Unpacking Reductionism in AI-Driven Mathematics Education: A Factor Analysis of Educators’ Insights and Applications

This study investigates the phenomenon of reductionism in AI-driven mathematics education by developing and validation a comprehensive survey instrument grounded in secondary educators’ perceptions. Drawing on a positivist framework, the research surveyed 59 mathematics teachers in Ifugao to assess their understanding of AI’s procedural tendencies, perceived benefits, potential drawbacks, and recommendations. Instrument development followed a rigorous sequence of item generation, expert validation, and pilot testing, culminating in a total of 75-item questionnaire. EFA using principal axis factoring with oblimin rotation identified nine coherent factors namely educators’ understanding of reductionism, perceived benefits, 6 drawbacks (Conceptual Dilution, Over-Reliance on Technology, Fragmentation of Learning, Decreased Metacognition Engagement, Loss of Mathematical Rigor, and Shallow Learning Outcomes), and Future Implications and Recommendations, accounting for 74.3% of total variance. The Cronbach’s alpha .83 to 0.91 showed that each subscale has high internal consistency. Scrutiny of the data showed that although Ifugao math teachers appreciate AI for scaffolding, visualization, and feedback, they still have major concerns about its tendency to fragment knowledge, compromise metacognition, and encourage shallow learning. Additionally, it was found that a planned, teacher-made integration maintains conceptual depth and helps learners to appreciate the true effectiveness of artificial intelligence. With these, it is recommended that educators shall engage in continuous professional development for them to be more equipped with the skills aligned with the use of AI. The verified instrument provides both researchers ad educators a consistent tool for continuous improvement on the use of AI in mathematics instruction.