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

Synthesis: Explaining learning engagement in AI-assisted learning — a structural equation modeling study of 758 Chinese university students in AI-assisted English learning. Guided by goal-setting theory, Wang & Wang (2026) show how teacher support drives learning engagement through four achievement goal orientations, extending goal-setting theory to AI contexts.

Wang & Wang (2026) address an under-explored question: not just whether AI helps language learning, but the psychological mechanisms that drive students' engagement in AI-assisted environments. The study integrates teacher support, achievement goals, and engagement into one framework, and tests which goal orientations mediate the teacher-support → engagement link.

Method

• Design: Structural equation modeling of survey data.
• Sample: 758 Chinese university students in AI-assisted English learning.
• Framework: Goal-setting theory, with four achievement goal orientations (mastery-approach, performance-approach, mastery-avoidance, performance-avoidance).

Key Findings

• Teacher support predicted the approach goals (mastery-approach and performance-approach) but not the avoidance goals (mastery-avoidance, performance-avoidance), and directly enhanced engagement.
• Engagement predictors: mastery-approach, performance-approach, and performance-avoidance goals all positively predicted engagement.
• Mediation: only mastery-approach and performance-approach goals served as significant mediators between teacher support and engagement.

Implications

• For higher education and language educators: teacher support is a lever for engagement in AI-assisted learning, working through students' mastery- and performance-approach goals — so supporting teachers to foster approach-oriented goals matters for AI tool uptake.
• For Motivation theory: extends goal-setting theory to AI-assisted language learning, showing approach goals (not avoidance goals) carry the teacher-support effect.
• For teachers: in AI-rich courses, explicit encouragement of mastery and performance-approach goals can amplify the engagement benefits of AI-assisted tools.

Connected Concepts

• Student Engagement
• Motivation
• Language Learning
• English Education (EAP / EFL / ESL)
• Higher Education
• Self-Determination Theory
• Teaching
• AI Feedback Quality

Connected Articles

• Beyond Task Completion: A Theoretical Integration and Framework for Guiding Students' ChatGPT Use for Learning — Theoretical framework for guiding students' ChatGPT use
• From disengaged to self-determined: a latent transition analysis of students' AI learning motivation — SDT latent transition analysis of AI learning motivation
• Students' engagement with generative AI in academic learning: A self-determination theory and epistemic network analysis study — SDT and student engagement with GenAI

Citation

Wang, Y., & Wang, Y. (2026). Explaining learning engagement in AI-assisted learning through teacher support and achievement goals: insights from goal-setting theory. Humanities and Social Sciences Communications, 13(1215).