Integrating realistic mathematics education, AI, and gamification to enhance students’ learning motivation and problem-solving skills

Abstract

The integration of artificial intelligence (AI) and gamification within the framework of realistic mathematics education (RME) presents substantial potential to foster meaningful, innovative, and adaptive learning experiences. Such integration can enhance students’ motivation and promote active engagement in solving nonroutine mathematical problems. Despite these opportunities, several challenges hinder the practical realization of this threefold integration. These include teachers limited digital literacy, the absence of pedagogical models that systematically merge AI and gamification within the RME framework, and the ongoing compartmentalization of these components in mathematics education practice. This study investigates how the synergy between AI and gamification-based scaffolding can support RME in enhancing students’ learning motivation and problem‑solving competence. A sequential explanatory mixed‑methods design was employed, involving 300 students from six Indonesian secondary schools. Data were gathered through mathematical problem‑solving tests and non‑test instruments, including classroom observations and semi‑structured interviews. Quantitative data were analyzed using Structural Equation Modeling (SEM), while qualitative data were examined through thematic analysis to contextualize and elaborate on the quantitative findings. The results reveal that RME supported by AI and APOS‑based transition strategies integrated with gamified elements significantly improves mathematical problem‑solving abilities (β = 0.40) and learning motivation (β = 0.35), yielding an overall effect size of β = 0.41. The findings demonstrate that AI‑infused, gamified RME environments can systematically foster students’ cognitive and affective engagement, thereby supporting both process‑ and outcome‑oriented dimensions of mathematics learning. This study contributes a replicable instructional design model that outlines explicit integration stages encompassing realistic learning contexts, AI‑driven adaptive support, and game mechanics that nurture sustained engagement and intrinsic motivation. The research yields theoretical and practical implications for advancing RME toward a more adaptive, student‑centered approach to mathematics learning, oriented toward meaningful and contextually rich problem‑solving in the digital era.