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

arXiv: https://arxiv.org/abs/2601.19053 · HTML: https://mlsanity.com/paper/2601.19053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 71 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking](https://mlsanity.com/paper/2410.03703/) (2024)
- [GPTCoach: Towards LLM-Based Physical Activity Coaching](https://mlsanity.com/paper/2405.06061/) (2024)
- [Generating Automatic Feedback on UI Mockups with Large Language Models](https://mlsanity.com/paper/2403.13139/) (2024)
- [Learning Visual Importance for Graphic Designs and Data Visualizations](https://mlsanity.com/paper/1708.02660/) (2017)
- [VizLinter: A Linter and Fixer Framework for Data Visualization](https://mlsanity.com/paper/2108.10299/) (2021)
- [How Good is ChatGPT in Giving Advice on Your Visualization Design?](https://mlsanity.com/paper/2310.09617/) (2023)
- ["If the Machine Is As Good As Me, Then What Use Am I?" -- How the Use of ChatGPT Changes Young Professionals' Perception of Productivity and Accomplishment](https://mlsanity.com/paper/2404.12549/) (2024)
- [Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning](https://mlsanity.com/paper/2410.08922/) (2024)
- [Think Together and Work Better: Combining Humans' and LLMs' Think-Aloud Outcomes for Effective Text Evaluation](https://mlsanity.com/paper/2409.07355/) (2024)
- [GUIComp: A GUI Design Assistant with Real-Time, Multi-Faceted Feedback](https://mlsanity.com/paper/2001.05684/) (2020)
- [Coaching Copilot: Blended Form of an LLM-Powered Chatbot and a Human Coach to Effectively Support Self-Reflection for Leadership Growth](https://mlsanity.com/paper/2405.15250/) (2024)
- [Visualizationary: Automating Design Feedback for Visualization Designers using LLMs](https://mlsanity.com/paper/2409.13109/) (2024)
- [Understanding Why ChatGPT Outperforms Humans in Visualization Design Advice](https://mlsanity.com/paper/2508.01547/) (2025)
- [Perceptual Pat: A Virtual Human System for Iterative Visualization Design](https://mlsanity.com/paper/2303.06537/) (2023)
- [From Idea to Implementation: Evaluating the Influence of Large Language Models in Software Development -- An Opinion Paper](https://mlsanity.com/paper/2503.07450/) (2025)
- [Ask Me or Tell Me? Enhancing the Effectiveness of Crowdsourced Design Feedback](https://mlsanity.com/paper/2101.06143/) (2021)
- [AI That Helps Us Help Each Other: A Proactive System for Scaffolding Mentor-Novice Collaboration in Entrepreneurship Coaching](https://mlsanity.com/paper/2508.11052/) (2025)

## Built on by (most cited first, 1 of 1)

- [CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming](https://mlsanity.com/paper/2607.05571/) (2026)

## Abstract (arXiv)

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.