Intelligent tutors are computer systems that use technologies such as artificial intelligence to provide learners with personalized instruction and feedback. They have been shown to be highly effective at improving students' learning, largely because of their ability to provide students with individualized, adaptive support as they learn. This project aims to advance the technology for building intelligent tutors. To do so, it will develop a framework for building intelligent tutors that makes it easier to create personalized learning experiences that adapt to individual learners. The resulting intelligent tutoring system will use data to determine what actions the system should take, when the system should act, and explain why these actions should lead to improved learning. This new intelligent tutoring system has the potential to transform STEM learning environments by providing accessible, low-cost, individualized instruction for complex STEM topics.
This project will develop a generalizable data-driven framework to induce a wide range of instructional interventions and robust, yet flexible pedagogical decision-making policies across three STEM fields (logic, probability, and programming) and types of intervention (worked examples, buggy examples, and Parsons problems). The system will be designed to allow use of the intelligent tutor in a wide range of STEM-related domains. The system will use advanced machine learning and data mining techniques to generate instructional interventions, mixed-initiative pedagogical policies, and human-in-the-loop explanations. This research will advance knowledge about data-driven generation of mixed-initiative decision making that balances a student’s sense of agency with their need for effective instructional interventions at critical decision points. The efficacy of the resulting system will be evaluated via a series of empirical studies comparing the new interventions with existing tutoring systems to determine the impact on learning outcomes, agency, personalization, and effective interactions. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources. The IUSE: EHR program supports research and development projects to improve the effectiveness of STEM education for all students. This project is in the Engaged Student Learning track, through which the program supports the creation, exploration, and implementation of promising practices and tools.
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.
