AI ‐Enhanced Gamification in Education: Developing and Validating a Scale for Measuring Engagement and Motivation Among Secondary School Students: Insights From the Network Analysis Perspective

ABSTRACT This study aimed to develop and validate the Gamified AI Learning Engagement and Motivation Scale (GALEMS) to assess secondary school students' motivation and engagement within AI‐enhanced gamified learning environments in Jordan. The research employed a sequential exploratory mixed methods design grounded in Self‐Determination Theory and Flow Theory. In the qualitative phase, semi‐structured interviews with 20 experts led to the identification of six key dimensions: Motivational Resonance, Learning Flow Experience, AI‐Driven Social Dynamics, Cognitive Immersion, Reflective Metacognition and Learning Self‐Efficacy. An initial 47‐item scale was constructed and refined through expert validation. In the quantitative phase, 640 secondary students participated, with the sample split into two groups ( n = 320 each) for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). EFA revealed a six‐factor structure explaining 52.3% of the total variance, and CFA confirmed good model fit (CFI = 0.940, RMSEA = 0.067, SRMR = 0.039). The final scale included 33 items, all with factor loadings > 0.40. Internal consistency was confirmed (Cronbach's α and McDonald's ω > 0.70), and test–retest reliability over 2 weeks yielded high correlations (e.g., GALEMS total score r = 0.92, p < 0.001). Multi‐group invariance testing demonstrated measurement consistency across gender (ΔCFI < 0.004). Exploratory Graph Analysis (EGA) with 1000 bootstrap samples confirmed the stability of the six‐dimensional structure in 99.8% of iterations. GALEMS provides a culturally grounded, psychometrically sound tool for evaluating motivation and engagement in AI‐based gamified learning environments.