Kernekoncepter
Large language models like GPT-4 can automate heuristic evaluation for UI mockups, catching errors and improving design.
Resumé
The content explores the use of large language models, specifically GPT-4, to provide automatic feedback on user interface (UI) mockups. The study focuses on applying these models to automate heuristic evaluation, comparing their performance with human experts. The process involves prototyping UI in Figma, selecting guidelines for evaluation, and receiving constructive feedback. Results show that while GPT-4 is generally accurate and helpful in identifying issues in poor UI designs, its performance decreases after iterations of edits. Participants found the tool useful despite some inaccuracies.
Directory:
- Introduction
- Importance of feedback in UI design.
- Challenges of obtaining human feedback.
- Heuristic Evaluation with LLMs
- Use of large language models for automated feedback.
- Implementation as a Figma plugin.
- System Details
- Design goals for the automatic evaluation tool.
- Implementation details and techniques to improve LLM performance.
- Study Methodology
- Description of three studies conducted: Performance Study, Manual Heuristic Evaluation Study with Human Experts, Iterative Usage Study.
- Results
- Quantitative results from the Performance Study and comparison with human evaluators.
Statistik
We assessed performance on 51 UIs using three sets of guidelines.
GPT-4-based feedback is useful for catching subtle errors and improving text.
Participants spent an average of 6.8 hours evaluating suggestions.
Citater
"Feedback is essential for guiding designers towards improving their UIs."
"LLMs have shown capacity for rule-based reasoning."
"GPT-4 was generally accurate and helpful in identifying issues in poor UI designs."