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].”
MethodResultInfluentialAn assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006) · Educational Psychologist · doi:10.1207/s15326985ep4102_1 · Cited by 7,776
Venkatesh, V. (2000) · Information Systems Research · doi:10.1287/isre.11.4.342.11872 · Cited by 6,664 · cited 4×
“Moreover, Venkatesh [92] discovered that the impact of enjoyment on the perception of ease of use becomes more pronounced as users accumulate direct experience with the system over time.”
BackgroundMethodInfluential(2000) · Elsevier eBooks · doi:10.1016/b978-0-12-109890-2.x5027-6 · Cited by 4,453 · cited 1×
“…of awareness that learners possess regarding their strengths and weaknesses, their ability to employ effective learning strategies, self-motivation for active engagement in learning, and their capacity to formulate and implement strategies and tactics to enhance the learning process [13 – 15].”
BackgroundKallio, H., Pietilä, A., Johnson, M., & Kangasniemi, M. (2016) · Journal of Advanced Nursing · doi:10.1111/jan.13031 · Cited by 4,295 · cited 1×
“This method facilitated direct communication with ChatGPT participants, providing unique understandings [106,107] and allowing in-depth exploration of complex phenomena [108].”
MethodAn updated and expanded assessment of PLS-SEM in information systems research
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017) · Industrial Management & Data Systems · doi:10.1108/imds-04-2016-0130 · Cited by 4,043 · cited 1×
“These values surpass the recommended threshold of 0.7, affirming strong internal consistency [102,123].”
BackgroundInfluentialA social cognitive view of self-regulated academic learning.
Zimmerman, B. J. (1989) · Journal of Educational Psychology · doi:10.1037/0022-0663.81.3.329 · Cited by 3,807 · cited 1×
“Numerous studies have consistently demonstrated the substantial influence of SRL, with a specific emphasis on metacognition, on students ’ academic performance, irrespective of their innate abilities or prior achievements [10 – 12].”
BackgroundZimmerman, B. J. (2008) · American Educational Research Journal · doi:10.3102/0002831207312909 · Cited by 3,735 · cited 1×
“Successful online learners set specific goals, focus on learning rather than performance, and possess task-specific self-efficacy and self-instruction tendencies, which are crucial in learning [15,16].”
BackgroundMetacognitive Aspects of Problem Solving
Flavell, J. H. (2024) · Routledge eBooks · doi:10.4324/9781032646527-16 · Cited by 2,809
Belief, attitude, intention, and behavior: An introduction to theory and research
Hill, R. J., Fishbein, M., & Ajzen, I. (1977) · Contemporary Sociology · doi:10.2307/2065853 · Cited by 2,379 · cited 4×
“Personal Competence, assessing perceived competency in AI usage, comprises five items from sources [59,62].”
BackgroundMethodInfluentialMultiple goals, multiple pathways: The role of goal orientation in learning and achievement
Pintrich, P. R. (2000) · Journal of Educational Psychology · doi:10.1037/0022-0663.92.3.544 · Cited by 2,254 · cited 4×
“As defined by Pintrich [33], self-regulation involves learners ’ ability to control, monitor, and manage their learning processes independently to attain their educational objectives.”
BackgroundInfluentialHandbook of Self-Regulation of Learning and Performance
Schunk, D. H. (2011) · Routledge eBooks · doi:10.4324/9780203839010 · Cited by 2,191
Zimmerman, B. J., & Pons, M. M. (1986) · American Educational Research Journal · doi:10.3102/00028312023004614 · Cited by 2,038
Conducting Semi‐Structured Interviews
Adams, W. C. (2015) · Wiley eBooks · doi:10.1002/9781119171386.ch19 · Cited by 1,692 · cited 1×
“The semi-structured format ensured flexibility, adaptability, and participant comfort [103 – 105].”
BackgroundSchraw, G., Crippen, K. J., & Hartley, K. (2006) · Research in Science Education · doi:10.1007/s11165-005-3917-8 · Cited by 1,529
Handbook of Metacognition in Education
Hacker, D. J., Dunlosky, J., & Graesser, A. C. (2009) · doi:10.4324/9780203876428 · Cited by 1,509 · cited 3×
“Proficient use of metacognitive skills relies on applying metacognitive knowledge, encompassing students ’ ability to assess their progress in cognitive tasks and employ strategies to regulate their progress systematically [10,11].”
