Artificial intelligence literacy among Chinese Master of Education students: Current situation, influencing factors and generative pathways

Abstract

As future high-quality educators, Master of Education students’ levels of artificial intelligence literacy (AIL) directly influence the quality of future talent cultivation. The study, grounded in the technological pedagogical content knowledge framework and the unified theory of acceptance and use of technology, employed covariance-based structural equation modelling and fuzzy-set qualitative comparative analysis to empirically examine the AIL of 1,575 Master of Education students in China. The findings reveal that the overall level of AIL is moderate. Students scored lower in knowledge, competence and thinking dimensions than in attitudes and ethics. Significant differences in AIL were observed across gender, institutional type, affiliation and regional location. Effort expectancy, performance expectancy, behavioural intention, facilitating conditions, social influence and technological pedagogical content knowledge all showed significant positive effects on AIL. Moreover, AIL is shaped by the complex interplay of multiple influencing factors. The high AIL level includes three generative paths: external support-driven, cognitive-social synergy and self-directed development. Conversely, the low AIL level includes two generative paths: weak cognitive ability and motivation-social deficit.

Implications for practice or policy:

• Universities should require AIL in all Master of Education programmes through discipline-integrated modules.
• Governments must reduce regional disparities by funding digital infrastructure and collaborative platforms for under-resourced institutions.
• Universities should tackle gender gaps via targeted support, peer mentoring and inclusive AI teaching practices.
• Universities should partner with technology companies to develop adaptable, secure AI tools suited to varied disciplines and teaching contexts.