The Status Quo and Future of AI-TPACK for Mathematics Teacher Education Students: A Case Study in Chinese Universities

TL;DR

The paper investigates the status, determinants, and development of AI-TPACK among mathematics teacher education students (MTES) in Chinese universities. It develops and validates a dedicated AI-TPACK scale, then analyzes MTES across seven universities (n=412) to assess AI-TPACK, self-efficacy, and teaching beliefs, employing a novel AI-TPACK-SEM to model interrelations. Key findings show MTES operate at a medium level of AI-TPACK, with self-efficacy positively predicting AI-TPACK components while teaching beliefs can negatively influence AI-TPCK-related pathways; differences across grades are not observed for AI-TPACK but are evident for self-efficacy and teaching beliefs. The study offers practical curriculum recommendations and a methodological framework for future MTES AI-TPACK research, aiming to support digital transformation in mathematics education in China.

Abstract

As artificial intelligence (AI) technology becomes increasingly prevalent in the filed of education, there is a growing need for mathematics teacher education students (MTES) to demonstrate proficiency in the integration of AI with the technological pedagogical content knowledge (AI-TPACK). To study the issue, we firstly devised an systematic AI-TPACK scale and test on 412 MTES from seven universities. Through descriptive statistical analyses, we found that the current status of AI-TPACK for MTES in China is at a basic, preliminary stage. Secondly, we compared MTES between three different grades on the six variables and found that there is no discernible difference, which suggested that graduate studies were observed to have no promotion in the development of AI-TPACK competencies. Thirdly, we proposed a new AI-TPACK structural equation model (AI-TPACK-SEM) to explore the impact of self-efficacy and teaching beliefs on AI-TPACK. Our findings indicate a positive correlation between self-efficacy and AI-TPACK. We also come to a conclusion that may be contrary to common perception, excessive teaching beliefs may impede the advancement of AI-TPACK. Overall, this paper revealed the current status of AI-TPACK for MTES in China for the first time, designed a dedicated SEM to study the effect of specific factors on AI-TPACK, and proposed some suggestions on future developments.

Figures (10)

• Figure 1: TPACK and AI-TPACK framework.
• Figure 2: MTES's AI-TPACK research framework.
• Figure 3: Hypothetical paths of AI-TPACK-SEM.
• Figure 4: Exploratory factor analysis results (only factor loadings greater than 0.4 are shown).
• Figure 5: Descriptive statistics (M, SD) of six variables.