从答案提供者到设计导师：通过认知 apprenticeship 模型引导 LLMs
From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model
AI总结：
本文通过认知 apprenticeship 模型引导 LLMs 作为设计导师，提升设计推理和反思反馈质量。
摘要：
设计反馈有助于从业者改进其成果，同时促进反思和设计推理。大型语言模型（如ChatGPT）可以支持设计工作，但往往提供通用的一次性建议，限制了反思性参与。我们研究如何通过应用认知 apprenticeship 模型来引导 LLMs 作为设计导师，该模型强调通过六种方法展示推理：建模、指导、支架、表达、反思和探索。我们通过结构化提示将这些教学方法具体化，并在数据可视化从业者中进行组内研究进行评估。参与者与基线 LLM 和设计认知 apprenticeship 提示的 LLM 进行互动。通过调查、访谈和对话日志分析比较各条件下的体验。我们的发现表明，认知引导的提示能够引发更深入的设计推理和更反思性的反馈交流，尽管基线模型在某些任务类型或经验水平下有时更受欢迎。我们总结了促进反思实践的 AI 辅助反馈系统的設計考量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
