The DOI matches its Crossref record; 40 of the 43 references it cites were found in a registry or an index.
Supporting Teachers’ Professional Development With Generative AI: The Effects on Higher Order Thinking and Self-Efficacy
Abstract
Generative AI has emerged as a noteworthy milestone and a consequential advancement in the annals of major disciplines within the domains of human science and technology. This study aims to explore the effects of generative AI-assisted pre-service teaching skills training on pre-service teachers’ self-efficacy and higher order thinking. The participants of this study were 215 pre-service mathematics, science, and computer teachers from a university in China. Firstly, a pretest-posttest quasi-experimental design was implemented for an experimental group (teaching skills training by generative AI) and a control group (teaching skills training by traditional methods) by investigating the teacher self-efficacy and higher order thinking of the two groups before and after the experiment. Secondly, a semi-structured interview comprising open-ended questions was administered to 25 pre-service teachers within the experimental group to present their views on generative AI-assisted teaching. The results showed that the scores of pre-service teachers in the experimental group, who used generative AI for teachers’ professional development, were considerably higher than those of the control group, both in teacher self-efficacy (F = 8.589, p = 0.0084< 0.05) and higher order thinking (F = 7.217, p = 0.008 < 0.05). It revealed that generative AI can be effective in supporting teachers’ professional development. This study produced a practical teachers’ professional development method for pre-service teachers with generative AI.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“These interventions embedded GenAI within learning platforms to generate real-time hints, guiding questions, automated scoring, or corrective feedback in response to student inputs [32,47,49,59].”
From A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students' Critical Thinking (2026) · cites this paper as [47]
In the citing paper
“These interventions were frequently embedded in performance-based assessments supported by CT-aligned rubrics (e.g., Bloom's Revised Taxonomy, Paul & Elder's Elements of Thought) [15,47,95].”
From A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students' Critical Thinking (2026) · cites this paper as [47]
In the citing paper
“These cognitive affordances are amplified when paired with pedagogical intentionality [47].”
From A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students' Critical Thinking (2026) · cites this paper as [47]
Citations
Cites
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1978) · Advances in Behaviour Research and Therapy · doi:10.1016/0146-6402(78)90002-4 · Cited by 10,586
The differential antecedents of self-efficacy beliefs of novice and experienced teachers
Tschannen-Moran, M., & Hoy, A. W. (2007) · Teaching and Teacher Education · doi:10.1016/j.tate.2006.05.003 · Cited by 1,976 · cited 1×
“In addition, teachers’ self-efficacy also plays an important role in learning and teaching [11], [12].”
BackgroundA Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123618 · Cited by 1,452
A SWOT analysis of ChatGPT: Implications for educational practice and research
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2195846 · Cited by 1,384
ChatGPT for Education and Research: Opportunities, Threats, and Strategies
Rahman, M. M., & Watanobe, Y. (2023) · Applied Sciences · doi:10.3390/app13095783 · Cited by 1,080 · cited 1×
“ThereisnodoubtthatAIGChasalso evokedpolarizedreactionsineducation,havingasignificant andfar-reachingimpactonschooleducationinashortperiod [7].”
Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.85 · Cited by 869
Purposely Teaching for the Promotion of Higher-order Thinking Skills: A Case of Critical Thinking
Barak, M., Ben‐Chaim, D., & Uri, Z. (2007) · Research in Science Education · doi:10.1007/s11165-006-9029-2 · Cited by 757
Engineering Education in the Era of ChatGPT: Promise and Pitfalls of Generative AI for Education
Qadir, J. (2023) · 2023 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon54358.2023.10125121 · Cited by 707 · cited 1×
“Since November 2022, with the emergence of ChatGPT and GPT-4, the generative artificial intelligence represented by Chat-GPT has become a significant landmark and effective advance in the history of major human science and technology [6].”
BackgroundJeon, J., & Lee, S. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11834-1 · Cited by 696
Examining TPACK Among K-12 Online Distance Educators in the United States
Archambault, L., & Crippen, K. J. (2009) · Contemporary issues in technology and teacher education · Cited by 560
Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey
Shoufan, A. (2023) · IEEE Access · doi:10.1109/access.2023.3268224 · Cited by 499 · cited 2×
“This study makes an overall study of higher order thinking and related research [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] and organizes the relevant higher order thinking scale based on the Bloom educational goal classification framework.”
BackgroundMethodHolistic Evaluation of Language Models
Bommasani, R., Liang, P., & Lee, T. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15007 · Cited by 480 · cited 1×
“ChatGPT cannot only complete text generation tasks [19] but also solve various problems with its good reasoning ability [20].”
