Same Feedback, Different Source: How AI Vs. Human Feedback Shapes Learner Engagement

Date

02/2026

AI Overview

Why it matters

Prior studies do not control for identical feedback content when comparing AI and human feedback. Understanding how believing feedback comes from AI versus a human shapes learner engagement is essential for designing hybrid educational systems.

Study at a glance

Question: Does telling learners feedback comes from an AI versus a human teaching assistant affect their engagement and feedback ratings?
Setting and data: Online creative coding tutorial, 25 adult participants recruited via Prolific.
Method: Online experiment (2x2 mixed design).
AI tools: Claude Sonnet 4

Key findings

Learners told feedback came from a human teaching assistant spent significantly more time on tasks than those told it came from AI, despite receiving identical feedback.
Learners in both conditions rated technical and creative feedback helpfulness similarly, showing no significant preference for human over AI feedback across task types.
AI feedback ratings were strongly predicted by prior trust in AI, whereas teaching assistant feedback ratings were predicted by perceived feedback genuineness.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

When learners receive feedback, what they believe about its source may shape how they engage with it. As AI is used alongside human instructors, understanding these attribution effects is essential for designing effective hybrid AI-human educational systems. We designed a creative coding interface that isolates source attribution while controlling for content: all participants receive identical LLM-generated feedback, but half see it attributed to AI and half to a human teaching assistant (TA). We found two key results. First, perceived feedback source affected engagement: learners in the TA condition spent significantly more time and effort (d = 0.88-1.56) despite receiving identical feedback. Second, perceptions differed: AI-attributed feedback ratings were predicted by prior trust in AI (r = 0.85), while TA-attributed ratings were predicted by perceived genuineness (r = 0.65). These findings suggest that feedback source shapes both engagement and evaluation, with implications for hybrid educational system design.