Students’ Attitudes Toward Generative AI as a Mediating Pathway to Perceived Generative AI Support for Self-Regulated Learning in Higher Education
DOI:
https://doi.org/10.7160/eriesj.2026.190302Keywords:
Generative artificial intelligence, higher education, responsible learning, self-regulated learning, student attitudes, technology acceptanceAbstract
Generative artificial intelligence (GAI) is increasingly integrated into higher education, creating opportunities for flexible and personalised learning while raising questions about AI-supported learning. This study examines whether students’ attitudes toward GAI mediate the relationships between perceived ease of use (PEU), perceived usefulness (PU), and perceived Generative AI support for self-regulated learning (GAI-SRL). A cross-sectional survey was conducted with 264 undergraduate students at a public university in Indonesia. Data were analysed using partial least squares structural equation modelling (PLS-SEM). PEU and PU were positively associated with attitudes toward GAI, while attitudes were positively associated with perceived GAI support for self-regulated learning. PEU and PU also showed significant direct relationships with GAI-SRL, and their indirect relationships through attitudes were significant, indicating partial statistical mediation. These findings suggest that students’ attitudes toward GAI constitute an intermediate statistical pathway linking technology-related perceptions with perceived GAI support for self-regulated learning. The study extends technology acceptance research by positioning attitudes toward GAI within a learning-oriented framework and highlights responsible, learner-centred use of GAI while preserving students’ learning agency.
References
Acosta-Enriquez, B. G., Arbulú Pérez Vargas, C. G., Huamaní Jordan, O., Arbulú Ballesteros, M. A. and Paredes Morales, A. E. (2024) ‘Exploring attitudes toward ChatGPT among college students: An empirical analysis of cognitive, affective, and behavioral components using path analysis’, Computers and Education: Artificial Intelligence, Vol. 7, p. 100320. https://doi.org/10.1016/j.caeai.2024.100320
Akbay, B., Karakoç Topal, Ö. and Bostan Sarıoğlan, A. (2025) ‘Artificial Intelligence Literacy and Anxiety Levels of Pre-service Science Teachers: A Mixed Method Study’, Journal on Efficiency and Responsibility in Education and Science, Vol. 18, No. 4, pp. 318–333. https://doi.org/10.7160/eriesj.2025.180407
Asio, J. M. R. (2024) ‘AI Literacy, Self-Efficacy, and Self-Competence among College Students: Variances and Interrelationships among Variables’, Malaysian Online Journal of Educational Sciences (MOJES), Vol. 12, No. 3, pp. 44–60. https://doi.org/10.22452/aldad.vol12no3.4
Banihashem, S. K., Kerman, N. T., Noroozi, O., Moon, J. and Drachsler, H. (2024) ‘Feedback sources in essay writing: peer-generated or AI-generated feedback?’, International Journal of Educational Technology in Higher Education, Vol. 21, No. 1. https://doi.org/10.1186/s41239-024-00455-4
Buabeng-Andoh, C. (2025) ‘Investigating student–teachers’ continuous intention to use mobile learning management system: the technology acceptance model and expectation confirmatory model’, Discover Education, Vol. 4, No. 1, p. 76. https://doi.org/10.1007/s44217-025-00447-0
Bueno-Vesga, J. A., Xu, X., Gu, Y., He, H., Li, S. and Duan, Y. (2026) ‘The Role of Cognitive Load and Engagement in Students’ Adoption of an AI-supported VR Training System’, IEEE Transactions on Visualization and Computer Graphics, Vol. 32, No. 5, pp. 4617–4625. https://doi.org/10.1109/TVCG.2026.3680684
Chang, D. H., Lin, M. P. C., Hajian, S. and Wang, Q. Q. (2023) ‘Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization’, Sustainability (Switzerland), Vol. 15, No. 17, p. 12921 https://doi.org/10.3390/su151712921
Chatzopoulos, A. (2026) ‘Using generative AI at the university: Do ChatGPT and other AI tools promote computational thinking?’, in Kalogiannakis, M. and Sotiropoulos, D. (eds.) Generative AI-enhanced science education from early childhood to higher education, Hershey, PA: IGI Global Scientific Publishing, pp. 155–240. https://doi.org/10.4018/979-8-3373-9074-1.ch005
Chauncey, S. A. and McKenna, H. P. (2023) ‘A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning’, Computers and Education: Artificial Intelligence, Vol. 5, p. 100182. https://doi.org/10.1016/j.caeai.2023.100182
Chiu, T. K. F. (2024) ‘A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: a case of ChatGPT’, Educational Technology Research and Development, Vol. 72, No. 4, pp. 2401–2416. https://doi.org/10.1007/s11423-024-10366-w
Choi, G. W., Kim, S. H., Lee, D. and Moon, J. (2024) ‘Utilizing Generative AI for Instructional Design: Exploring Strengths, Weaknesses, Opportunities, and Threats’, TechTrends, Vol. 68, No. 4, pp. 832–844. https://doi.org/10.1007/s11528-024-00967-w
Creswell, J. W. (2012) Educational research: Planning, conducting, and evaluating quantitative and qualitative research, 4th edition, Boston, MA: Pearson.
