Claims

Within-user exercise accuracy does not differ significantly between high- and low-score sessions

Claim · All claims
Evidence · 1 study · causal r2 · q3
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q3 i? Within-user exercise accuracy differences between above-average and below-average score sessions were not statistically significant (0.76 vs. 0.70, N=56, p=0.281). → Po-Chin Chang 2026

Evidence

Po-Chin Chang 2026

Po-Chin Chang, Nicholas Hogan, Aske Plaat, Michiel T. van der Meer. (2026). Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring. https://arxiv.org/abs/2606.20138 doi:10.48550/arxiv.2606.20138

q3 · i? · causal · r2

Within-user analysis from the live A/B deployment comparing same-student sessions where the AI scored above versus below its average. Accuracy of 0.76 vs. 0.70 was "not statistically significant" (p=0.281); equivalence was not tested.

"Furthermore, the subsequent within-user exercise accuracy differences were not statistically significant (0.76 vs.0.70,N= 56,p= 0.281, App. P)."

Discussion

Page metadata
Field Value
type claim
id within-user-accuracy-null-result
title Within-user exercise accuracy does not differ significantly between high- and low-score sessions
description Within-user exercise accuracy does not differ significantly between high- and low-score sessions
status review
evidence_strength weak
newest_evidence 2026
generated by process:wiki-ingest on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 1 — po-chin-chang-2026

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