Sanddy H. Madera^1,, Jennifer O. Parcutilo¹

The lack of practical and sustainable strategies for incorporating error analysis into mathematics classrooms is a significant research gap in mathematics education examined in this systematic mapping review. In order to determine common instructional goals, sample characteristics, mathematical domains, research design, strategies, types of errors addressed, and reported learning outcomes, thirteen empirical studies published between 2017 and 2025 were analyzed. The findings show that while error analysis improves problem-solving skills, metacognitive development, and conceptual understanding, its use can be demanding for teachers and students. Students often struggle to analyze and articulate their errors, notably when guidance is lacking, and teachers must deal with heavier workloads, time restraints, and specialized training requirements. While international research emphasizes the potential of artificial intelligence tools to support real-time feedback and lower instructional demands, local studies mainly rely on conventional, teacher-led methods. The review recommends a hybrid model that blends AI-enhanced tools with teacher facilitation to support error analysis's scalable, efficient, and context-responsive integration in mathematics classrooms.

error analysis, mathematics classroom innovation, AI in mathematics education