MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences.

Mental health concerns are prevalent among college students, highlighting the need for effective interventions that promote self-awareness and holistic well-being. MindScape explores a novel approach to AI-powered journaling by integrating passively collected behavioral patterns such as conversational engagement, sleep, and location with Large Language Models (LLMs). This integration creates a highly personalized and context-aware journaling experience, enhancing self-awareness and well-being by embedding behavioral intelligence into AI.

Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination.

Traditional interventions for academic procrastination often fail to capture the nuanced, individual-specific factors that underlie them. Large language models (LLMs) hold immense potential for addressing this gap by permitting open-ended inputs, including the ability to customize interventions to individuals' unique needs. However, user expectations and potential limitations of LLMs in this context remain underexplored.

Large displays enhance optical flow cues and narrow the gender gap in 3-D virtual navigation.

Objective: Existing reports suggest that males significantly outperform females in navigating 3-D virtual environments. Although researchers have recognized that this may be attributable to males and females possessing different spatial abilities, most work has attempted to reduce the gender gap by providing more training for females. In this paper, we explore using large displays to narrow the gender gap within these tasks.