Explaining learning engagement in AI-assisted learning through teacher support and achievement goals: insights from goal-setting theory

Abstract While AI reshapes higher education, motivational mechanisms driving student engagement in AI-assisted language learning remain underexplored. Guided by Goal-setting Theory, this study investigates how teacher support influences learning engagement via four achievement goal orientations in AI-assisted English learning. Data from 758 Chinese university students were analyzed using structural equation modeling. Teacher support positively predicted mastery-approach and performance-approach goals (not mastery-avoidance/performance-avoidance) and directly enhanced engagement. Mastery-approach, performance-approach, and performance-avoidance goals positively predicted engagement; only mastery-approach and performance-approach served as significant mediators. Findings extend Goal-setting Theory to AI contexts and inform optimized teacher support and effective AI tool use in language education.