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AI Training and Science Student Teachers’ TPACK in Campus-Based and Distance Education: A Comparative Study

IMPACT SIGNAL86/100
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Information from the abstract

Despite AI’s growing educational potential, little is known about science student teachers’ preparedness to integrate it into classroom practice. This study examined whether AI training level and institutional context were associated with student teachers’ TPACK. Using a quantitative, comparative, descriptive survey design, data were collected from final-year Bachelor of Education science students at one campus-based and one distance education university in South Africa (n = 186). Final-year students were selected because they are nearing entry into the profession, which makes their self-reported readiness to integrate AI particularly informative. Statistical analyses included descriptive statistics, exploratory factor analysis, reliability testing, nonparametric comparisons, and regression analysis. The findings revealed that while 64% of participants at the campus-based university reported strong TPACK, only 47.4% at the distance university did so. Regression results indicated that AI training was associated with self-reported TPACK only among students at the distance university. Specifically, completing a short course was associated with significantly weaker self-reported TPACK than receiving no training (p = .039), whereas no significant association was observed at the campus-based university (p = .607). Given the modest variance explained, these associations are interpreted cautiously. Pedagogical knowledge was the weakest TPACK component across both institutions. The findings suggest that uniform AI training may not produce consistent outcomes across contexts. In South Africa, where teacher education includes both distance and campus-based provision amid uneven digital access, AI training should be context-responsive, infrastructure-sensitive, and pedagogically grounded.

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Why this record is monitored

This record has an Impact Signal of 86/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.

Related topics: Artificial Intelligence in Healthcare and Education · Digital literacy in education · Online Learning and Analytics

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Thai researcher and institutional participation

Prasart Nuangchalerm · Mahasarakham University

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