881
Vahlo, J., Tuuri, K., & Välisalo, T. (2022). Exploring gameful motivation of autonomous learners. Frontiers in Psychology, 13, 825840.
Vistorte, A. O. R., Deroncele-Acosta, A., Ayala, J. L. M., Barrasa, A., López-Granero, C., & Martí-González, M. (2024). Integrating Artificial Intelligence to Assess Emotions in Learning Environments: A Systematic Literature Review. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1387089
Wamba-Taguimdje, S.-L., Wamba, S. F., Kamdjoug, J. R. K., & Wanko, C. E. T. (2020). Influence of Artificial Intelligence (AI) on Firm Performance: The Business Value of AI-based Transformation Projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/bpmj-10-2019-0411
Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2024). Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy. International Journal of Human–Computer Interaction, 1-23.
Wang, R., Bush-Evans, R., Arden-Close, E., Bolat, E., McAlaney, J., Hodge, S., Thomas, S., & Phalp, K. (2023). Transparency in persuasive technology, immersive technology, and online marketing: Facilitating users’ informed decision making and practical implications. Computers in human behavior, 139, 107545.
Wang, S., Wang, H., Jiang, Y., Li, P., & Yang, W. (2023). Understanding students’ participation of intelligent teaching: an empirical study considering artificial intelligence usefulness, interactive reward, satisfaction, university support and enjoyment. Interactive Learning Environments, 31(9), 5633-5649.
Wang, Y.-Y., & Chuang, Y.-W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29(4), 4785-4808.
Weiner, B. (1974). Motivational psychology and educational research. Educational Psychologist, 11(2), 96-101.
Wong, G.-Z., Wong, K.-H., Lau, T.-C., Lee, J.-H., & Kok, Y.-H. (2024). Study of intention to use renewable energy technology in Malaysia using TAM and TPB. Renewable Energy, 221, 119787.
Yongli, G., Qi, D., & Zhipeng, C. (2024). Leveraging the Synergy of IPv6, Generative AI, and Web Engineering to Create a Big Data-Driven Education Platform. Journal of Web Engineering, 197-226. https://doi.org/10.13052/jwe1540-9589.2321
Yu, X., Xu, S., & Ashton, M. (2023). Antecedents and outcomes of artificial intelligence adoption and application in the workplace: the socio-technical system theory perspective. Information Technology & People, 36(1), 454-474.
Yurt, E., & Kasarci, I. (2024). A Questionnaire of Artificial Intelligence Use Motives: A contribution to investigating the connection between AI and motivation. International Journal of Technology in Education, 7(2).
Zimmerman, K. N., Torelli, J. N., & Chow, J. C. (2022). Planning positive reinforcement cycles in behavior intervention plans. Behavior analysis in practice, 15(3), 924-937.