Claims

Human-AI tutoring is associated with a 36% rise in IXL skill proficiency, but the gap widened in favor of reactively tutored students (difference-in-differences preprint)

Claim · All claims
Evidence · 1 study (2 entries) · causal r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · 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.

q2 i? Human-AI tutoring significantly improved student skill proficiency in IXL (36% in the IK bandwidth; +6.10 skills, p<.001). → Ashish Gurung 2026 q2 i? Despite improvements for both groups, the skill proficiency gap widened by 3.69 skills (14.79%), with the reactive group demonstrating greater proficiency than the proactive group. → Ashish Gurung 2026 (2)

Evidence

Ashish Gurung 2026

Ashish Gurung, Ge Gao, Jordan Gutterman, Danielle R. Thomas, Shivang Gupta, Lee Branstetter, Emma Brunskill, Vincent Aleven, Kenneth R. Koedinger. (2026). Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs. arXiv preprint. https://arxiv.org/abs/2605.11155 doi:10.48550/arxiv.2605.11155

q2 · i? · causal · r2

Within-subject DiD analysis of IXL skill proficiency (SmartScore-based skills mastered) over the 10 weeks before each MAP test. Improvement was "significant improvement in student skill proficiency in both the IK bandwidth subsample (36%; +6.10 skills, p<.001)" and the full sample.

"human-AI tutoring was associated with significant improvement in student skill proficiency in both the IK bandwidth subsample (36%; +6.10 skills, p<.001) and the full sample (42%; +6.75 skills, p <.001)."

Ashish Gurung 2026 (2)

Ashish Gurung, Ge Gao, Jordan Gutterman, Danielle R. Thomas, Shivang Gupta, Lee Branstetter, Emma Brunskill, Vincent Aleven, Kenneth R. Koedinger. (2026). Improving Hybrid Human-AI Tutoring by Differentiating Human Tutor Roles Based on Student Needs. arXiv preprint. https://arxiv.org/abs/2605.11155

q2 · i? · causal · r2

DiD comparison of proactive versus reactive groups on IXL skill proficiency (Table 6). The article reports the "proficiency gap widened by 3.69 skills (14.79%)" favoring the reactive group; the proactive group had lower baseline proficiency (-2.17, p=.067 in the IK bandwidth).

"Following the introduction of human support, despite improvements for both groups, the proficiency gap widened by 3.69 skills (14.79%), with the reactive group demonstrating greater proficiency than their proactive peers."

Discussion

Page metadata
Field Value
type claim
id human-ai-tutoring-skill-proficiency-gap-widens
title Human-AI tutoring is associated with a 36% rise in IXL skill proficiency, but the gap widened in favor of reactively tutored students (difference-in-differences preprint)
description Human-AI tutoring is associated with a 36% rise in IXL skill proficiency, but the gap widened in favor of reactively tutored students (difference-in-differences preprint)
status review
evidence_strength mixed
newest_evidence 2026
source_checked 2026-10-20
generated by process:wiki-ingest on 2026-10-11
trust unverified — no one has confirmed this page's content
sources 2 — ashish-gurung-2026, ashish-gurung-2026-2

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