Personalizing LLM-Based Conversational Programming Assistants

2026-04-14

AI Summary

Current large language model (LLM)-driven programming assistants struggle to accommodate the diverse, ambiguous, and open-ended interaction needs arising from developers’ varying cognitive styles and organizational contexts. This work presents the first framework that systematically integrates developer cognitive traits and organizational context into the personalized design of LLM-based programming assistants. By leveraging user modeling, dialogue systems, and human-computer interaction analysis, the authors develop a prototype conversational programming assistant capable of adaptive personalization. The resulting system demonstrates markedly enhanced inclusivity and practical utility, offering a novel paradigm for intelligent programming tools tailored to heterogeneous developer populations.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)

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
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) have shown much promise in powering a variety of software engineering (SE) tools. Offering natural language as an intuitive interaction mechanism, LLMs have recently been employed as conversational ``programming assistants'' capable of supporting several SE activities simultaneously. As with any SE tool, it is crucial that these assistants effectively meet developers' needs. Recent studies have shown addressing this challenge is complicated by the variety in developers' needs, and the ambiguous and unbounded nature of conversational interaction. This paper discusses our current and future work towards characterizing how diversity in cognition and organizational context impacts developers' needs, and exploring personalization as a means of improving the inclusivity of LLM-based conversational programming assistants.

Problem

• personalization
• conversational programming assistants
• developer needs
• cognitive diversity
• organizational context

Innovation

• personalization
• conversational programming assistants
• large language models
• developer diversity
• software engineering tools