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The Effects of Agent Type and Feedback Style on Self-Directed Learning: A Mixed-Methods Study
1Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China.
Behavioral Sciences (Basel, Switzerland)
|July 28, 2026
Summary
Customized artificial intelligence (AI) agents provide more accurate feedback than general-purpose ones. The effectiveness of AI feedback in self-directed learning (SDL) depends on agent type, feedback style, and learner evaluation.
Area of Science:
- Artificial Intelligence in Education
- Human-Computer Interaction
- Self-Directed Learning
Background:
- Learner engagement with artificial intelligence (AI)-generated feedback is crucial for self-directed learning (SDL).
- Understanding how agent type and feedback style influence this engagement is essential for optimizing AI in educational settings.
Purpose of the Study:
- To investigate the association between agent type (customized vs. general-purpose) and feedback style (Socratic vs. directive) with learner engagement with AI-generated feedback in SDL.
- To analyze patterns in feedback quality, self-regulatory behaviors, learning experiences, and learning outcomes.
Main Methods:
- A 2x2 mixed factorial experiment involving 51 postgraduate students completing instructional design tasks.
- Quantitative analysis of feedback accuracy, specificity, comprehension monitoring, and cognitive load.
- Qualitative analysis of learner reflection, feedback adoption, and trust in AI agents.
Main Results:
- Customized agents produced more accurate and specific feedback than general-purpose agents.
- Socratic feedback enhanced comprehension monitoring, while directive feedback increased cognitive load.
- An interaction effect showed customized agents improved learning outcomes under directive, but not Socratic, feedback.
Conclusions:
- AI feedback effectiveness is influenced by agent customization, feedback style, and learner's evaluation of feedback accuracy and alignment.
- Socratic feedback promotes deeper reflection, whereas directive feedback offers actionable task completion guidance.
- Learner trust in AI is contingent on clear, contextually relevant feedback from customized agents.
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