Artificial Intelligence and Gamification-Integrated Analytic Geometry Instruction: Design and Implementation in Higher Education - Tri Candra Wulandari; Sunismi Sunismi; Zainal Abidin (Jurnal Pendidikan Terapan, 2026)
Purpose: This study aims to: (1) design an Analytical Geometry learning model that integrates Artificial Intelligence (AI) and gamification, and (2) identify the strengths and limitations of AI in supporting mathematics learning, particularly within Analytical Geometry lectures in higher education. Methods: The study employed a qualitative Design-Based Research (DBR) approach that began from a classroom phenomenon and tested the design empirically in an authentic course. Participants were 20 undergraduate students from mathematics education study program. Data were collected through documentation, observation notes, and AI-generated outputs, then analyzed using descriptive, content and thematic analysis. Findings: The developed learning design includes three stages: Introduction, Main Points, and Closing. Gamification elements are embedded in all stages, while AI integration occurs primarily in the Main Points stage to support conceptual explanation of Wordwall-generated problems. AI proved effective in providing step-by-step problem-solving explanations but demonstrated limitations in geometric visual representation and mathematical notation accuracy. Wordwall served effectively as an assessment tool capable of identifying students’ learning difficulties. Research Implications: The findings suggest that integrating AI and gamification into Analytical Geometry learning is associated with improved instructional clarity and student engagement, although careful instructional planning is required to address AI’s limitations in visual and symbolic mathematical representation. Originality: This study contributes novelty by presenting a practical, systematically designed model for integrating AI and gamification into Analytical Geometry learning using DBR, documenting how lecturer can implement it in real classrooms and offering empirical insights into the capabilities and constrains of AI in higher-level mathematics instruction.