Task Engagement

Task engagement research investigates how individuals interact with and perform tasks, particularly within human-computer interaction and artificial intelligence contexts. Current research focuses on understanding factors influencing engagement, such as trust in autonomous systems, the impact of secondary tasks, and the effectiveness of AI-driven interventions to improve focus and productivity. This work utilizes various models, including linear dynamical systems, reinforcement learning algorithms (like those employing "reset and distill" strategies), and large language models to enhance task completion and user experience. Ultimately, these studies aim to optimize human-machine collaboration and improve the design of interfaces and AI assistants for increased efficiency and user well-being.