Investigating the impact of professional development on teachers' AI competency and their teaching performance in higher education : the mediating role of TPACK /

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Student Theses

2025

Tan, Xiao

Hong Kong : The Education University of Hong Kong

Thesis

The development of teacher profession development (PD) centred on artificial intelligence (AI) technology is an emerging priority in education. The acceleration of generative and data-driven AI tools has increased teachers' demand for such training. However, there is still a lack of comprehensive, evidence-based research exploration on how university teachers can enhance their AI competency. This research project comprises three interrelated studies: a systematic review and two empirical studies. The research project aims to identify an effective pathway in a university contexts to enhance teachers' AI competency and further improve their teaching practice. First, the research systematically reviewed 95 empirical studies published between 2015 and 2024. The review identified three key imbalances: (1) research focus - 65% on teachers' AI teaching applications compared to 35% on teachers' professional development; (2) ethical considerations - the PD design lacks attention to responsible AI issues; and (3) methodological orientation - exploratory design predominated over interventional verification. These gaps indicate an urgent need for theory-based PD programmes in higher education that address moral challenges and enhance teachers' AI competency, technological pedagogical content knowledge (TPACK), and teaching performance. Based on this, two empirical studies were conducted. The first administered a questionnaire to 247 university teachers and analyzed the pathway from AI competency to TPACK to teaching performance using a structural equation modelling. The results show that teachers' AI competency indirectly affects teaching performance through TPACK, highlighting the key mediating role of TPACK. The second transformed the model into a six-month PD programme and evaluated its effectiveness through a design of pre-/post-test design with experimental and control groups: 64 teachers in the experimental group participated in PD, while 61 teachers in the control group did not receive the PD intervention. After controlling for the pre-test differences, the experimental group showed significantly greatere gains in the Intelligent Technological Knowledge (AITK) and Intelligent Technological Pedagogical Knowledge (AITPK) than the control group. Attendance rate emerged as a key predictor of enhancement. Interestingly, negative "gain" scores, interpreted as a metacognitive calibration consistent with the Dunning-Kruger effect, emphasized the necessity of an iterative PD programme cycle to normalize teachers' evolving self-awareness. Overall, this research project provides a comprehensive account of how AI empowers teaching and professional development across a systematic review (Study 1), a mediation study (Study 2), and an intervention study (Studies 3). It offers evidence for a mechanism linking teachers' AI competency to outcome, provides higher education institutions with a scalable PD programme blueprint, and offers university teachers practical guidance for integrating AI in teaching

LG51.H43 Dr 2025eb Tanx

https://educoll.lib.eduhk.hk/records/dAQHPEE6