This research explores how LLMs can be adapted to match diverse problem-solving styles among software engineers, potentially creating more inclusive AI coding assistants.

Key Findings:

• LLMs can be prompted to adapt explanations to different problem-solving styles
• Matching explanations to engineers' styles may improve perception and effectiveness
• Adaptation based on the Gender Inclusiveness Magnifier (GenderMag) framework shows promise for inclusive design
• Engineers respond differently to explanations that match vs. mismatch their problem-solving approach

For engineering teams, this research provides a pathway to optimize LLM coding assistants for diverse teams, potentially improving productivity and engineer satisfaction through personalized AI interaction.

Original Paper: An LLM's Attempts to Adapt to Diverse Software Engineers' Problem-Solving Styles: More Inclusive & Equitable?