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.
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These interventions embedded GenAI within learning platforms to generate real-time hints, guiding questions, automated scoring, or corrective feedback.
“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]
These interventions were frequently embedded in performance-based assessments supported by CT-aligned rubrics.
“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]
These cognitive affordances are amplified when paired with pedagogical intentionality.
“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]
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Validity and Trustworthiness of AI-Generated Information in an Online Nutrition Course
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- 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
- Physical Sciences › Computer Science › Information Systems › 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 ›
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