From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model

2026-01-27

AI Summary

This work addresses the limitation of current large language models (LLMs) in design feedback, which often provide generic, one-off suggestions that fail to foster user reflection and design reasoning. To overcome this, the paper introduces the first systematic integration of cognitive apprenticeship into LLM prompting, structuring prompts to guide the model in delivering design coaching through six core strategies: modeling, coaching, scaffolding, articulation, reflection, and exploration. This approach reframes the AI’s role from a mere answer provider to a reflective design mentor. User studies demonstrate that the proposed method significantly enhances the depth of design reasoning and the reflectiveness of feedback interactions, offering critical mechanisms and design insights for future AI-augmented design feedback systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Large Language Models
• design feedback
• reflective engagement
• design reasoning
• Cognitive Apprenticeship

Innovation

• Cognitive Apprenticeship
• Large Language Models
• Design Mentorship
• Reflective Feedback
• Structured Prompting