BackgroundWhat factors contribute to the acceptance of artificial intelligence? A systematic review
Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, Ó. (2022) · Telematics and Informatics · doi:10.1016/j.tele.2022.101925 · Cited by 1,340 · cited 6×
“Additionally, perceived trust in AI systems and perceived AI usefulness are pivotal as they address students ’ confidence in the technology and their perception of its value in enhancing their metacognitive self-regulated learning [64].”
BackgroundMethodInfluentialAttaining self-regulation : a social cognitive perspective
Zimmerman, B. J. (2000) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 1,237
Anderson, L. W., & Krathwohl, D. R. (2001) · Bibliothèque et Archives nationales du Québec (Québec government) · Cited by 1,194
Dent, A. L., & Koenka, A. C. (2015) · Educational Psychology Review · doi:10.1007/s10648-015-9320-8 · Cited by 1,011
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017) · Computers & Education · doi:10.1016/j.compedu.2016.10.001 · Cited by 1,003 · cited 3×
“In line with established guidelines for evaluating convergent validity, our study carefully examined factor loadings, average variance extracted (AVE), and composite reliability (CR) [68,116,122].”
BackgroundStrzelecki, A. (2024) · Interactive Learning Environments · doi:10.1080/10494820.2023.2209881 · Cited by 807 · cited 1×
“N.A. Dahri et al. aid in regulating their thinking, motivation, and actions [69,70].”
BackgroundYilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100147 · Cited by 745 · cited 1×
“Yilmaz and Karaoglan Yilmaz [72] has proposed that AI-powered tools and environments can enhance metacognition and self-learning power by interacting with students, offering personalized support and feedback as they learn programming.”
BackgroundOpen AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning
Mhlanga, D. (2023) · Sustainable Development Goals Series · doi:10.1007/978-3-031-37776-1_17 · Cited by 718
Song, C., & Song, Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1260843 · Cited by 717 · cited 2×
“…use of AI tools in pre-service teacher education can be advantageous for pursuing a career in teaching, especially, ChatGPT is a generative AI tool created by OpenAI that has attracted a lot of attention due to its ability to give students real-time feedback, explanations, and advice [1,24,27,40].”
BackgroundAl-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11816-3 · Cited by 637 · cited 2×
“Venkatesh et al. and Davis conducted research that emphasizes the significance of intention as a predictor of technology adoption [91,96].”
BackgroundUnlocking the Power of ChatGPT: A Framework for Applying Generative AI in Education
Su (苏嘉红), J., & Yang (杨伟鹏), W. (2023) · ECNU Review of Education · doi:10.1177/20965311231168423 · Cited by 625
Teo, T. P., & Noyes, J. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.03.002 · Cited by 576 · cited 5×
“Teo et al. [90] research explores the significance of reported enjoyment in the context of online learning systems [91].”
BackgroundResultInfluentialRafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103732 · Cited by 546 · cited 1×
“…employed to understand the determinants of technology acceptance in various regions including Germany [50], US [51], Saudi Arabia [52], Pakistan [53], Malaysia [54], Turkey [55], Greece [56], Indonesia [57], South Korea [58], and China [56,57], and across different technology-related domains…”
BackgroundUnlocking the opportunities through ChatGPT Tool towards ameliorating the education system
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023) · BenchCouncil Transactions on Benchmarks, Standards and Evaluations · doi:10.1016/j.tbench.2023.100115 · Cited by 535 · cited 1×
“During the performance phase, learners are tasked with actively participating in learning activities and fine-tuning their learning approaches to achieve their objectives [17,35,36].”
BackgroundJin, S.-H., Im, K., Yoo, M., Roll, I., & Seo, K. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00406-5 · Cited by 528 · cited 6×
“Structured interviews were utilized to provide a context-specific approach, addressing the limitations of surveys [1,10].”
BackgroundMethodInfluentialLiu, G., & Ma, C. (2024) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2023.2240316 · Cited by 511
Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA
Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A. A. A., Ghobakhloo, M., Annamalai, N., & Naghmeh‐Abbaspour, B. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2226495 · Cited by 510 · cited 1×
“This was done per the well-established two-step approach suggested by other research [116,115,117,118].”
MethodInvestigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis
Choudhury, A., & Shamszare, H. (2023) · Journal of Medical Internet Research · doi:10.2196/47184 · Cited by 470 · cited 3×
“People must feel safe and confident in the technology ’ s skills and moral application, which makes trust a crucial component of technology adoption [82].”
BackgroundInfluentialAl-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019) · IEEE Access · doi:10.1109/access.2019.2899368 · Cited by 435 · cited 6×
“Previous studies supported personal competency on ChatGPT acceptance [62,63].”
BackgroundMethodResultInfluentialMailizar, M., Burg, D., & Maulina, S. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10557-5 · Cited by 420 · cited 2×
“…acceptance in various regions including Germany [50], US [51], Saudi Arabia [52], Pakistan [53], Malaysia [54], Turkey [55], Greece [56], Indonesia [57], South Korea [58], and China [56,57], and across different technology-related domains such as e-learning, remote education, massive open online…”
BackgroundAcceptance of artificial intelligence among pre-service teachers: A multigroup analysis
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00420-7 · Cited by 413 · cited 1×
“TAM has been extensively employed to understand the determinants of technology acceptance in various regions including Germany [50], US [51], Saudi Arabia [52], Pakistan [53], Malaysia [54], Turkey [55], Greece [56], Indonesia [57], South Korea [58], and China [56,57], and across different…”
BackgroundChang, D. H., Lin, M. P.-C., Hajian, S., & Wang, Q. Q. (2023) · Sustainability · doi:10.3390/su151712921 · Cited by 370 · cited 3×
“Integrating Artificial Intelligence (AI) holds great promise for enhancing metacognitive self-regulated learning [2,28,29].”
BackgroundKhan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., & Khan, S. U. (2017) · Telematics and Informatics · doi:10.1016/j.tele.2017.09.009 · Cited by 310 · cited 2×
“Trust is crucial to technology adoption, as shown in the literature and aligned with studies [80,81].”
BackgroundResultLai, C. Y., Cheung, K. Y., & Chan, C. S. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100178 · Cited by 310 · cited 2×
“Furthermore, studies by Lai et al. emphasize how important metacognition is to enhance learning results [35].”
BackgroundWu, T., Lee, H., Li, P., Huang, C.-N., & Huang, Y. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331231191125 · Cited by 302
Conversational commerce: entering the next stage of AI-powered digital assistants
Balakrishnan, J., & Dwivedi, Y. K. (2024) · Annals of Operations Research · doi:10.1007/s10479-021-04049-5 · Cited by 301 · cited 1×
“This construct reflects how the user perceives the AI tool ’ s ability to offer perceptive and intelligent assistance for metacognitive self-regulated learning [94].”
BackgroundChatGPT: The cognitive effects on learning and memory
Bai, L., Liu, X., & Su, J. (2023) · Brain‐X · doi:10.1002/brx2.30 · Cited by 238 · cited 3×
“Although AI is becoming increasingly common in education [24,26, 29], it is unclear how pre-service instructors in higher education perceive these tools and how effective they are.”
BackgroundAssessing Health Students' Attitudes and Usage of ChatGPT in Jordan: Validation Study
Sallam, M., Salim, N. A., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R., & Hallit, S. (2023) · JMIR Medical Education · doi:10.2196/48254 · Cited by 234 · cited 3×
“The term "intention to use ChatGPT" describes a person ’ s desire and resolve to utilize ChatGPT for metacognitive self-regulated learning [87].”
BackgroundResultInfluentialExtending UTAUT2 toward acceptance of mobile learning in the context of higher education
Arain, A. A., Hussain, Z., Rizvi, W. H., & Vighio, M. S. (2019) · Universal Access in the Information Society · doi:10.1007/s10209-019-00685-8 · Cited by 219 · cited 2×
“Social influence on technology uptake is supported by previous research [41,77,78].”
BackgroundResultYoon, H.-Y. (2016) · The Journal of Academic Librarianship · doi:10.1016/j.acalib.2016.08.003 · Cited by 217 · cited 6×
“Personal competence, reflecting students ’ self-perceived skills in using AI for learning, assumes significance in this context, as does the influence of peers and social networks, encapsulated by the social influence factor [51, 58,68].”
