HAIDL 提出：把人与 AI 的交互记录变成可查询的结构化数据
Human--AI Interactions as First-Class Data: A Formal Interaction Model and Runtime for Explainable AI Collaboration
这篇论文把 Jupyter 里你和 AI 的每次对话都存成可回放、可查询的结构化数据，还能算出求助和学习行为指标，做 AI 协作分析的人可以看看。
arXiv 论文提出 HAIDL（Human-AI Interaction Description Language），用类 BNF 形式语法把每次人机交互编码为包含计算上下文、请求、提示、AI 回复、反馈和来源信息的结构化对象。配套的 HAIDL-X 是一个 JupyterLab 中间件，负责捕获用户与 AI 状态、构建上下文提示、对接 AI 服务并把交互存入仓库。作者在其上叠加学习层，将交互对象与 notebook 执行轨迹映射为参与度、求助行为、错误导向使用和学习画像等可复现指标。
Human--AI Interactions as First-Class Data: A Formal Interaction Model and Runtime for Explainable AI Collaboration
AI assistants are increasingly embedded in computational notebooks, scientific workflows, software engineering tools, and learning systems, yet human-AI interactions are often treated as transient prompt-response exchanges rather than durable data products, hindering reproducibility, explainability, replay, behavioral analysis, and adaptive AI support. We argue that these interactions form a new data class requiring formal representation and introduce the Human-AI Interaction Description Language (HAIDL), a formal interaction model with a Backus-Naur Form-inspired concrete syntax. HAIDL encodes each interaction as a structured object capturing computational context, user request, prompt payload, AI response, feedback, problem context, provenance, and model metadata. We integrate this vision into HAIDL-X, a middleware for JupyterLab that instantiates HAIDL by capturing user-bot state, building contextualized prompts, interfacing with AI providers, collecting feedback, and storing interactions in a repository. We add a learning layer over HAIDL that maps interaction objects and notebook execution traces into reproducible measures of user behavior, including engagement, performance, help-seeking, error-oriented use, and learning personas. HAIDL-X provides a foundation for treating human-AI collaboration as structured, queryable, replayable, analyzable data.