The widespread adoption of smartphones and wearable devices has enabled users to collect extensive data on sleep, mood, diet, and physical activity. However, many health-tracking applications present data in raw or overly complex formats that increase cognitive load, limit interpretability, and contribute to user disengagement. This project investigates whether AI-generated interpretive feedback enhances perceived engagement, usability, and insightfulness compared to traditional visualization-based feedback in a health-tracking application designed for college students. We will develop two interactive UI prototypes grounded in HCI research, personal informatics theory, and mHealth literature: (1) an AI-enhanced version incorporating features such as LLM-based summaries, predictive insights, and conversational feedback, and (2) a non-AI version relying on standard charts and graphical data representations. Using a mixed-methods evaluation approach, 8–10 participants per prototype will complete structured usability tasks including logging health data, interpreting trends and predictions, identifying cross-domain relationships (e.g., sleep and mood), and navigating behavior-change recommendations. Think-aloud protocols, observational notes, and post-task interviews will capture confusion, motivation, skepticism, and perceived clarity. Participants will also complete the System Usability Scale (SUS) and Net Promoter Score (NPS) surveys. By integrating quantitative usability metrics with qualitative insights, this study aims to determine whether AI-generated interpretive feedback meaningfully improves user confidence, understanding, and motivation to consistently track and improve their health behaviors.