BackgroundMethodInfluentialMolenaar, I. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100070 · Cited by 217 · cited 1×
“A key component of the Technology Acceptance Model (TAM) is behavioral intention, which emphasizes how important it is for students to be willing and determined to use ChatGPT for their metacognitive self-regulated learning [44,67].”
BackgroundAl-Maatouk, Q., Othman, M. S., Aldraiweesh, A., Alturki, U., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2020) · IEEE Access · doi:10.1109/access.2020.2990420 · Cited by 189 · cited 1×
“…understand the determinants of technology acceptance in various regions including Germany [50], US [51], Saudi Arabia [52], Pakistan [53], Malaysia [54], Turkey [55], Greece [56], Indonesia [57], South Korea [58], and China [56,57], and across different technology-related domains such as…”
BackgroundAltalhi, M. M. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10317-x · Cited by 178 · cited 1×
“[110 – 112].”
Using chatbots for English language learning in higher education
Annamalai, N., Rashid, R. A., Hashmi, U. M., Mohamed, M. H., Alqaryouti, M. H., & Sadeq, A. E. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100153 · Cited by 165 · cited 5×
“First, TAM is ideally suited for analyzing AI adoption within the framework of metacognitive self-regulated learning, having been originally designed to forecast and explain consumer acceptance and usage of information technology [45,47 – 49].”
BackgroundInfluentialHamzah, H., Hamzah, M. I., & Zulkifli, H. (2022) · Sustainability · doi:10.3390/su14020813 · Cited by 161 · cited 1×
“Meta-cognitive self-regulation learning is the term for this [98].”
BackgroundLi, K. (2023) · Sustainability · doi:10.3390/su15065221 · Cited by 157 · cited 1×
“The model explains that PU and PEOU shape individuals ’ attitudes and both PU and attitudes toward technology usage predict BI [46,60].”
BackgroundInvestigating the drivers and barriers to MOOCs adoption: The perspective of TAM
Al‐Adwan, A. S. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10250-z · Cited by 156 · cited 2×
“Many important aspects affect students ’ behavioral intention to utilize ChatGPT in the educational context [65], which influences the adoption of AI for metacognitive self-regulated learning [63,66].”
BackgroundAl-Rahmi, W. M., Al‐Adwan, A. S., Al-Maatouk, Q., Othman, M. S., Alsaud, A. R., Almogren, A. S., & Al-Rahmi, A. M. (2023) · Sustainability · doi:10.3390/su15108144 · Cited by 155 · cited 1×
“The construct reliability, AVE, and CR are presented in Table 3, and factor loading values, as presented in Table 2, indicate the rigorous evaluation of the measurement model ’ s reliability and validity in our study [61,77,124].”
MethodFu-hai, A., Xi, L., & Yu, J. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11959-3 · Cited by 144 · cited 3×
“People actively employ self-regulation processes and metacognitive strategies to enhance their learning experiences [73].”
BackgroundExploring factors affecting the adoption of MOOC in Generation Z using extended UTAUT2 model
Meet, R. K., Kala, D., & Al‐Adwan, A. S. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11052-1 · Cited by 137 · cited 1×
“To assess the reliability of the questionnaire, a pilot test was administered to 35 pre-service students, and the Cronbach ’ s alpha method, with a recommended cutoff point of 0.7, was employed to evaluate its internal consistency [114,115].”
MethodInfluentialLosinski, M. L., Cuenca‐Carlino, Y., Zablocki, M., & Teagarden, J. M. (2014) · Behavioral Disorders · doi:10.17988/0198-7429-40.1.52 · Cited by 135 · cited 2×
“Some interventions, such as self-regulated strategy development, have demonstrated the potential to improve academic skills in students with learning difficulties [22].”
