Boosting Diary Study Outcomes with a Fine-Tuned Large Language Model

Abstract

This study explores fine-tuned Large Language Models (LLMs) integration into diary studies within the Human-Computer Interaction (HCI) field to enhance data collection and analysis. Leveraging a Mistral 7B model fine-tuned with a curated dataset of over 1,000 diary entries, this research addresses challenges such as participant engagement and data richness. The fine-tuned model offers personalized feedback, facilitating deeper reflection and structured recording while reducing the cognitive load on participants. The DiaryQuest educational platform, enhanced with advanced visualization tools and semantic search capabilities, enables educators to efficiently analyze diary data, extract thematic insights, and provide targeted guidance. Results from user evaluations reveal that the optimized platform improves learning outcomes, teaching efficiency, and overall user experience. By bridging traditional diary methodologies with state-of-the-art LLMs, this study advances HCI education and establishes a scalable framework for applying AI in broader educational and research contexts.