Investigating the mediating role of TPACK on teachers’ AI competency and their teaching performance in higher education
Open accessAbstract
The growing integration of artificial intelligence (AI) in education has significantly influenced teachers' professional development. While AI offers substantial support for teaching practices and serves as a powerful instructional aid, it also places greater demands on teachers, prompting an urgent need for role transformation and skill enhancement. Therefore, the interplay among teachers' AI competency, technological pedagogical content knowledge (TPACK), and teaching performance has emerged as a critical area of research. This study employed a mixed-methods approach, combining questionnaire surveys with classroom observations, to assess the AI competency, TPACK levels, and teaching performance of 247 university teachers. Structural equation modelling (SEM) was used to analyze their relationships among the core constructs. The results revealed that teachers' AI competency positively predicted their TPACK levels, which in turn positively predicted teaching performance. Furthermore, TPACK was found to mediate the relationship between AI competency and teaching performance. Theoretically, this study elucidates the interconnections among teachers' AI competency, TPACK, and teaching performance, with a particular emphasis on the mediating role of TPACK. Practically, the findings offer implications for the design of professional development programmes aimed at strengthening teachers' AI competency, as well as for the development of data-informed teaching performance evaluation sy…
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- Authors
- Tan, X., Cheng, G., & Ling, M. H.
- Affiliations
- Education University of Hong Kong (HK)
- Corresponding author
- Cheng, Gary
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 9
- Pages
- 100461
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4413190322
- Cited by
- 27
- References
- 76
- Citations by year
- 2026: 24 · 2025: 3
- FWCI
- 28.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc)
- Funders
- The Education University of Hong Kong
- Education University of Hong Kong (02181)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Mathematics Education and Teaching Techniques
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Keywords
- TPACK, technological pedagogical content knowledge, AI competencies, teaching performance, higher education teachers, mediating effect, teacher professional development, artificial intelligence in 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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