Providing the correct solution in the prompt improves LLM alignment with human-authored distractors, confirming correct-solution recovery as a primary bottleneck¶
Claim · All claims
Evidence · 1 study · causalr2·q2
Currency · newest study 2026 · not yet checked against its sources · 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? Providing the correct answer in the prompt improves proportional match on Eedi (0.52±0.03→0.56±0.03) and SciQ (0.14±0.02→0.18±0.03), relative improvements of 6.4% and 30.7%. → Yanick Zengaffinen 2026
Evidence¶
Yanick Zengaffinen 2026¶
Yanick Zengaffinen, Andreas Opedal, Donya Rooein, Kv Aditya Srivatsa, Shashank Sonkar, Mrinmaya Sachan. (2026). Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation for Multiple-Choice Questions. https://arxiv.org/abs/2603.15547 doi:10.48550/arxiv.2603.15547
q2 · i? · causal · r2
Prompting experiment in the failure-mode analysis section: DEEPSEEK-V3.2 and GLM-4.7 run with and without the correct answer supplied. Both improvements are significant under a paired t-test, p<0.01, per the article's footnote; no effect size is printed.
"Providing the correct answer to the MCQ in the prompt improves proportional match on both Eedi (0.52±0.03→0.56±0.03 ) and SciQ (0.14±0.02→0.18±0.03"
Discussion¶
Related Claims¶
- AI-generated assessment items frequently need faculty correction, supporting AI as a draft generator within quality assurance — the general claim this finding bears on, with the studies behind it
- Candidate selection is a second bottleneck: an oracle ranker over proposed candidates substantially raises proportional match, and plausibility checks reject some valid distractors — related
- Chain-of-thought and reasoning prompting significantly improve LLM proportional match with human-authored distractors over direct prompting on both math and science MCQs — related
- On the misconception-annotated math dataset, LLMs predominantly follow a solve-first, error-injection process aligned with misconception-based distractor design — related
- Teacher-provided correct answers and pre-prompting reduce erroneous LLM output and hallucinations compared with open chatbot use — related
- Enforcing an explicit multi-step reasoning structure yields no significant improvement for the two studied models, which already self-organize an effective reasoning structure — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
correct-solution-in-prompt-bottleneck |
title |
Providing the correct solution in the prompt improves LLM alignment with human-authored distractors, confirming correct-solution recovery as a primary bottleneck |
description |
Providing the correct solution in the prompt improves LLM alignment with human-authored distractors, confirming correct-solution recovery as a primary bottleneck |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — yanick-zengaffinen-2026 |
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