Bridging Instead of Replacing Online Coding Communities with AI through Community-Enriched Chatbot Designs

2026-01-26

AI Summary

This work addresses the limited social learning mechanisms in current large language model–based programming assistants, which may inadvertently reduce user engagement with online programming communities. To bridge this gap, the authors propose a Community-Enriched AI design paradigm that integrates user-generated content and social interaction dynamics from platforms like Kaggle into a retrieval-augmented generation (RAG)–based chatbot. This integration endows the assistant with social context awareness, fostering collaboration between AI and community rather than substitution. Two user studies (N=40) demonstrate that this approach significantly enhances users’ trust in the AI, increases their willingness to participate in the community, and improves task-solving performance.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

LLM-based chatbots like ChatGPT have become popular tools for assisting with coding tasks. However, they often produce isolated responses and lack mechanisms for social learning or contextual grounding. In contrast, online coding communities like Kaggle offer socially mediated learning environments that foster critical thinking, engagement, and a sense of belonging. Yet, growing reliance on LLMs risks diminishing participation in these communities and weakening their collaborative value. To address this, we propose Community-Enriched AI, a design paradigm that embeds social learning dynamics into LLM-based chatbots by surfacing user-generated content and social design feature from online coding communities. Using this paradigm, we implemented a RAG-based AI chatbot leveraging resources from Kaggle to validate our design. Across two empirical studies involving 28 and 12 data science learners, respectively, we found that Community-Enriched AI significantly enhances user trust, encourages engagement with community, and effectively supports learners in solving data science tasks. We conclude by discussing design implications for AI assistance systems that bridge -- rather than replace -- online coding communities.

Problem

• LLM-based chatbots
• online coding communities
• social learning
• community engagement
• AI assistance

Innovation

• Community-Enriched AI
• social learning
• RAG-based chatbot
• online coding communities
• LLM integration