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All Journal Journal of Artificial Intelligence in Education & Learning Innovation (JAIELI)
Gampal , Nelvin
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Computer Science & IT Education Languange, Linguistic, Communication & Media Mathematics

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The future of teaching: Analyzing the interplay between AI literacy and TPACK among BEED pre-service teachers Aglibot, Karl Alvin; Rito, Chelsea Lorraine; Villalon, Larence; Gampal , Nelvin
Journal of Artificial Intelligence in Education & Learning Innovation Vol. 1 No. 1 (2025): Journal of Artificial Intelligence in Education & Learning Innovation
Publisher : CV Rezki Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56003/jaieli.v1i1.570

Abstract

Background: As artificial intelligence (AI) continues transforming the educational landscape, pre-service teachers must develop theoretical understanding and practical skills in AI integration. Objectives: This study explored the relationship between AI literacy and TPACK among 139 BEED pre-service teachers. Methods: Using descriptive statistics, Spearman's correlation, and multiple linear regression analysis, the study investigated how components of AI literacy relate to TPACK. Results: Results revealed that the pre-service teachers demonstrated high levels of AI literacy, particularly in ethical awareness and intrinsic motivation. Pre-service teachers expressed strong interest in AI's educational potential and a commitment to its responsible use. While their foundational AI knowledge was high, many reported moderate self-efficacy and lacked confidence in executing AI-related tasks. Gaps were also noted in applying AI concepts to real-world teaching scenarios and designing AI-driven solutions. Furthermore, the results showed high TPACK proficiency, with strength in pedagogical and technological knowledge, though weaknesses were observed in content areas like mathematics and technical troubleshooting. A moderate positive correlation (Spearman's ρ = 0.48, p < 0.05) between AI literacy and TPACK indicates a meaningful association. Regression analysis revealed that AI literacy components explained 25.5% of the variance in TPACK. Cognitive learning emerged as the only significant predictor. Conclusions: The findings underscore the need for teacher education programs to provide hands-on, cognitive-focused AI training. Hence, enhancing pre-service teachers' cognitive and practical skills is essential to thoroughly preparing them for AI-enhanced teaching environments' demands.
Co-Authors Aglibot, Karl Alvin Rito, Chelsea Lorraine Villalon, Larence