Automated real-time feedback in an adaptive diagnostic ITS produced greater progression on a SOLO-based mathematics assessment than the same system without real-time feedback¶
Claim · All claims
Evidence · 1 study · reviewr2·q2·i2medium
Currency · newest study 2026 · checked against its sources 2026-10-18 (automated, 1 of 1 entry) · 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 i2 Learners using the adaptive diagnostic ITS with automated real-time feedback showed greater SOLO-based progression than learners using the same system without it, with a significant time-by-group interaction and a reported Cohen's d of 0.73. → Ogunsakin IB 2026
Evidence¶
Ogunsakin IB 2026¶
Ogunsakin IB, Ayanwale MA, Falebita OS and Felemu OJ (2026) Reimagining mathematics education through intelligent tutoring: evidence, opportunities, and implementation challenges. Front. Educ. 11:1943906. https://doi.org/10.3389/feduc.2026.1943906
q2 · i2 · review · r2
The review reports Marwiang et al. (2025)'s comparison of learners using the same adaptive diagnostic ITS with and without automated real-time feedback. The feedback group's means rose from 1.90 (SD = 1.43) to 3.54 (SD = 0.85) versus 1.90 (SD = 1.31) to 2.85 (SD = 1.27) on the four-point SOLO scale, a "meaningful advantage associated with real-time feedback".
"The significant time-by- group interaction, F (1, 76) = 9.24, p = .003, partial η2 = .108, together with the reported Cohen’s d of 0.73, indicates a meaningful advantage associated with real-time feedback."
Discussion¶
Related Claims¶
- Intelligent tutoring systems improve achievement over non-individualised instruction — the general claim this finding bears on, with the studies behind it
- Reviewed literature identifies personalisation, adaptive support, rapid feedback, and teacher-facing automation as recurring pedagogical opportunities of AI in mathematics education — a broader claim this one bears on
- AI-driven PBL environments enhance educator and student collaboration via real-time feedback, intelligent scaffolding, and data-informed instructional decisions — related
- AI presents four solution types for overcoming situated learning barriers: adaptive systems, intelligent tutoring in authentic scenarios, administrative automation, and data-driven teacher support — related
- Learning paths used for SRL improved accuracy and performance without increasing effort, and tutoring-system benefits required sustained practice — related
- In a controlled evaluation with 268 ninth-grade students, embedding elaborated feedback in the ACED system did not impair assessment quality and produced significantly greater learning than a control group — related
- LLMs can deliver scalable, personalized, interactive feedback almost instantly, reducing dependency on teacher availability and supporting self-regulated learning in large courses — a broader claim this one bears on
- Implementation intention formation enhances accessibility of specified opportunities and automates goal-directed responses — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
real-time-feedback-its-solo-progression-advantage |
title |
Automated real-time feedback in an adaptive diagnostic ITS produced greater progression on a SOLO-based mathematics assessment than the same system without real-time feedback |
description |
Automated real-time feedback in an adaptive diagnostic ITS produced greater progression on a SOLO-based mathematics assessment than the same system without real-time feedback |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
source_checked |
2026-10-18 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — ogunsakin-ib-2026 |
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