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<p>This study investigates the impact mechanism of artificial intelligence (AI) literacy on the development of technological pedagogical content knowledge (TPACK) among pre-service teachers. It explores the mediating role of deep learning (DL) in the relationships between perceived usefulness (PU), perceived ease of use (PEU), perceived artificial intelligence literacy (PAIL), and TPACK competence, while also revealing the moderating effect of AI literacy on the relationship between DL and TPACK. Grounded in the technology acceptance model (TAM), the TPACK framework, and constructivist learning theory, the research analyzes data from 423 pre-service teachers engaged in blended learning through Structural Equation Modeling (SEM). Key results include: DL significantly mediates the effects of PU, PEU, and PAIL on TPACK; AI literacy positively moderates the impact of deep learning on TPACK, with high-literacy teachers demonstrating significantly greater knowledge transformation efficiency; PAIL directly and positively predicts TPACK competence. The study validates the technology acceptance, deep learning and competence integration pathway and highlights AI literacy’s dual role as both a mediating bridge and a moderating catalyst in blended teaching.</p>

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Can AI literacy empower future educators? unpacking its dual role in transforming deep learning into TPACK competence within blended classrooms

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Jingxian Zhao

摘要

This study investigates the impact mechanism of artificial intelligence (AI) literacy on the development of technological pedagogical content knowledge (TPACK) among pre-service teachers. It explores the mediating role of deep learning (DL) in the relationships between perceived usefulness (PU), perceived ease of use (PEU), perceived artificial intelligence literacy (PAIL), and TPACK competence, while also revealing the moderating effect of AI literacy on the relationship between DL and TPACK. Grounded in the technology acceptance model (TAM), the TPACK framework, and constructivist learning theory, the research analyzes data from 423 pre-service teachers engaged in blended learning through Structural Equation Modeling (SEM). Key results include: DL significantly mediates the effects of PU, PEU, and PAIL on TPACK; AI literacy positively moderates the impact of deep learning on TPACK, with high-literacy teachers demonstrating significantly greater knowledge transformation efficiency; PAIL directly and positively predicts TPACK competence. The study validates the technology acceptance, deep learning and competence integration pathway and highlights AI literacy’s dual role as both a mediating bridge and a moderating catalyst in blended teaching.