Evaluating Educational Game Design Through Human–Machine Pair Inspection: Case Studies in Adaptive Learning Environments

Educational games often fail to effectively merge game mechanics with educational goals, lacking adaptive feedback and real-time performance monitoring. This study explores how Human–Computer Interaction principles and adaptive feedback can enhance educational game design to improve learning outcomes and user experience. Four educational games were analyzed using a mixed-methods approach and evaluated through established frameworks, such as the Serious Educational Games Evaluation Framework, the Assessment of Learning and Motivation Software, the Learning Object Evaluation Scale for Students, and Universal Design for Learning guidelines. In addition, a novel Human–Machine Pair Inspection protocol was employed to gather real-time data on adaptive feedback, cognitive load, and interactive behavior. Findings suggest that Human–Machine Pair Inspection-based adaptive mechanisms significantly boost personalized learning, knowledge retention, and student motivation by better aligning games with learning objectives. Although the sample size is small, this research provides practical insights for educators and designers, highlighting the effectiveness of adaptive Game-Based Learning. The study proposes the Human–Machine Pair Inspection methodology as a valuable tool for creating educational games that successfully balance user experience with learning goals, warranting further empirical validation with larger groups.