BackgroundTzafilkou, K., Perifanou, M. A., & Economides, A. A. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10604-1 · Cited by 129 · cited 2×
“…of technology acceptance in various regions including Germany [50], US [51], Saudi Arabia [52], Pakistan [53], Malaysia [54], Turkey [55], Greece [56], Indonesia [57], South Korea [58], and China [56,57], and across different technology-related domains such as e-learning, remote education,…”
BackgroundDahri, N. A., Al-Rahmi, W. M., Almogren, A. S., Yahaya, N., Vighio, M. S., Al-Maatouk, Q., Al-Rahmi, A. M., & Al‐Adwan, A. S. (2023) · Sustainability · doi:10.3390/su15118514 · Cited by 128 · cited 3×
“This study provides a significant gap in the literature by analyzing the factors influencing pre-service teachers ’ decision to use ChatGPT and examining their adoption of the AI tool for metacognitive self-regulated learning [10,28].”
BackgroundNa, S., Heo, S., Choi, W., Kim, C., & Whang, S.-W. (2023) · Buildings · doi:10.3390/buildings13102518 · Cited by 124
Exploring College Students' Attitudes and Self-Efficacy of Mobile Learning
Yang, S.-H. (2012) · The Turkish Online Journal of Educational Technology · Cited by 115
Can ChatGPT Pass High School Exams on English Language Comprehension?
de Winter, J. C. F. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00372-z · Cited by 115 · cited 2×
“It ’ s gained attention in creative writing, coding, teaching innovation, sentiment analysis, and annotation tasks [31].”
BackgroundAn, S., Eck, T., & Yim, H. (2023) · Sustainability · doi:10.3390/su15010832 · Cited by 114
Internet banking adoption under Technology Acceptance Model—Evidence from Cambodian users
Ly, B., & Ly, R. (2022) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2022.100224 · Cited by 101 · cited 2×
“[84], and [90].”
BackgroundAn Overview: Metacognition in Education
Mahdavi, M. (2014) · International Journal of Multidisciplinary and Current Research · Cited by 93
Dahri, N. A., Vighio, M. S., Bather, J. D., & Arain, A. A. (2021) · Sustainability · doi:10.3390/su132313222 · Cited by 88 · cited 1×
“Learners benefit from actively posing and addressing questions pertinent to their studies to reach their desired learning outcomes [37,38].”
BackgroundFactors affecting undergraduates’ selection of online library resources in academic tasks
Joo, S., & Choi, N. (2015) · Library Hi Tech · doi:10.1108/lht-01-2015-0008 · Cited by 87
Alyoussef, I. Y. (2021) · Sustainability · doi:10.3390/su13137374 · Cited by 79 · cited 4×
“Reliability and continuing technology usage are strongly correlated with enjoyment [89].”
BackgroundResultInfluentialExploring student satisfaction and acceptance of e-learning technologies in Saudi higher education
Alqahtani, M. A., Alamri, M. M., Sayaf, A. M., & Al-Rahmi, W. M. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.939336 · Cited by 78
Tayan, O., Hassan, A., Khankan, K., & Askool, S. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100513 · Cited by 75 · cited 1×
“Tayan et al. [39] critically examine the integration of AI chatbots like ChatGPT into higher education technology courses.”
BackgroundNavarro, R., Vega, V., Bayona, H., Bernal, V. M. G., & García, A. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1227956 · Cited by 74
Learning with Generative Artificial Intelligence Within a Network of Co-Regulation
Lodge, J. M., De Barba, P., & Broadbent, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.7.02 · Cited by 73 · cited 5×
“Further, Lodge et al. [44] explore the transformative potential of generative AI, such as ChatGPT, in education, proposing a "co-regulation" model challenging traditional student-teacher dynamics; AI ’ s role includes providing personalized feedback, suggesting resources, and fostering…”
BackgroundMethodInfluentialIntention and barriers to use MOOCs: An investigation among the post graduate students in India
Mohan, M. M., Upadhyaya, P., & Rajasekharan Pillai, K. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10215-2 · Cited by 68 · cited 2×
“[110,111].”
ResultBaroni, I., Re Calegari, G., Scandolari, D., & Celino, I. (2022) · Human Computation · doi:10.15346/hc.v9i1.134 · Cited by 65 · cited 2×
“Trust is crucial to technology adoption, as shown in the literature and aligned with studies [80,81].”
