让大模型从给答案变身为设计导师，引导深度思考。
From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model
- 用认知师徒制六法设计提示，引导模型展示推理过程。
- 实验证明新方法使反馈更深入，用户反思更充分。
- 适合希望提升设计思维的从业者和教育者参考。
设计反馈能帮助实践者改进作品并促进反思与设计推理。大型语言模型（如ChatGPT）可辅助设计工作，但常提供泛化、一次性建议，限制反思性互动。本文探索如何通过认知师徒制模型引导LLM成为设计导师，该模型强调通过六种方法展现推理：示范、指导、支架、表述、反思与探索。我们通过结构化提示实现这些教学策略，并在数据可视化从业者中开展自身对照实验。参与者分别与基线LLM及基于认知师徒制提示设计的LLM交互。通过问卷、访谈与对话日志分析对比不同条件下的体验。结果表明，认知导向提示能激发更深层的设计推理和更多反思性反馈交流，但在特定任务类型或经验水平下，基线模型仍可能更受青睐。研究提炼出支持反思性实践的AI辅助反馈系统设计原则。
原文摘要 · Abstract (English)
Design feedback helps practitioners improve their artifacts while also fostering reflection and design reasoning. Large Language Models (LLMs) such as ChatGPT can support design work, but often provide generic, one-off suggestions that limit reflective engagement. We investigate how to guide LLMs to act as design mentors by applying the Cognitive Apprenticeship Model, which emphasizes demonstrating reasoning through six methods: modeling, coaching, scaffolding, articulation, reflection, and exploration. We operationalize these instructional methods through structured prompting and evaluate them in a within-subjects study with data visualization practitioners. Participants interacted with both a baseline LLM and an instructional LLM designed with cognitive apprenticeship prompts. Surveys, interviews, and conversational log analyses compared experiences across conditions. Our findings show that cognitively informed prompts elicit deeper design reasoning and more reflective feedback exchanges, though the baseline is sometimes preferred depending on task types or experience levels. We distill design considerations for AI-assisted feedback systems that foster reflective practice.
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