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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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1 retracted
Wang, C., Ahmad, S. F., Ahmad, A. Y. A. B., Awwad, E. M., Irshad, M., Ali, Y. A., Al‐Razgan, M., Khan, Y., & Han, H. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18349 · Cited by 297
RetractedUsing thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190 · cited 4×
“These collective findings affirm that these constructs effectively measure distinct latent variables, ensuring the absence of multicollinearity or overlapping influences [125 – 127]. technology for educational purposes.”
BackgroundMethodResultInfluentialPerceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · cited 3×
“Empirical support for this concept is evident in Dent and Koenka ’ s [19] meta-analysis of 61 studies, where measures of metacognitive processes exhibited stronger correlations with achievement compared to measures of cognitive strategy utilization.”
BackgroundA new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
(1975) · PhilPapers (PhilPapers Foundation) · Cited by 36,737
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541 · cited 5×
“These values surpass the recommended threshold of 0.7, affirming strong internal consistency [102,123].”
BackgroundInfluentialUser Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709 · cited 3×
“The model explains that PU and PEOU shape individuals ’ attitudes and both PU and attitudes toward technology usage predict BI [46,60].”
BackgroundMethodPartial least squares structural equation modeling (PLS-SEM): An emerging tool in business research
F. Hair Jr, J., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014) · European Business Review · doi:10.1108/ebr-10-2013-0128 · Cited by 11,728 · cited 2×
“This was done per the well-established two-step approach suggested by other research [116,115,117,118].”
BackgroundMethodComparison of convenience sampling and purposive sampling
Etikan, I. (2016) · American Journal of Theoretical and Applied Statistics · doi:10.11648/j.ajtas.20160501.11 · Cited by 11,412 · cited 4×
“[110,111].”
MethodResultInfluential
Research works printed but not linked: 10
- Jiang, 2016, Assessing teachers' metacognition in teaching: the teacher metacognition inventory, Teach, Teach. Educ., 59, 403
- Lai, 2011, Metacognition: a literature review, always learn, Pearson Res. Rep., 24, 1
- Bran, 2011, Metacognitive regulation and in-depth learning. A study on the students preparing to become teachers, Procedia-Social, Behav. Sci., 11, 107
- Muijs D., Bokhove C., Metacognition and self-regulation: evidence review, Educ. Endow. Found. (2020). report 2020.
- Altwairesh, 2021, Mobile payments from merchants' perspective: an empirical study using the TAM model in Saudi Arabia, Int. J. Comput. Sci. Netw. Secur., 21, 317
- McAuley, 1985, Modeling and self-efficacy: a test of Bandura's model, J. Sport Exerc, Psychol., 7, 283
- Lee, 2023, 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., 10, 1
- Alshenqeeti, 2014, Interviewing as a data collection method: a critical review, English Linguist, Res., 3, 39
- Hair, 2006, vol. 6
- Morris, 2000, Age differences in technology adoption decisions: implications for a changing work force, Pers, Psychol., 53, 375
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Items 101–200 of 239
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Xia, Q., Yang, Y., Weng, X., Cheng, W. K., & Chiu, T. K. F. (2025) · European Journal of Education · doi:10.1111/ejed.70328 · Cited by 7
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Lap, N. D., & Çakır, R. (2025) · Journal of Teacher Education and Lifelong Learning · doi:10.51535/tell.1808375 · Cited by 2
In, S., & Chanchamnan, S. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106268 · Cited by 3
Braha, M. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2026.2621266 · Cited by 7
Yu, A., Ma, Y., Xu, G., Zhou, W., Gao, A., & Trainin, G. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106451 · Cited by 2
Syharat, C. M., & Zaghi, A. E. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1769327 · Cited by 3
Gao, H., Kumar, J., Kumari, J., Huang, J., & Kumar, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-46175-9 · Cited by 3
“Recent studies show that PE is a strong predictor of students’ readiness to use AI technologies in educational settings 29,52,61 including generative AI tools like ChatGPT and DeepSeek 63,64 .”
Asag, M. A., & Mamun, M. A. A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100614 · Cited by 4
Tsiligkiris, V., Kéri, A., & Bowyer, D. M. (2026) · Accounting Education · doi:10.1080/09639284.2026.2688988 · Cited by 4
Oyelakun, O. B., & Oluseyanu, O. (2024) · International Journal of Scientific Research and Management (IJSRM) · doi:10.18535/ijsrm/v12i07.em14 · Cited by 3
“Dahri et al. (2024) conducted a mixed-methods study to explore the acceptance of ChatGPT as a tool for metacognitive self-regulated learning (MSRL) among academics.”
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Gao, B., Liu, R., & Chu, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93412-4_11 · Cited by 7
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Alhasasneh, S., Shishan, F., & Balushi, W. A. (2025) · Journal of Business and Socio-economic Development · doi:10.1108/jbsed-07-2025-0295 · Cited by 1
Muris, D., & Siregar, R. (2026) · SHS Web of Conferences · doi:10.1051/shsconf/202622705004 · Cited by 2
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Stevens, E., van Rijt, J., Bluijs, S., & Bax, S. (2026) · Digital studies in language and literature · doi:10.1515/dsll-2025-0003 · Cited by 2
“TAM has been used to assess AI acceptance for various groups of pre-and in-service teachers (Bergdahl and Sjöberg 2025; Dahri et al. 2024; Kong et al. 2024; Wang et al. 2025; Zhang et al. 2023).”