BackgroundJaipal-Jamani, K., & Angeli, C. (2016) · Journal of Science Education and Technology · doi:10.1007/s10956-016-9663-z · Cited by 287
Holistic Evaluation of Language Models
Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2211.09110 · Cited by 255
An Interview with Anita Woolfolk: The Educational Psychology of Teacher Efficacy
Shaughnessy, M. F. (2004) · Educational Psychology Review · doi:10.1023/b:edpr.0000026711.15152.1f · Cited by 219
Fidan, M., & Gencel, N. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331221077901 · Cited by 186
Lee, J., & Choi, H. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.06.015 · Cited by 160
Enabling AI-Generated Content Services in Wireless Edge Networks
Du, H., Li, Z., Niyato, D. T., Kang, J., Xiong, Z., Shen, X., & Kim, D. I. (2024) · IEEE Wireless Communications · doi:10.1109/mwc.004.2300015 · Cited by 155
Potential ChatGPT Use in Undergraduate Chemistry Laboratories
Humphry, T., & Fuller, A. L. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00006 · Cited by 152
A study of the effects of video tape recording in microteaching training
Kpanja, E. (2001) · British Journal of Educational Technology · doi:10.1111/1467-8535.00215 · Cited by 145
Using microteaching to enhance teacher efficacy in pre‐service teachers
Mergler, A., & Tangen, D. (2010) · Teaching Education · doi:10.1080/10476210902998466 · Cited by 143
The Microteaching Experience: Student Perspectives
Benton-Kupper, J. (2001) · Cited by 131
Microteaching and pre-service teachers’ sense of self-efficacy in teaching
Arsal, Z. (2014) · European Journal of Teacher Education · doi:10.1080/02619768.2014.912627 · Cited by 115
Prospective Teachers' Perspectives on Microteaching Lesson Study.
Fernández, M. L., & Robinson, M. C. (2006) · Cited by 114
Visuals to Text: A Comprehensive Review on Automatic Image Captioning
Ming, Y., Hu, N., Fan, C., Feng, F., Zhou, J., & Yu, H. L. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105734 · Cited by 85
Priyaadharshini, M., & Sundaram, B. V. (2018) · Computer Applications in Engineering Education · doi:10.1002/cae.22035 · Cited by 75
Liu, D., & Hai, Z. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10922-y · Cited by 75
Schina, D., Valls, C., Borrull, A., Usart, M., & González, V. E. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00264-z · Cited by 69
Fung, D. C.-L. (2014) · International Journal of Educational Research · doi:10.1016/j.ijer.2014.02.002 · Cited by 64
Lazarides, R., & Schiefele, U. (2021) · Learning and Instruction · doi:10.1016/j.learninstruc.2021.101489 · Cited by 57
Can You Help Me with My Pitch? Studying a Tool for Real-Time Automated Feedback
Schneider, J., Börner, D., van Rosmalen, P., & Specht, M. M. (2016) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2627043 · Cited by 50
TEACHERS' UNDERSTANDING AND PRACTICES IN ASSESSING HIGHER ORDER THINKING SKILLS AT PRIMARY SCHOOLS
Driana, E., & Ernawati, E. (2019) · ACITYA Journal of Teaching & Education · doi:10.30650/ajte.v1i2.233 · Cited by 46
Enabling AI-Generated Content (AIGC) Services in Wireless Edge NetworksPreprint
Du, H., Li, Z., Niyato, D. T., Kang, J., Xiong, Z., Xuemin, Shen, S., & Kim, D. I. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.03220 · Cited by 44
Chao, S.-H., Jiang, J., Hsu, C., Chiang, Y.-T., Ng, E., & Fang, W. (2020) · Applied Sciences · doi:10.3390/app10051602 · Cited by 28
Pair microteaching:an unrealistic pedagogy in pre-service methodology courses?
Yan, C., & He, C. (2017) · Journal of Education for Teaching International Research and Pedagogy · doi:10.1080/02607476.2017.1286783 · Cited by 23
Are They Efficacious? Exploring Pre-Service Teachers' Teaching Efficacy Beliefs during the Practicum
Aydın, S., Demırdöğen, B., & Tarkın, A. (2012) · Van Yüzüncü Yıl University Academic Data Management System · Cited by 15
Asiri, Y., Millard, D. E., & Weal, M. J. (2021) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2021.3104817 · Cited by 13
Montgomery, G. K. (1977) · Psychophysiology · doi:10.1111/j.1469-8986.1977.tb01170.x · Cited by 11
Avcı, Z. Y., & Osman, R. (2019) · Educational Studies · doi:10.1080/03055698.2019.1702509 · Cited by 4
Research works printed but not linked: 2
- 10.18260/1-2--34923 · DOI
- Archambault, The preparation and perspective of online K-12 teachers in Nevada, 1836
Cited by
Sorted by citations
Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105224 · Cited by 531
Artificial intelligence in teaching and teacher professional development: A systematic review
Tan, X., Cheng, G., & Ling, M. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100355 · Cited by 420
Wang, J., & Fan, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04787-y · Cited by 322
RetractedKong, S.-C., & Yang, Y. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3392830 · Cited by 298
Wu, D., Zhang, S., Ma, Z., Yue, G. X.-G., & Dong, R. K. (2024) · Systems · doi:10.3390/systems12090332 · Cited by 127
Metadata
- Authors
- Lu, J., Zheng, R., Gong, Z., & Xu, H.
- Affiliations
- Hangzhou Normal University (CN)
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2024
- Volume
- 17
- Pages
- 1267-1277
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W4392173880
- Cited by
- 144
- References
- 43
- Citations by year
- 2026: 65 · 2025: 65 · 2024: 14
- FWCI
- 54.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion)
- Funders
- Ministry of Education's Artificial Intelligence to Promote the Construction of Teachers' Team
- Major Humanities and Social Sciences Research Projects in Zhejiang Higher Education Institutions (2023GH005)
- Teaching Academic and Reform Project of Hangzhou Normal University
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Teacher Education and Leadership Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, teacher professional development, pre-service teachers, teacher self-efficacy, higher-order thinking skills, teaching skills training, mathematics teachers, science teachers, informatics teachers, initial teacher education
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