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A. and Soomro, R. B. (2024) ‘Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study’, Heliyon, Vol. 10, No. 8, p. e29317. https://doi.org/10.1016/j.heliyon.2024.e29317
Ding, J. and Song, Y. (2026) ‘Descriptive writing using generative AI as a cognitive scaffold in the metaverse environment: university students’ perceptions, learning engagement, and performance’, Educational Psychology, Vol. 46, No. 1, pp. 183–207. https://doi.org/10.1080/01443410.2025.2568189
EL Hosayny, O., Razkane, H. and Yeou, M. (2026) ‘Metacognitive regulation and ChatGPT-assisted writing: introducing an interactive model for cultivating strategic engagement’, Discover Education, Vol. 5, No. 1, p. 246. https://doi.org/10.1007/s44217-026-01282-7
Fitriati, S. W. and Williyan, A. (2025) ‘AI-enhanced self-regulated learning: EFL learners’ prioritization and utilization in presentation skills development’, Journal of Pedagogical Research, Vol. 9, No. 2, pp. 22–37. https://doi.org/10.33902/JPR.202530647
Ge, Z.-G. (2026) ‘The mechanisms of AI feedback’s influence on college students’ learning engagement in human-AI collaborative learning’, Innovations in Education and Teaching International, pp. 1–14. https://doi.org/10.1080/14703297.2026.2620543
Guan, L., Li, S. and Gu, M. M. (2024) ‘AI in informal digital English learning: A meta-analysis of its effectiveness on proficiency, motivation, and self-regulation’, Computers and Education: Artificial Intelligence, Vol. 7, p. 100323. https://doi.org/10.1016/j.caeai.2024.100323
Hair, J. F., Howard, M. C. and Nitzl, C. (2020) ‘Assessing measurement model quality in PLS-SEM using confirmatory composite analysis’, Journal of Business Research, Vol. 109, pp. 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Risher, J. J., Sarstedt, M. and Ringle, C. M. (2019) ‘When to use and how to report the results of PLS-SEM’, In European Business Review, Vol. 31, No. 1, pp. 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hou, C., Zhu, G., Liu, Y., Sudarshan, V., Chong, J. L. L., Zhang, F. Y., Tan, M. Y. H. and Ong, Y. S. (2026) ‘The effects of critical thinking intervention on reliance behaviors, problem-solving quality, and creativity during human-Generative AI collaborative learning’, Computers and Education, Vol. 247, p. 105576. https://doi.org/10.1016/j.compedu.2026.105576
Hwang, M., Lee, E. and Lee, H. K. (2025) ‘Exploring EFL Learners’ Acceptance of ChatGPT: Application of the Extended Technology Acceptance Model’, English Teaching (South Korea), Vol. 80, No. 1, p. 45–69. https://doi.org/10.15858/engtea.80.1.202503.45
Jiang, Y., Wang, P., Li, Q. and Li, Y. (2022) ‘Students’ Intention toward Self-Regulated Learning under Blended Learning Setting: PLS-SEM Approach’, Sustainability (Switzerland), Vol. 14, No. 16, p. 10140. https://doi.org/10.3390/su141610140
Joo, S., Han, I. and Park, I. (2026) ‘The effect of metacognitive prompts using generative AI on cognitive load, task performance, and self-efficacy in online self-regulated learning’, Educational Psychology, pp. 1–24. https://doi.org/10.1080/01443410.2026.2618717
Karan, B. and Chakma, C. (2025) ‘Influence of higher education students’ perceived behaviour on their artificial intelligence acceptance: an empirical investigation using technology acceptance model’, Journal of Applied Research in Higher Education., Vol. 18, No. 3, pp. 745–760. https://doi.org/10.1108/JARHE-11-2023-0535
Kim, J. H., Moldavan, A. M., Terry, R. and Massey, C. C. (2025) ‘Enhancing student engagement with generative AI’, in Kyei-Blankson, L. and Ntuli, E. (eds.) Transformative AI practices for personalized learning strategies, Hershey, PA: IGI Global Scientific Publishing, pp. 69–106. https://doi.org/10.4018/979-8-3693-8744-3.ch004