BackgroundResultImpacts of Interview as Research Instrument of Data Collection in Social Sciences
Monday, T. U. (2020) · Journal of Digital Art & Humanities · doi:10.33847/2712-8148.1.1_2 · Cited by 64 · cited 3×
“The semi-structured format ensured flexibility, adaptability, and participant comfort [103 – 105].”
BackgroundWang, C.-Y., & Lin, J. J. H. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107721 · Cited by 64 · cited 5×
“Wang and Lin [8] investigate the potential of AI as a tool for analyzing and supporting self-regulated learning (SRL).”
BackgroundInfluentialJauk, S., Kramer, D., Avian, A., Berghold, A., Leodolter, W., & Schulz, S. (2021) · Journal of Medical Systems · doi:10.1007/s10916-021-01727-6 · Cited by 59 · cited 1×
“The post-task reflection sessions were an integral component of data collection, allowing participants to provide qualitative insights and share their experiences regarding using ChatGPT for metacognitive self-regulated learning [86].”
BackgroundTechnology Acceptance and Performance in Online Learning Environments: Impact of Self-Regulation
Tabak, F., & Nguyen, N. T. H. (2013) · Cited by 53
Towards Adaptive E-Learning among University Students: by Applying Technology Acceptance Model (TAM)
Alamri, M. M., Al-Rahmi, W. M., Yahaya, N., Al-Rahmi, A. M., Abualrejal, H. M., Zeki, A. M., & Al-Maatouk, Q. (2019) · International Journal of Engineering and Advanced Technology · doi:10.35940/ijeat.f1043.0986s319 · Cited by 53 · cited 8×
“This construct reflects the application of metacognitive knowledge and skills to maximize and manage one ’ s learning [63,66].”
BackgroundMethodResultInfluentialAlmogren, A. S., & Aljammaz, N. A. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1050532 · Cited by 52
Tian, Y., & Wang, X. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.986800 · Cited by 47
Mobile-Based Training and Certification Framework for Teachers’ Professional Development
Dahri, N. A., Al-Rahmi, W. M., Almogren, A. S., Yahaya, N., Vighio, M. S., & Al-Maatouk, Q. (2023) · Sustainability · doi:10.3390/su15075839 · Cited by 42
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Almogren, A. S., & Vighio, M. S. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12434-9 · Cited by 42 · cited 1×
“Learners benefit from actively posing and addressing questions pertinent to their studies to reach their desired learning outcomes [37,38].”
BackgroundNa, S., Heo, S., Choi, W., Han, S., & Kim, C. (2023) · Buildings · doi:10.3390/buildings13041066 · Cited by 38 · cited 2×
“[62,101].”
BackgroundUsability Evaluation of Mobile App for the Sustainable Professional Development of Teachers
Dahri, N. A., Vighio, M. S., Al-Rahmi, W. M., & Alismaiel, O. A. (2022) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v16i16.32015 · Cited by 30 · cited 1×
“Notably, the perception of enjoyment has been consistently linked to the intention to use computers [24,89,90,93].”
BackgroundSOCIAL INFLUENCE IN TECHNOLOGY ADOPTION RESEARCH: A LITERATURE REVIEW AND RESEARCH AGENDAPreprint
Graf‐Vlachy, L., & Buhtz, K. (2017) · SSRN Electronic Journal · Cited by 27
Encouraging engagement: video-conference augmentation of online distance learning environments
Starr‐Glass, D. (2020) · On the Horizon The International Journal of Learning Futures · doi:10.1108/oth-06-2020-0020 · Cited by 27 · cited 1×
“In the Chinese context, an et al. [73,74] discovered that technology acceptance significantly impacted middle school students ’ self-regulated learning (SRL), with information management.”
BackgroundStudents’ Intention toward Self-Regulated Learning under Blended Learning Setting: PLS-SEM Approach
Jiang, Y., Wang, P.-H., Li, Q., & Li, Y. (2022) · Sustainability · doi:10.3390/su141610140 · Cited by 27 · cited 1×
“[51,58,113].”
Assessing the Impact of Mobile-Based Training on Teachers’ Achievement and Usage Attitude
Dahri, N. A., Vighio, M. S., Alismaiel, O. A., & Al-Rahmi, W. M. (2022) · International Journal of Interactive Mobile Technologies (iJIM) · doi:10.3991/ijim.v16i09.30519 · Cited by 25 · cited 3×
“Social influence on technology uptake is supported by previous research [41,77,78].”
