Students were often overconfident about their understanding of how to give peer feedback after chatbot instruction¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Participants who judged they knew how to give a feedback type often answered CCQs incorrectly or failed to revise fabricated feedback correctly, indicating overconfidence. → Michael Pin-Chuan Lin 2025

Evidence¶

Michael Pin-Chuan Lin 2025¶

Michael Pin-Chuan Lin, Daniel H. Chang, Philip H. Winne. (2025). A proposed methodology for investigating student-chatbot interaction patterns in giving peer feedback. Education Tech Research Dev. https://doi.org/10.1007/s11423-024-10408-3

q2 · i? · design · r2

Analysis of 23 undergraduates' chatbot interaction logs in a first-year educational psychology course tutorial; the authors interpret the pathway data as showing misjudged understanding, quoting "over-confident in their judgement". No effect size is printed.

"Our preliminary findings suggest participants were over-confident in their judgement as they often misjudged their understanding of how to provide feedback."

Discussion¶

Learner Variables¶

• Metacognition — outcome: instruction changes it

Related Claims¶

• Practice with feedback, regardless of feedback type, improves metacognitive calibration relative to lecture, reducing overconfidence by about 13 percentage points — related
• Chatbot instruction appeared effective for participants lacking prior knowledge of posing questions for improvement (path J, n = 16) — related
• Most participants rejected the chatbot's guidance when revising fabricated peer feedback — related
• Students perceive LLM chatbots as useful for learning but requiring critical use, reporting hallucinations, overconfidence, and dependency risks — related