Kim, Y. and Thille, C. (2026) ‘Personalized learning experiences with AI’, in Ives, K. S., Cini, M. and Schroeder, R. (eds.) AI applications in online higher education administration: Strategies for maximizing returns and improving outcomes, New York: Routledge, pp. 75–87. https://doi.org/10.4324/9781003586388-10
Kizilcec, R. F., Huber, E., Papanastasiou, E. C., Cram, A., Makridis, C. A., Smolansky, A., Zeivots, S. and Raduescu, C. (2024) ‘Perceived impact of generative AI on assessments: Comparing educator and student perspectives in Australia, Cyprus, and the United States’, Computers and Education: Artificial Intelligence, Vol. 7, p. 100269. https://doi.org/10.1016/j.caeai.2024.100269
Lai, J. W. (2024) ‘Adapting Self-Regulated Learning in an Age of Generative Artificial Intelligence Chatbots’, Future Internet, Vol. 16, No. 6, p. 218. https://doi.org/10.3390/fi16060218
Lee, A. T., Ramasamy, R. K. and Subbarao, A. (2025) ‘Understanding psychosocial barriers to healthcare technology adoption: A review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT frameworks’, Healthcare, Vol. 13, No. 3, p. 250. https://doi.org/10.3390/healthcare13030250
Li, Y., He, X., Li, Y., Xiao, X., Li, R., Fang, J. and Zhou, R. (2025) ‘Exploring dynamics in learners’ cognitive engagement associated with learning activities using EEG microstate approach’, Thinking Skills and Creativity, Vol. 58, p. 101911. https://doi.org/10.1016/j.tsc.2025.101911
Madrid Akpovo, S. (2026) ‘Generative AI in the intercultural learning process: An exploration of its usefulness in university virtual exchange programs’, in Dervin, F. and R’boul, H. (eds.) An introduction to AI and intercultural communication education, New York: Routledge, pp. 64–76. https://doi.org/10.4324/9781003675983-7
Mahniza, M., Sari, R. E., Suci, P. H., Saputra, I. and Putri, E. Y. (2024) ‘AI-Driven Learning: Mediating and Moderating Dynamics in Self-Regulated Learning’, Journal of Educational Science and Technology (EST), Vol. 10, No. 3, p. 229. https://doi.org/10.26858/est.v10i3.68254
Melanou, C. and Beege, M. (2026) ‘Scaffolding Generative AI as a Tutor: A Quasi-Experimental Study of Learning Outcomes and Motivational, Cognitive and Metacognitive Processes’, Education Sciences, Vol. 16, No. 4, p. 651. https://doi.org/10.3390/educsci16040651
Molenaar, I. (2022) ‘The concept of hybrid human-A regulation: Exemplifying how to support young learners’ self-regulated learning’, Computers and Education: Artificial Intelligence, Vol. 3, p. 100070. https://doi.org/10.1016/j.caeai.2022.100070
Moya, S. and Camacho, M. (2024) ‘Leveraging AI-powered mobile learning: A pedagogically informed framework’, Computers and Education: Artificial Intelligence, Vol. 7, p. 100276. https://doi.org/10.1016/j.caeai.2024.100276
Mustofa, R. H., Kuncoro, T. G., Atmono, D., Hermawan, H. D. and Sukirman. (2025) ‘Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among students’, Computers and Education: Artificial Intelligence, Vol. 8, p. 100379. https://doi.org/10.1016/j.caeai.2025.100379
Nilson, L. B. (2023) Creating self-regulated learners: Strategies to strengthen students’ self-awareness and learning skills, New York: Routledge. https://doi.org/10.4324/9781003443803
Ozili, P. K. (2025) ‘Technology impact model: a transition from the technology acceptance model’, AI and Society, Vol. 40, No. 2, p. 1–3. https://doi.org/10.1007/s00146-024-01896-1
Park, J. H., Lee, C. W. and Do, C. (2025) ‘Examining Users’ Acceptance Intention of Health Applications Based on the Technology Acceptance Model’, Healthcare (Switzerland), Vol. 13, No. 6, p. 596. https://doi.org/10.3390/healthcare13060596
Polit, D. F. and Beck, C. T. (2006) ‘The Content Validity Index: Are You Sure You Know What’s Being Reported? Critique and Recommendations’, Research in Nursing and Health, Vol. 29, No. 5, pp. 489–497. https://doi.org/10.1002/nur.20147
