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

TL;DR

This paper explores guiding Large Language Models as design mentors using the Cognitive Apprenticeship Model to enhance reflective feedback and reasoning in design tasks.

Contribution

It introduces a structured prompting approach based on cognitive apprenticeship methods to improve LLMs' effectiveness as design mentors.

Findings

Cognitively informed prompts lead to deeper design reasoning.

Participants provided more reflective feedback with the instructional LLM.

Baseline LLM sometimes preferred depending on task and experience.

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.