Tutors perceive Tutor CoPilot as helpful for on-the-spot explanations but flag suggestions that are not grade-level appropriate and 'too smart'¶

Claim · All claims
Evidence · 1 study (2 entries) · causal r3 · q1
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person · recheck by 2028: tool and model accuracy changes quickly

Subclaims¶

q1 i? Treatment tutors generally found Tutor CoPilot helpful, particularly for well-phrased explanations and breaking down complex concepts on the spot. → Rose E. Wang 2025 q1 i? Tutors reported that suggestions occasionally lacked alignment with students' grade levels and were sometimes perceived as 'too smart,' requiring simplification and adaptation. → Rose E. Wang 2025 (2)

Evidence¶

Rose E. Wang 2025¶

Rose E. Wang, Ana T. Ribeiro, Carly D. Robinson, Susanna Loeb, and Dora Demszky. (2025). Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise. arXiv:2410.03017v2. https://arxiv.org/abs/2410.03017 doi:10.48550/arxiv.2410.03017

q1 · i? · causal · r3

Qualitative interviews with approximately 20 treatment tutors conducted a week after the study concluded, exploring usage and how use evolved over time. The article reports themes, including usefulness for explaining difficult topics such as differentiating mean and median. No effect size applies.

"Tutors generally found Tutor CoPilot helpful, particularly for its ability to provide well-phrased explanations and break down complex concepts on the spot."

Rose E. Wang 2025 (2)¶

Rose E. Wang, Ana T. Ribeiro, Carly D. Robinson, Susanna Loeb, and Dora Demszky. (2025). Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise. arXiv:2410.03017v2. https://arxiv.org/abs/2410.03017

q1 · i? · qualitative · r1

Same interview study with treatment tutors. Tutors noted areas for improvement, mentioning that the tool's suggestions occasionally lacked alignment with students' grade levels. This is qualitative self-report; no effect size applies.

"A common issue was that the responses were sometimes perceived as "too smart," requiring tutors to simplify and adapt them for clarity."

Discussion¶

Related Claims¶

• Tutors with Tutor CoPilot access use more understanding-fostering strategies (e.g., prompting students to explain) and rely less on giving away answers than control tutors — related
• Tutor CoPilot's benefit is largest for lower-rated and less-experienced tutors (9 p.p. and 7 p.p.), diminishing as tutor effectiveness increases — related
• Students whose tutors have access to Tutor CoPilot are 4 percentage points more likely to master lesson topics (pass exit tickets) in live K-12 math tutoring — related
• Tutor CoPilot shows no statistically significant effects on participation points, student survey outcomes, or end-of-year math test scores — related
• Human oversight remained necessary alongside AI-based tutoring, especially for misconceptions and advanced topics — related