<![CDATA[Integrating cooperative learning with artificial intelligence (AI)-based technology offers opportunities to strengthen interaction, feedback, and student engagement, but empirical evidence from secondary school economics education remains limited. This study examined changes in learning motivation and economics learning outcomes following the implementation of Student Teams Achievement Divisions (STAD) integrated with Wayground AI at SMAN 1 Nanga Pinoh. A quantitative pre-experimental design involved 144 Grade X students. Motivation was assessed using a four-point Likert-scale questionnaire with a Cronbach’s alpha coefficient of 0.993, while learning outcomes were measured using a 25-item multiple-choice test covering cognitive levels C2–C5. Data were analyzed using descriptive statistics, paired-samples t-tests, 95% confidence intervals, and paired-samples Cohen’s d in IBM SPSS Statistics 31. Mean motivation scores increased from 98.22 (SD = 15.01) to 137.25 (SD = 4.73), representing a mean gain of 39.03 (|t|(143) = 26.04, p < .001, d = 2.17). Mean learning outcome scores increased from 58.53 (SD = 3.68) to 87.32 (SD = 1.29), representing a mean gain of 28.79 (|t|(143) = 90.86, p < .001, d = 7.57). These findings document substantial within-group improvements following the intervention. However, the absence of a control group limits interpretation to pretest–posttest changes and prevents definitive attribution of these gains to the intervention. The findings suggest that STAD integrated with Wayground AI has potential as an active, structured, and responsive approach to economics instruction through interactive assessment and digital feedback.]]>
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