Prior programming performance moderates the misalignment–performance association, with the cost concentrated in lower-performing teams¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A response-surface analysis found the interaction of the two partners' discrepancy perceptions negative and significant (bAB = −121.05, SE = 49.82, p = .015), with performance highest near mutual alignment. → Laura Graf 2026

Evidence¶

Laura Graf 2026¶

Laura Graf, Ramona Beinstingel, Stephan Krusche, Oleksandra Poquet. (2026). Students' Perception Accuracy of Partners' AI Use and its Relation to Collaboration Performance. Preprint, Technical University of Munich. https://arxiv.org/abs/2606.23237 doi:10.48550/arxiv.2606.23237

q2 · i? · associational · r2

Second-order polynomial response surface (Fig. 1) with heteroscedasticity-robust standard errors, described by the authors as an illustrative complement (model R² = .37, F = 10.30) given a modest sample and discrepancy scores concentrated near zero; regression coefficients are printed but no standardised effect size.

"Consistent with this pattern, the interaction term of student A AI use perception discrepancy and student B discrepancy was negative and significant (bAB = −121.05, SE = 49.82, p = .015), whereas the remaining polynomial terms were non-significant."

Discussion¶

Related Claims¶

• Greater early-project misalignment in partners' perceptions of each other's AI use is associated with lower final project scores — a broader claim this one bears on
• Teams whose perception of who uses more AI reversed between t1 and t3 achieved lower scores than teams with stable perceptions — related
• The effect of mathematics test practice varies across prior-achievement levels, with significant effects at intermediate-to-high levels (levels 3 and 4) but not at the lowest or highest — related