
Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work
The article discusses a new educational approach to teaching AI through benchmark construction, specifically using a tool called QuestBench. This method allows students to create expert-level questions and evaluate AI systems, fostering a deeper understanding of AI's role in knowledge work. The findings indicate that many AI systems struggle with accuracy, highlighting the importance of critical evaluation in AI education.
- ▪QuestBench consists of 256 questions across 14 humanities and social-science domains.
- ▪Evaluation of QuestBench revealed that the mean question-level pass rate for thirteen AI systems was only 16.85%.
- ▪The best-performing system, GPT-5.5, achieved a pass rate of 57.58%, indicating significant room for improvement.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.21413 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | NexYkZIxr--G |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.21413 (cs) [Submitted on 20 May 2026 (v1), last revised 21 May 2026 (this version, v2)] Title:Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work Authors:Haiyang Shen, Jiuzheng Wang, Taian Guo, Mugeng Liu, Wenchun Jing, Chongyang Pan, Siqi Zhong, Zhiyang Chen, Weichen Bi, Yudong Han, Xiaoying Bai, Yun Ma View a PDF of the paper titled Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work, by Haiyang Shen and 11 other authors View PDF HTML (experimental) Abstract:As AI becomes part of everyday learning, many courses teach students to use it mainly as a productivity tool: how to prompt, search, summarize, write, code,…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.