Rahimi, A. R. and Faskhodi, A. A. (2026) ‘Shaping AI-driven language learners’ learning approaches, and motivation through computational thinking skills in computer-assisted language learning (CTCALL): A bisymmetric research design’, Smart Learning Environments, Vol. 13, No. 1, p. 27. https://doi.org/10.1186/s40561-026-00451-3
Runge, I., Hebibi, F. and Lazarides, R. (2025) ‘Acceptance of Pre-Service Teachers Towards Artificial Intelligence (AI): The Role of AI-Related Teacher Training Courses and AI-TPACK Within the Technology Acceptance Model’, Education Sciences, Vol. 15, No. 2, p. 167. https://doi.org/10.3390/educsci15020167
Saqr, M., Cheng, R., López-Pernas, S. and Beck, E. D. (2024) ‘Idiographic artificial intelligence to explain students’ self-regulation: Toward precision education’, Learning and Individual Differences, Vol. 114, p. 102499. https://doi.org/10.1016/j.lindif.2024.102499
Sardi, J., Darmansyah, Candra, O., Yuliana, D. F., Habibullah, Yanto, D. T. P. and Eliza, F. (2025) ‘How Generative AI Influences Students’ Self-Regulated Learning and Critical Thinking Skills? A Systematic Review’, International Journal of Engineering Pedagogy, Vol. 15, No. 1, pp. 94–108. https://doi.org/10.3991/ijep.v15i1.53379
Scherer, R. (2025) ‘Is the Technology Acceptance Model just old wine in new wineskins? Exploring issues for further model development’, In Journal of University Teaching and Learning Practice, Vol. 21, No. 08. https://doi.org/10.53761/var55x96
Schunk, D. H. and Zimmerman, B. J. (eds.) (2012) Motivation and self-regulated learning: Theory, research, and applications, New York: Routledge. https://doi.org/10.4324/9780203831076
Shahzad, M. F., Xu, S. and Asif, M. (2025) ‘Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model’, British Educational Research Journal, Vol. 51, No. 2, pp. 489–513. https://doi.org/10.1002/berj.4084
Svenningsson, J., Höst, G., Hultén, M. and Hallström, J. (2022) ‘Students’ attitudes toward technology: exploring the relationship among affective, cognitive and behavioral components of the attitude construct’, International Journal of Technology and Design Education, Vol. 32, No. 3, pp. 1531–1551. https://doi.org/10.1007/s10798-021-09657-7
Tang, T., Sha, J., Zhao, Y., Wang, S., Wang, Z. and Shen, S. (2024) ‘Unveiling the efficacy of ChatGPT in evaluating critical thinking skills through peer feedback analysis: Leveraging existing classification criteria’, Thinking Skills and Creativity, Vol. 53, p. 101607. https://doi.org/10.1016/j.tsc.2024.101607
Tariq, M. U. (2025a) ‘AI-driven personalized learning: Revolutionizing student engagement and teacher efficiency in Education 5.0’, in Mobo, F. D. (ed.) AI and emerging technologies for emergency response and smart cities, Hershey, PA: IGI Global Scientific Publishing, pp. 75–96. https://doi.org/10.4018/979-8-3693-9770-1.ch004
Tariq, M. U. (2025b) ‘AI-enhanced project-based learning: Revolutionizing commerce education with generative AI’, in ElSayary, A. (ed.) Prompt engineering and generative AI applications for teaching and learning, Hershey, PA: IGI Global Scientific Publishing, pp. 125–142. https://doi.org/10.4018/979-8-3693-7332-3.ch008
van den Berg, G. (2024) ‘Generative AI and Educators: Partnering in Using Open Digital Content for Transforming Education’, Open Praxis, Vol. 16, No. 2, pp. 130–141. https://doi.org/10.55982/openpraxis.16.2.640
Wang, C., Li, X. and Zou, B. (2025) ‘Revisiting Integrated Model of Technology Acceptance Among the Generative AI-Powered Foreign Language Speaking Practice: Through the Lens of Positive Psychology and Intrinsic Motivation’, European Journal of Education, Vol. 60, No. 1. https://doi.org/10.1111/ejed.70054
Wang, Y. (2024) ‘Cognitive and sociocultural dynamics of self-regulated use of machine translation and generative AI tools in academic EFL writing’, System, Vol. 126, p. 103505. https://doi.org/10.1016/j.system.2024.103505