BackgroundMethodResultChildren's metamemory: A review of the literature and implications for the classroom
Karably, K., & Zabrucky, K. M. (2009) · lnternational Electronic Journal of Elementary Education · Cited by 24
Zha, X., Zhang, J., & Yan, Y. (2014) · Journal of Librarianship and Information Science · doi:10.1177/0961000614532677 · Cited by 23 · cited 1×
“The behavioral psychology research of Fishbein and Ajzen [97] offers a theoretical framework for comprehending how attitude affects behavioral intentions.”
BackgroundA Glimpse in ChatGPT Capabilities and its impact for AI researchPreprint
Joublin, F., Ceravola, A., Deigmoeller, J., Gienger, M., Franzius, M., & Eggert, J. P. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.06087 · Cited by 23
University students’ acceptance of mobile learning: A comparative study in Turkey and Kyrgyzstan
Adanır, G. A., & Muhametjanova, G. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10620-1 · Cited by 21 · cited 1×
“…determinants of technology acceptance in various regions including Germany [50], US [51], Saudi Arabia [52], Pakistan [53], Malaysia [54], Turkey [55], Greece [56], Indonesia [57], South Korea [58], and China [56,57], and across different technology-related domains such as e-learning, remote…”
BackgroundA survey on technology supported collaborative learning tools and techniques in teacher education
Dahri, N. A., Vighio, M. S., & Dahri, M. H. (2019) · International Conference on Information Science and Communication Technology (ICISCT) · doi:10.1109/cisct.2019.8777421 · Cited by 20 · cited 2×
“Integrating Artificial Intelligence (AI) holds great promise for enhancing metacognitive self-regulated learning [2,28,29].”
BackgroundRajan, K. K., & Pandit, A. S. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03578-2 · Cited by 18 · cited 1×
“Moreover, the flexibility and convenience offered by computer-assisted and mobile-assisted learning can enhance students ’ enjoyment and self-efficacy or personal competency, meta-cognition and self-learning skills ultimately resulting in increased participation in the learning process [71].”
BackgroundOcak, C., Kopcha, T. J., & Dey, R. (2023) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2023.100146 · Cited by 13 · cited 1×
“Furthermore, LLMs have evolved beyond text and can now interpret and generate content across multiple formats. their impact is already being felt in the fields of education and research [8,25].”
BackgroundTran, H. H., Capps, D. K., & Hodges, G. W. (2022) · Sustainability · doi:10.3390/su14105923 · Cited by 11
Ming, G. K., & Mansor, M. B. (2023) · Asian Journal of Research in Education and Social Sciences · doi:10.55057/ajress.2023.5.4.6 · Cited by 10 · cited 3×
“A limited amount of research has focused on pre-service teachers, a distinct population with particular expectations and characteristics, despite numerous studies examining educators ’ and students ’ acceptance of technology [42,43].”
BackgroundIqbal, S., Hanif, R., Ali, F., Tahir, M., Minhas, R., Yasmeen, R., Khokhar, A., & Laique, T. (2021) · Pakistan Journal of Medical and Health Sciences · doi:10.53350/pjmhs2115123498 · Cited by 7 · cited 2×
“[96].”
BackgroundAdams, L. L. (2014) · OhioLink ETD Center (Ohio Library and Information Network) · Cited by 1
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
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31887 · Cited by 250
Schei, O. M., Møgelvang, A., & Ludvigsen, K. (2024) · Education Sciences · doi:10.3390/educsci14080922 · Cited by 166
“Dahri et al. [30] used an extended TAM model, finding a high acceptance of ChatGPT among university students.”
Zong, Y., & Yang, L. (2025) · European Journal of Education · doi:10.1111/ejed.12925 · Cited by 173
Almulla, M. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e32220 · Cited by 174
“Satisfied students exhibit higher levels of persistence and achieve better learning outcomes, adopting a positive learning environment [34,35].”
BackgroundMethodResultInfluentialAlshammarı, S. H., & Babu, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-91634-4 · Cited by 161
“Technology solutions that students perceive as easy to use, such as ChatGPT, are more likely to be used and to interact with 30 .”
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
