Enhancing M Enhancing mathematics problem-solving skills in AI-driven environment: Integrated SEM-neural network approach
Anass Bayaga
Abstract
This study explores the nexus of gamification, artificial intelligence (AI), and mathematics cognition. Sample size of 71 responded in an intervention using game-based learning (GBL) approach. The purpose of designing the GBL was to enhance computational thinking and mathematical skills. The research employed multigroup partial least squares structural equation modelling (MGA-PLS-SEM) and artificial neural networks (ANN) through multilayer perceptron (MLP) as data analysis technique. The findings showed significant positive influence on class engagement, attitudes toward mathematics, as well as student performance. The analysis also revealed gender-related variations, which affirmed the model's consistency across diverse groups. The study validated the hypothesis and consequently advocated for the transformative potential of gamification, in preparation of 21st-century learners for AI-driven digital landscape. The implications are to ensure the integration of gamified elements into educational strategies, benefiting educators, curriculum developers, and policymakers resonating strongly for educators, curriculum developers, and policymakers. • AI-Gamification Integration: Combines SEM and ANN to study AI-enabled gamification in math education. • Gender Variations: Finds stronger positive effects of gamification on female learners. • Improved Engagement: Demonstrates significant boosts in student engagement and performance. • Analytical Rigor: Validates model reliability using MGA-PLS and ANN techniques. • Actionable Insights: Recommends tailored gamification strategies for educational improvement.