想让学生真正理解AI局限性的教育者，可以用QuestBench的方法把课堂变成AI测试场——学生自己设计问题来考AI，比单纯教提示词更有深度。
北京大学团队提出一种新的AI教育方法：让学生通过构建基准测试来学习AI，而非仅将其作为效率工具。学生将学科知识转化为可验证的专家级问题，互相审查设计中的歧义和捷径，并评估AI系统。由此产生的QuestBench包含256个问题，覆盖14个人文社科领域。评估显示，13个AI系统的平均通过率仅16.85%，最佳系统GPT-5.5也仅达57.58%，暴露了当前深度研究系统的隐藏失败。学生反馈表明，这种实践帮助他们将专业知识视为判断AI输出的基础，而非AI可检索的内容。
Teaching AI Through Benchmark Construction: QuestBench as a Course-Based Practice for Accountable Knowledge Work
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.