CRII: HCC: AI-Powered Educational Game Platform for Collaborative Decision-Making with Adaptive Feedback Using Cognitive Models and LLMs

About This Grant

Game-based learning has emerged as a promising approach for training decision-making skills across industries, such as business negotiations, law enforcement, and cybersecurity. However, most educational games primarily focus on individual problem-solving rather than teaching collaborative decision-making in dynamic environments. Many real-world decisions require collaboration, where people must work together, consider different perspectives, and balance personal goals and collective success. Yet, existing platforms designed to support teamwork are often too rigid, complex, or time-consuming to customize, limiting broader adoption in education. This project addresses these challenges by developing an AI-powered educational game platform that supports collaborative decision-making through real-time and adaptive feedback. Through gamification, learners engage in dynamic group scenarios where they explore the consequences of their choices, refine strategic thinking, and improve teamwork. By making collaborative decision-making education more interactive, personalized, and accessible through customizable features, this project benefits educators, students, and professionals, expanding opportunities for decision making training across educational and professional settings. To meet these goals, this project has three key objectives. First, the project will develop a cognitive tutor enhanced by Large Language Models (LLMs). This tutor will infer human intentions and predict decision-making strategies, providing personalized feedback while adapting its responses to enhance teamwork and group coordination. Second, the project will leverage gamification in team-based settings to design a customizable platform that integrates strategy simulators with reinforcement learning agents and a real-time learning analytics dashboard. Educators and researchers can adjust game parameters to create a flexible and scalable learning environment. Learners can also explore and refine decision-making strategies using strategy simulators and learning analytics. Finally, the project will evaluate the platform’s effectiveness in improving collaborative decision-making. A combination of qualitative and quantitative methods will assess task performance, collaboration effectiveness, and user trust and engagement. This research will provide an interactive framework for studying human behavior in dynamic group settings. It will also deepen understanding of social decision-making and lay the groundwork. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.