AI literacy, preparedness, and use motives of elementary education pre-service teachers of a state university in the Philippines

Marjon Malacapay ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 9, 1120-1136

Abstract: Digital competence in the 21st century is a crucial aspect for future educators. However, some pre-service teachers still lack this competence, particularly in the use of artificial intelligence (AI). This study aimed to assess the perceptions of 46 pre-service elementary teachers regarding their literacy levels, preparedness, and motivations for using AI. The research employed an explanatory sequential mixed-method design, collecting quantitative data through three adapted survey questionnaires from students enrolled at a state university in the Philippines for the academic year 2024-2025. Subsequently, qualitative interviews were conducted with eight participants to provide context and deeper insights. Quantitative data were analyzed using descriptive statistics (mean and standard deviation) and inferential statistics (regression analysis), while qualitative data were thematically analyzed. The results indicated that the participants' AI literacy was above average. They also demonstrated a moderate level of preparedness and showed openness to integrating AI into classroom practices. However, some participants expressed uncertainty about AI usage. Regression analysis revealed that preparedness, more than literacy, significantly predicts motivation to use AI. Thematic analysis of interview data identified key themes, including foundational awareness with conceptual gaps, enthusiasm coupled with uncertainty, ethical concerns, inadequate institutional guidelines, and a strong desire for AI integration. These findings highlight the necessity for Teacher Education Institutions (TEIs) to implement structured, ethically grounded AI training programs for pre-service elementary education teachers. Such initiatives are essential to foster confident, responsible, and innovative educators capable of thriving in an AI-driven future.

Keywords: Generative AI; Higher education; Mixed-methods; Negros Occidental; Tertiary education. (search for similar items in EconPapers)
Date: 2025
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