The DOI matches its Crossref record; 110 of the 113 references it cites were found in a registry or an index.
Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Cited by
Sorted by citations
Items 201–239 of 239
Kurtz, G., & Amzalag, M. (2026) · Systems · doi:10.3390/systems14080884
Fang, F., Meng, Y., Tang, L., & Cui, Y. H. (2026) · BMC Psychology · doi:10.1186/s40359-026-05198-7
“The Technology Acceptance Model (TAM) identifies perceived usefulness and perceived ease of use as important antecedents of technology-related attitudes [9], while more recent research highlights the additional roles of psychological readiness, individual experiences, and learning contexts [8].”
A cognitive resource trade-off model of AI-assisted language learning in Chinese undergraduates
Li, S., Hua, Y., Yuan, Y., & Li, D. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1872681
Sun, Z., Zhang, Q., & Hu, Q. (2026) · Empirical Studies of the Arts · doi:10.1177/02762374261473199
Alyoussef, I. Y., Dahri, N. A., Alhashmi, K. A., Al-Rahmi, W. M., Muzna, P., & Dahri, F. H. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1833043 · Cited by 1
“…studies primarily emphasize digital transformation, AI adoption readiness, and technological infrastructure, while ethical AI preparedness and responsible classroom integration remain underexplored changes (Al-Rahmi, 2025; Dahri et al., 2024a; Khan and Alamäki, 2023; Al-Maroof et al., 2025).”
Wibowo, A., Narmaditya, B. S., Alkhudri, A. T., & Sahid, S. (2026) · Discover Education · doi:10.1007/s44217-026-02002-x
“In addition, some investigations (e.g., [4, 9]) identified trends and the potential role of AI in higher education and highlighted the value added by its use in the classroom.”
Ibrahim, I., Firmansyah, J., Jalaluddin, J., & Marwan, M. (2026) · Research Square · doi:10.21203/rs.3.rs-8675352/v1 · Cited by 1
Kim, K., Kwon, K., 권혜성, & Brush, T. A. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10692-1
Lin, X., Zhu, X., & Sun, Y. (2026) · Teachers and Teaching · doi:10.1080/13540602.2026.2716188
Christopoulos, A., Mystakidis, S., Perikos, I., Bourazeri, A., Michael, A., & Laakso, M. (2026) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2026.100378
Profiling Undergraduate EFL Learners’ Use of AI Tools: A Person-Centered Analysis of TAM and SRL
Rahmani, E. F. (2026) · Journal of Educational Technology Systems · doi:10.1177/00472395261480325
Cognitive, Metacognitive, and Self-Regulated Practices for Pre-Service Teachers in the AI Era
Fenty, N. S., & Park, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-3840-6.ch008
Dubey, V., Verma, S., Kumari, P., Kumar, V., & Verma, S. (2026) · Trends in Scholarly Publishing · doi:10.21124/tsp.2026.124.137
Rong, Y., Bao, Z., Huang, Y., & Xia, Y. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2702022
Wang, Z., & Gomez, A. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663920
Pham, A. T. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663886
“This supports earlier studies that highlight the role of AI in strengthening monitoring behaviors during writing [8], [9].”