Weng, X., Xia, Q., Ahmad, Z. and Chiu, T. K. F. (2024) ‘Personality traits for self-regulated learning with generative artificial intelligence: The case of ChatGPT’, Computers and Education: Artificial Intelligence, Vol. 7, p. 100315. https://doi.org/10.1016/j.caeai.2024.100315
Xia, Q., Chiu, T. K. F., Chai, C. S. and Xie, K. (2023) ‘The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot’, British Journal of Educational Technology, Vol. 54, No. 4, pp. 967–986. https://doi.org/10.1111/bjet.13305
Xiaolei, S. and Teng, M. F. (2024) ‘Three-wave cross-lagged model on the correlations between critical thinking skills, self-directed learning competency and AI-assisted writing’, Thinking Skills and Creativity, Vol. 52, p. 101524. https://doi.org/10.1016/j.tsc.2024.101524
Yanto, D. T. P., Ganefri, Hastuti, Candra, O., Kabatiah, M., Andrian and Zaswita, H. (2023) ‘The Affecting Factors of Students’ Attitudes Toward the Use of a Virtual Laboratory: A Study in Industrial Electrical Engineering’, International Journal of Online and Biomedical Engineering (IJOE), Vol. 19, No. 13, pp. 4–16. https://doi.org/10.3991/ijoe.v19i13.41219
Zhai, C., Wibowo, S. and Li, L. D. (2024) ‘The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review’, Smart Learning Environments, Vol. 11, No. 1. https://doi.org/10.1186/s40561-024-00316-7
Zhang, L. and Chen, S. C. (2026) ‘Towards human-AI collaborative learning: synergizing self-regulation and artificial-intelligence literacy’, Education and Information Technologies, Vol. 31, No. 7, pp 1879–1907. https://doi.org/10.1007/s10639-025-13880-3
Zhang, S., Zhao, X., Zhou, T. and Kim, J. H. (2024) ‘Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior’, International Journal of Educational Technology in Higher Education, Vol. 21, No. 1. https://doi.org/10.1186/s41239-024-00467-0
Zimmerman, B. J. (2013) ‘Theories of self-regulated learning and academic achievement: An overview and analysis’, in Zimmerman, B. J. and Schunk, D. H. (eds.) Self-regulated learning and academic achievement: Theoretical perspectives, 2nd edition, New York: Routledge, pp. 1–35. https://doi.org/10.4324/9781410601032-1
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Juli Sardi, Darmansyah, Oriza Candra, Nuzul Hidayat, Devi Faizah Yuliana
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors declare with this manuscript intended for publication to ERIES Journal that:
- all co-authors agree with the publication of the manuscript even after amendments arising from peer review;
- all co-authors agree with the posting of the full text of this work on the web page of ERIES Journal and to the inclusion of references in databases accessible on the internet;
- no results of other researchers were used in the submitted manuscript without their consent, proper citation, or acknowledgement of their cooperation or material provided;
- the results (or any part of them) used in the manuscript have not been sent for publication to any other journal nor have they already been published (or if so, that the relevant works are cited in this manuscript);
- submission of the manuscript for publication was completed in accordance with the publishing regulations pertaining to place of work;
- experiments performed comply with current laws and written consent of the Scientific Ethics Committee / National Animal Care Authority (as is mentioned in the manuscript submitted);
- grant holders confirm that they have been informed of the submitted manuscript and they agree to its publication.
Authors retain copyright and grant ERIES Journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the published work with an acknowledgement of its initial publication in ERIES Journal. Moreover, authors are able to post the published work in an institutional repository with an acknowledgement of its initial publication in ERIES Journal. In addition, authors are permitted and encouraged to post the published work online (e.g. institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
