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Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study
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This mixed-method study explores the acceptance of ChatGPT as a tool for Metacognitive Self-Regulated Learning (MSRL) among academics. Despite the growing attention towards ChatGPT as a metacognitive learning tool, there is a need for a comprehensive understanding of the factors influencing its acceptance in academic settings. Engaging 300 preservice teachers through a ChatGPT-based scenario learning activity and utilizing convenience sampling, this study administered a questionnaire based on the proposed Technology Acceptance Model at UTM University's School of Education. Structural equation modelling was applied to analyze participants' perspectives on ChatGPT, considering factors like MSRL's impact on usage intention. Post-reflection sessions, semi-structured interviews, and record analysis were conducted to gather results. Findings indicate a high acceptance of ChatGPT, significantly influenced by personal competency, social influence, perceived AI usefulness, enjoyment, trust, AI intelligence, positive attitude, and metacognitive self-regulated learning. Interviews and record analysis suggest that academics view ChatGPT positively as an educational tool, seeing it as a solution to challenges in teaching and learning processes. The study highlights ChatGPT's potential to enhance MSRL and holds implications for teacher education and AI integration in educational settings.
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“Similarly, the positive effects of SI are consistent with prior research indicating that social expectations, peer endorsement, and collective support can shape users’ acceptance of new technologies [61,103,104,105,106].”
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“Research confirms that ease of use significantly influences perceptions of usefulness, reinforcing the role of usability in technology adoption (Al-Adwan et al., 2024; Ateş & Yilmaz, 2024; Dahri et al., 2024; Venkatesh & Davis, 2000).”
AI misuse and passiveness of students in medical education
Hosseini, S. M. (2025) · AJP Advances in Physiology Education · doi:10.1152/advan.00164.2025 · Cited by 7
Bećirović, S., Augustín, M., Mattoš, B., & Dančo, J. (2026) · Discover Computing · doi:10.1007/s10791-026-10042-y · Cited by 7
Sanchez, J. M. P., Sumalinog, G. G., Mananay, J. A., Goles, C. E., Alejandro, I. M. V., & Fernandez, C. B. (2025) · STEM Education · doi:10.3934/steme.2025045 · Cited by 10
Khojasteh, L., Karimian, Z., Nasiri, E., Kafipour, R., & Farahmandi, A. Y. (2025) · BMC Medical Education · doi:10.1186/s12909-025-08288-z · Cited by 10
“Data analysis was conducted using SPSS software, version 26, for Exploratory Factor Analysis.”
Li, F., Chen, J., Shao, T., & Sun, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1756708 · Cited by 5
DeepL Translate can be a scaffold to speaking fluency: Technology Acceptance Model (TAM)
Imran, M. C., Marzuki, M., Sasabobe, L., Jubhari, Y., & Sujarwo, S. (2025) · EDULEC Education Language and Culture Journal · doi:10.56314/edulec.v5i1.302 · Cited by 5
Alsaedi, N. S. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/17302 · Cited by 4
The impact of DeepSeek’s perceived interactivity on medical students’ self-directed learning ability
Ju, Y., Li, J., Zhang, X., Wu, M., Pang, X., Li, Z., Wang, J., Li, J., Zhang, Y., & Dai, X. (2026) · Scientific Reports · doi:10.1038/s41598-025-33780-3 · Cited by 4
“Many surveyed educators stated they use AI technologies due to positive peer experiences 60 .”
Büyükeke, A. (2025) · Üniversite Araştırmaları Dergisi · doi:10.32329/uad.1728785 · Cited by 4
Munusamy, S., Rao, S., & Rajagopalan, V. (2026) · American Journal of Pharmaceutical Education · doi:10.1016/j.ajpe.2026.101943 · Cited by 5
Huang, F., & Zhang, H. (2026) · Applied Linguistics · doi:10.1093/applin/amag009 · Cited by 4
Examining the use of artificial intelligence in pre-service teacher education
Ponomarenko, E. B., Sergeeva, O. V., ZHELTUKHINA, M. R., Baranova, K. M., Budkevich, R. L., & Melnik, M. V. (2026) · Contemporary Educational Technology · doi:10.30935/cedtech/18458 · Cited by 5
Empowering Learners: GenIA and Their Impact on Self-regulated learning
Safsouf, Y., & Mouachi, R. (2024) · ITM Web of Conferences · doi:10.1051/itmconf/20246901004 · Cited by 4
“The study revealed a strong acceptance of GenAI chatbots among trainee teachers as tools to support self-regulated learning (SRL) in teaching [18].”
Predicting the actual use of artificial intelligence features of Apple Vision Pro using PLS-SEM
Al-Maroof, R. S., Tawafak, R. M., Al-Rahmi, W. M., Alhashmi, K. A., & Alyoussef, I. Y. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16208 · Cited by 4
Exploring educators’ perspectives on ChatGPT integration in engineering and management education
Bhaskar, P., Khan, M., & Bhaskar, P. (2025) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-04-2025-0088 · Cited by 5
AI in Tourism Education: A Review of AIGC Teaching and Evaluation Tools
Tan, Z., Yang, Y., Yang, X., Liu, X., Wei, Q., & Kong, L. H. (2025) · International Conference on Education and Training Technologies (ICETT) · doi:10.1109/icett66247.2025.11136881 · Cited by 3
“The integration of AI in education has transformed traditional teaching and learning paradigms through several key applications [33].”
AI-based career management to improve the quality of decision making in higher education
Fawaid, A., Baharun, H., Hamzah, M., Rohimah, R., Munawwaroh, I., & Putri, D. F. (2025) · IEEE Integrated STEM Education Conference (ISEC) · doi:10.1109/isec64801.2025.11147274 · Cited by 48
“With a system that can analyze data quickly and accurately, higher education can provide more targeted career recommendations for students, based on their abilities, interests, and relevant job market trends[27].”
Kılıç, E., Almasri, F., & Çelik, H. E. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100486 · Cited by 5
Lu, H., & Li, X. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-06392-5 · Cited by 3
“In terms of AI device adoption, Dahri et al. (2024) integrated factors such as perceived AI trust and perceived AI capability into the TAM to study ChatGPT usage behavioral intentions.”
Zahrani, A. A. (2025) · IEEE Access · doi:10.1109/access.2025.3647788 · Cited by 3
“For example, digital infrastructure has been shown to influence e-learning adoption [76], [77], cloud computing acceptance [78], [82], and AI integration in organizational [79].”
Metadata
- Authors
- Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & Soomro, R. B.
- Affiliations
- University of Technology Malaysia (MY)
- King Saud University (SA)
- Ural Federal University (RU)
- Corresponding author
- Dahri, Nisar Ahmed
- Yahaya, Noraffandy
- ORCID
- Venue
- Heliyon
- Publisher
- Elsevier BV
- Published
- 2024-04
- Volume
- 10
- Issue
- 8
- Pages
- e29317
- ISSN
- 2405-8440 (ISSN-L)
- PMC
- PMC11016976
- OpenAlex
- W4394611033
- Cited by
- 239
- References
- 113
- Citations by year
- 2026: 117 · 2025: 99 · 2024: 23
- FWCI
- 89.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Heliyon (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- King Saud University (RSP2024R159)
- Universiti Teknologi Malaysia (Q.J130000.21A2.07E10)
- Research Management Centre, Universiti Teknologi Malaysia
- Microsoft Research
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Education and Learning Interventions
- Keywords
- ChatGPT, technology acceptance, pre-service teachers, artificial intelligence in education, trust in AI, teacher professional development, AI integration in schools
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