FROM ANSWER GENERATION TO CONCEPTUAL UNDERSTANDING: A MIXED-METHODS INVESTIGATION OF GENERATIVE AI-SUPPORTED MATHEMATICS LEARNING - Adi Candra Kusuma; Nurul Saila; Sri Asih Agustin (INJOSEDU: International Journal of Social and Education, 2026)
The development of Generative Artificial Intelligence has brought significant changes to mathematics learning practices, particularly in providing fast and interactive support for students. However, the effectiveness of Generative AI is measured not only by its ability to generate answers but also by its contribution to the development of in-depth mathematical conceptual understanding. This study aims to examine the role of Generative AI in supporting the transition of mathematics learning from simply generating answers to strengthening students' conceptual understanding. The study used a literature review with a mixed-methods approach that integrates quantitative and qualitative findings from various relevant scientific publications. The results indicate that Generative AI has great potential to increase access to mathematical explanations, provide personalized feedback, and help students understand concepts through diverse representations and reasoning. On the other hand, excessive reliance on AI-generated answers has the potential to reduce students' cognitive engagement and hinder the development of critical thinking skills if not accompanied by appropriate pedagogical strategies. This study provides theoretical and practical implications for the development of mathematics learning models that effectively utilize Generative AI to support sustained conceptual understanding.