Enhancing teachers’ AI competency: A professional development intervention study based on intelligent-TPACK framework

With the rapid penetration of generative artificial intelligence (AI) in higher education, university teachers' AI competency has become a critical determinant of effective technology integration in teaching. However, systematic and empirically validated intervention frameworks to support the development of this competency remain scarce. To address this gap, this study implemented a six-month professional development (PD) programme grounded in the Intelligent-TPACK framework and evaluated its effectiveness using a quasi-experimental pre-test-post-test design. A total of 64 teachers participated in the PD programme (experimental group), while pre- and post-test data were also collected from 61 teachers who did not participate (control group). Results indicate that the PD programme significantly enhanced AI competency in the experimental group, particularly in the domains of AI Technological Knowledge (AITK) and AI Technological Pedagogical Knowledge (AITPK). After controlling for baseline differences using ANCOVA, the effect size remained above the moderate threshold. A mixed-designed ANOVA further confirmed a significant interaction effect between group and time, ruling out maturation effects. Multi-level regression analysis revealed that background variables such as teaching experience, discipline, and professional title had limited predictive power for AI competency gains. Notably, self-perceived participation level did not significantly predict outcomes, whereas attendance rate emerged as a significant positive predictor. Interestingly, negative gain scores were observed in both groups. Follow-up interviews indicated that these scores did not reflect an actual decline in AI competency but rather a metacognitive recalibration, in which teachers shifted from unconscious incompetence to conscious incompetence—a pattern consistent with the Dunning–Kruger effect. This finding offers a novel theoretical perspective on the mechanism of change underlying the intervention. Overall, the PD programme based on the Intelligent-TPACK framework effectively enhanced university teachers’ AI competency and provides a systematic and evidence-based model for future PD initiatives in the AI era.

Authors

Publication Details

Journal
Computers and Education Artificial Intelligence
Published
2025-12-01
DOI
https://doi.org/10.1016/j.caeai.2025.100521
Citations
2
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
4.73
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article

Enhancing teachers’ AI competency: A professional development intervention study based on intelligent-TPACK framework

Man Ho Ling, Xiao Jian Tan, Gary Cheng
2 citations
Computers and Education Artificial Intelligence
Ethics and Social Impacts of AI
4.73
article

Enhancing teachers’ AI competency: A professional development intervention study based on intelligent-TPACK framework

Man Ho Ling, Xiao Jian Tan, Gary Cheng
article en
2 citations

Abstract

With the rapid penetration of generative artificial intelligence (AI) in higher education, university teachers' AI competency has become a critical determinant of effective technology integration in teaching. However, systematic and empirically validated intervention frameworks to support the development of this competency remain scarce. To address this gap, this study implemented a six-month professional development (PD) programme grounded in the Intelligent-TPACK framework and evaluated its effectiveness using a quasi-experimental pre-test-post-test design. A total of 64 teachers participated in the PD programme (experimental group), while pre- and post-test data were also collected from 61 teachers who did not participate (control group). Results indicate that the PD programme significantly enhanced AI competency in the experimental group, particularly in the domains of AI Technological Knowledge (AITK) and AI Technological Pedagogical Knowledge (AITPK). After controlling for baseline differences using ANCOVA, the effect size remained above the moderate threshold. A mixed-designed ANOVA further confirmed a significant interaction effect between group and time, ruling out maturation effects. Multi-level regression analysis revealed that background variables such as teaching experience, discipline, and professional title had limited predictive power for AI competency gains. Notably, self-perceived participation level did not significantly predict outcomes, whereas attendance rate emerged as a significant positive predictor. Interestingly, negative gain scores were observed in both groups. Follow-up interviews indicated that these scores did not reflect an actual decline in AI competency but rather a metacognitive recalibration, in which teachers shifted from unconscious incompetence to conscious incompetence—a pattern consistent with the Dunning–Kruger effect. This finding offers a novel theoretical perspective on the mechanism of change underlying the intervention. Overall, the PD programme based on the Intelligent-TPACK framework effectively enhanced university teachers’ AI competency and provides a systematic and evidence-based model for future PD initiatives in the AI era.

Computers and Education Artificial IntelligenceVol. 9
Openalex Percentile: Top 4%
Ethics and Social Impacts of AI
4.73
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