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ARXIV:2605.21413 · AI EDUCATION & BENCHMARKING · SUBMITTED 21 MAY · 20:28 UTC · FRESHNESS STALE
ARXIV:2605.21413AI EDUCATION & BENCHMARKINGSUBMITTED 21 MAY · 20:28 UTCFRESHNESS STALEHaiyang Shen · Jiuzheng Wang · Taian Guo · Mugeng Liu · Wenchun Jing · Chongyang Pan · +6 at arXiv
QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems.
Opportunity summary
Pain QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems. We argue that AI education also needs a setting in which students learn to…
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, and use tools more efficiently. We argue that…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluation on QuestBench shows that student-designed tasks reveal hidden failures in current deep research systems: across thirteen evaluated systems, the mean question-level pass rate…
AI Education & Benchmarking moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems.
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10.48550/arXiv.2605.21413QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems.
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, and use tools more efficiently. We argue that AI education also needs a setting in which students learn to test AI and understand their own role in judging machine-produced knowledge. To this end, we introduce a course-based practice that teaches AI through benchmark construction, using deep research systems as a concrete example of AI-era knowledge work. Students turn disciplinary knowledge into verifiable expert-level questions, review one another's designs for ambiguity and shortcuts, and evaluate AI systems on the resulting tasks. This activity gives students direct exposure to a powerful tool while asking them to specify what a trustworthy answer would require. The produced benchmark, QuestBench, consists of 256 questions across 14 humanities and social-science domains. Evaluation on QuestBench shows that student-designed tasks reveal hidden failures in current deep research systems: across thirteen evaluated systems, the mean question-level pass rate is only 16.85%, and the best-performing system, GPT-5.5, reaches a 57.58% pass rate. The failures are educationally useful because they show how fluent, source-backed answers can still miss the right query, source, term, or evidence standard. Reflections from five student contributors suggest that benchmark construction can help students see professional knowledge not only as content AI may retrieve, but as the basis for judging AI outputs. We present QuestBench as a benchmark artifact and as a reusable classroom setting for a larger educational question: how students can remain responsible knowledge actors as AI enters learning and professional work. The dataset is available at https://huggingface.co/datasets/PKUAIWeb/QuestBench/tree/main.
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PROBLEM
QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems. We argue that AI education also needs a setting in which students learn to test AI and understand their own role in judging mach...
METHOD
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, and use tools more efficiently. We argue that AI education also needs a setting in which students learn to test AI and und...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluation on QuestBench shows that student-designed tasks reveal hidden failures in current deep research systems: across thirteen evaluated systems, the mean question-level pass rate is only 16.85%, and...
WHY NOW
AI Education & Benchmarking moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems. We argue that AI education also needs a setting in which students learn to test AI and understand their own role in judging machine-produced knowledge.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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, and use tools more efficiently. We argue that AI education also needs a setting in which students learn to test AI and understand their own role in judging machine-produced knowledge.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Evaluation on QuestBench shows that student-designed tasks reveal hidden failures in current deep research systems: across thirteen evaluated systems, the mean question-level pass rate is only 16.85%, and the best-performing system, GPT-5.5, reaches a 57.58% pass rate. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Education & Benchmarking moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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QuestBench is a course-based practice and dataset for teaching AI through benchmark construction, revealing failures in current deep research systems.
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AI Education & Benchmarking
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