Rahmatika, L., Islam, M. M., Murshidi, G. H. A., Nawaz, S. S., & Riantina, E. M. (2026) · Online Learning · doi:10.24059/olj.v30i3.5273
Algorithmic Intentionality: A Distributed Cognition Framework
Toscano, J. (2026) · Human Affairs · doi:10.1515/humaff-2026-0012
Alshammarı, S. H., Alshammari, K. F., & Al-Shammari, E. J. (2026) · Education Sciences · doi:10.3390/educsci16091457
Gyekye, E. K. (2026) · British Educational Research Journal · doi:10.1002/berj.70277
Wang, N., & Lee, K.-Y. (2026) · Journal of Digital Contents Society · doi:10.9728/dcs.2026.27.8.2229
MAIN DRIVERS OF CHATGPT ADOPTION BY ROMANIAN COMPUTER SCIENCE STUDENTS
Popescu, P. Ș., Mihăescu, M. C., & Pribeanu, C. (2026) · Problems of Education in the 21st Century · doi:10.33225/pec/26.84.646
Wang, Y., Zhang, H., & Cui, Y. (2026) · SAGE Open · doi:10.1177/21582440261491028
Anonymous, Xu, W., Qi, Y., & Lu, D. (2026) · Physical Review Physics Education Research · doi:10.1103/62m6-lkh7
Naher, K., Siddiqi, K. O., Mamun, M. A. A., Chakma, S., Rashid, F., Arju, U. H., Alam, S., & Tanchangya, T. (2026) · Discover Education · doi:10.1007/s44217-026-02075-8
“According to research, both ease of use and perceived utility influence attitudes toward AI use [51–55].”
Nan, G., Christian, M., Yulita, H., Muzammil, O. M., Wibowo, S. A., Sunarno, S., Sbastian, D., & Hussain, S. Y. (2026) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech202671078.2026.11698986
“AI is often viewed positively because it can improve learning efficiency, support personalized learning, and simplify the preparation of teaching materials [6], [11], [12].”
Sa'adiah, A. H., Othman, M., Thinakaran, R., Al-Nidawi, W. J. A., & Sulaiman, H. B. (2026) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.25.9.19
Yang, Z., Yang, Y., & LIU, W. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1969743
“…use in more learn-ing-oriented terms may also report stronger autonomy-and competence-related academic experience, self-regulated learning, and critical-evaluation tendencies because these self-reports concern related aspects of academically engaged learning (Dahri et al., 2024; Xia et al., 2023).”
Sun, F., & Ma, R. (2026) · International Journal of Mental Health and Addiction · doi:10.1007/s11469-026-01740-1
Al-Sharafi, M. A., Al‐Emran, M., Asadi, S., Al-Qaysi, N., Ibrahim, N., & Sianaki, O. A. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103581
Users Continuance Intention of Using Generative AI for Generating Graphic Designs
Poniman, R. M., Aribowo, A., Hery, Widjaja, A. E., & Haryani, C. A. (2026) · International Conference on Technology Innovation and Its Applications (ICTIIA) · doi:10.1109/ictiia70925.2026.11716783
“The key drivers behind users' willingness to utilize GenAI in contexts such as Metacognitive Self-Regulated Learning (SRL) for teachers [11] and among university students [12] have been investigated.”
The impact of artificial intelligence on students’ metacognition: a systematic review
Ji, C., & Tan, Q. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1947198
“The GAI studies similarly showed that ChatGPT could support these regulatory processes, as well as information management, debugging, and task adjustment in writing and broader self-regulated learning contexts (Dahri et al., 2024; Yao et al., 2025; Amani and Bisriyah, 2025; Zhu, 2025).”
Iriqat, R. A. M., Al-Rawashdeh, O. M., Abotaleb, S., Herzallah, A. M., & Hamed, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-4604-3.ch008
Liu, X., Luo, J., Chen, Y., Zhu, J., Liu, Y., & Wang, Z. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1948768
“Previous studies have applied TAM-related concepts to explore factors influencing acceptance of LLMs (10, 11).”
Tang, M., & Xie, R. (2026) · SAGE Open · doi:10.1177/21582440261494879
Dewi, E. R., Alam, A. A., Zakaria, F. B., & Rusmiaty, R. (2026) · European Journal of Educational Research · doi:10.12973/eu-jer.16.1.23
Nguyen, N.-T., Tran, N.-K. H., Pham, K.-M. N., & Duong, N.-H. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.108061
Zueva, E. (2026) · Almanac Institute of special education · doi:10.66435/almanac.ise.63.06
Aladil, M. K., & Gameil, A. A. (2026) · Sustainability · doi:10.3390/su182110940
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
