Hint-not-solution is the most challenging rubric dimension, with scores ranging from 6% to 81% across models¶
Claim · All claims
Evidence · 1 study · designr2·q2
Currency · newest study 2026 · checked against its sources 2026-10-17 (automated, 1 of 1 entry) · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
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? Models varied widely in guiding students without giving away answers: gpt-oss:20b and olmo-3:7b scored 6% by consistently providing direct solutions, while gemma-4:31b, gemma3:27b, and nemotron-3-super reached 81%. → H. Chad Lane 2026
Evidence¶
H. Chad Lane 2026¶
H. Chad Lane, Bryson Kageler. (2026). CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming. SLM4ED'26: The 1st Workshop of Small Language Models for Education. https://github.com/InviteInstitute/CSTutorBench doi:10.1145/3770761.3777333
q2 · i? · design · r2
Per-criterion scoring of the 8 debugging questions in Trial 2 (Table 2), judged by Claude Sonnet 4 with hybrid review. The lowest scorers "consistently provide direct solutions"; the discussion attributes gpt-oss:20b's 6% to a helpfulness compulsion that completes next steps for the student. No effect sizes are printed.
"Hint_not_solution, which measures whether a model can guide a student toward a solution without giving it away, is the most challenging dimension. Scores range from 6% (gpt-oss:20b and olmo-3:7b, which consistently provide direct solutions) to 81%"
Discussion¶
Related Claims¶
- Persistence and performance costs concentrate among participants who used the AI to obtain direct solutions, while hint-users resemble controls — the general claim this finding bears on, with the studies behind it
- Unguarded answer-giving AI harmed unaided exam performance while a guarded version of the same model erased the harm (Bastani et al., 2025, as reported) — related
- AI assistance reduces persistence: persistence costs concentrate among learners who used AI for direct solutions, not hints — related
- Model selection experiments show newer models are not strict improvements, with component-specific effects on quality metrics — related
- Most models struggle to engage with a student's prior debugging attempts even when the iteration history is provided — related
- GPT-4 generates targeted corrective feedback for 66% of student errors, but 35% of hints are too general, incorrect, or give away the answer — related
- Preference explanations favoring LearnLM more often cite staying on topic, challenging the learner, and not giving away answers — related
- GPT-generated hints are moderately accessible by automated readability metrics (mean CAREC 0.128, mean CML2RI 3.95) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
hint-not-solution-most-challenging-dimension |
title |
Hint-not-solution is the most challenging rubric dimension, with scores ranging from 6% to 81% across models |
description |
Hint-not-solution is the most challenging rubric dimension, with scores ranging from 6% to 81% across models |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
source_checked |
2026-10-17 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — h-chad-lane-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.