Artificial Intelligence Competence Levels Among Secondary Mathematics Teachers in Northern Samar, Philippines
DOI:
https://doi.org/10.70232/jrep.v3i3.197Keywords:
AI Competency Framework for Teachers, Artificial Intelligence, Educational Technology, Mathematics TeachersAbstract
As Artificial Intelligence (AI) tools become more common in schools, mathematics teachers are increasingly expected to use them in ways that support learning without weakening reasoning or academic integrity. However, baseline evidence on the AI competence of K to 12 mathematics teachers and the profile factors associated with it remains limited, especially in resource-constrained settings. This study examined the relationship between the teaching-related profile and AI competence of public secondary mathematics teachers using UNESCO’s AI Competency Framework for Teachers (AI-CFT). A cross-sectional descriptive-correlational survey was conducted among 215 public secondary mathematics teachers in the Department of Education (DepEd) Schools Division of Northern Samar during School Year 2025–2026. The instrument measured educational attainment, years of teaching experience, teaching position, relevant AI/ICT-related training exposure, access to AI tools, and AI competence across five AI-CFT domains. Data were analyzed using descriptive statistics and correlational procedures. Results showed that respondents were largely mid-career, mostly in Teacher I to III positions, had limited AI/ICT-related training exposure, and largely reported access to at least one AI tool (91.2%). Overall AI competence was at the Competent level (x̄ = 3.53, SD = 0.75), with relatively stronger ratings in Human-Centered Mindset (3.71), AI Pedagogy (3.58), and AI Ethics (3.55), but lower ratings in AI Foundations and Applications (3.43) and AI for Professional Development (3.39). Educational attainment and teaching position were not significantly related to AI competence, while years of teaching experience showed a small negative relationship (r = −0.229, p < .001). In contrast, training exposure (rₛ = 0.293, p < .001) and access to AI tools (rₛ = 0.476, p < .001) were positively related to AI competence. The findings support targeted AI upskilling and equitable tool access to strengthen responsible AI integration in mathematics instruction.
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