Gow, S., & Illingworth, S. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-06-2025-0151 · Cited by 3
Duan, Z., Shan, Y., & Gong, Y. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100589 · Cited by 2
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Sultana, A., Latheef, N. A., Siby, N., Sellami, A., Abbasi, S. A., & Ahmad, Z. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2625448 · Cited by 1
Xu, S., & Zhang, G. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1828331 · Cited by 1
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“…a greater influence than perceived usefulness, as evidenced by research on generative AI in Peruvian and Malaysian settings (Cano & Nunez, 2024; Dahri et al., 2024). https://doi.org/10.3390/jintelligence14090220 Additionally, personalization enhances the intention to use AI platforms (Abdalla,…”
InfluentialSayuti, L., Supratman, S., Yaldi, Y., & Salim, A. (2024) · Intiha. · doi:10.58988/intiha.v1i3.316 · Cited by 1
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Faiq, F., Iqzad, M. K., & Syamsuar, D. (2025) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech67074.2025.11264971
“Within the context of AI tools in education, these modifications to TAM can significantly enhance personalized learning experiences and student engagement [13, 14].”
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Li, Y., Lyu, S., Yang, R., Zhu, E., Zhang, H., Zheng, H., Sun, H., & Wang, Y. (2025) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2025.12.551
Wang, A., Yang, H., & Şengül, M. (2026) · European Journal of Education · doi:10.1111/ejed.70441 · Cited by 1
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“More recent studies also suggest that enjoyment can amplify the influence of usefulness, further encouraging acceptance [17].”
InfluentialXu, X., Li, X., Alghamdi, A. A., Elahi, Z., & Rehman, S. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09675-w
“Importantly, attitude is frequently shaped by perceived usefulness, such that positive cognitive evaluations of AI tools foster more favorable emotional responses, which in turn strengthen intentions to accept AI [26, 47].”
Teng, J., & Cho, S. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2676667
Qinjie, S., Zhang, Z., Pituksung, S., Atsawamaitree, L., & He, Y. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.107286
A sensemaking approach for AI adoption: the role of AI framing and digital culture
Liu, S. Y. H., Gustafson, B., & Gustafson, M. (2026) · Marketing Intelligence & Planning · doi:10.1108/mip-03-2025-0276
Lım, W. (2026) · Journal of Musculoskeletal Science and Technology · doi:10.29273/jmst.2026.10.1.43
Sukmojati, E., Ariawan, S., Viana, S. A., Deta, A. Y., Lete, O. R. D., Ruron, M. Y., Tafana, P. D., Koto, R., Muliati, M., Chika, S., et al. (2026) · F1000Research · doi:10.12688/f1000research.183327.1
Арлаков, Е. А., & Miklyaeva, A. V. (2026) · Psychological Science and Education · doi:10.17759/pse.2026310301 · Cited by 1
Behavioral Pathways Behind Students’ Willingness to Pay for GenAI in Higher EducationPreprint
Baral, S., Adhikari, S., Buck, E. B., & Khatiwada, P. (2026) · Research Square · doi:10.21203/rs.3.rs-10210622/v1
Zhen, X., & Lian, X. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1845571 · Cited by 1
The effect of transformational leadership on employees’ generative AI usage intentions
Lim, S., & Kim, B. (2026) · Strategy and Leadership · doi:10.1108/sl-04-2026-0174 · Cited by 1
Altürk, A. V. (2026) · Journal of Hospitality Leisure Sport & Tourism Education · doi:10.1016/j.jhlste.2026.100624
Adoption of AI-powered E-commerce applications: insights from the Technology Acceptance Model
Carvache‐Franco, M., Martin-Moreno, O., Carvache-Franco, O., Moran-Cedeño, V., & Carvache‐Franco, W. (2026) · Cogent Social Sciences · doi:10.1080/23311886.2026.2692162 · Cited by 2
Xu, B., Ang, J., Ni, X., & Zhao, L. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2697041
Artificial intelligence in school language education
Imran, M., Almusharraf, N., & Shehzad, K. (2026) · Journal of Interdisciplinary Studies in Education · doi:10.32674/w506dk33
Zhang, Y., Tian, Y., Lee, K. C. S., & Beh, W. F. (2026) · PLoS ONE · doi:10.1371/journal.pone.0346003
“Similar barriers have been reported in ChatGPT and professional-training contexts, where users’ confidence, AI literacy, and understanding of system limitations influence acceptance [8,19].”
InfluentialThe Challenge of Technostress in AI-Driven Education: A Technology Acceptance Model Investigation
Wibowo, F. S., Saputra, M., & Panduwiyasa, H. (2026) · International Conference on Enterprise and Industrial System (ICOEINS) · doi:10.1109/icoeins69996.2026.11605858
“Clarity and accuracy are expected to enhance perceived usefulness by improving students’ confidence in the value of AI outputs, while clarity and sustainability are expected to influence perceived ease of use by reducing cognitive effort and uncertainty during system interaction [3].”
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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