Grounded in the learning sciences, LRDC researchers develop and investigate emerging learning technologies that draw on advances in artificial intelligence, natural language processing, machine learning, and human-computer interaction. Our work spans intelligent tutoring systems, collaborative learning environments, automated writing assessment and feedback, educational robotics, and applications of generative AI. Across these areas, theories and principles of learning guide both the design of new technologies and investigations of their effectiveness. Researchers examine how digital tools can support personalized learning, meaningful engagement, collaboration, and the development of complex reasoning and problem-solving skills. Educational data and learning analytics provide additional insights into how students learn and how technology can support more effective teaching. Together, this work advances our understanding of when, how, and for whom emerging technologies improve learning, while addressing questions of accessibility, equity, and responsible use.
Training the next generation of scholars is integral to this work, with opportunities for graduate and undergraduate students to participate in cutting-edge research. Below, we highlight key research themes and the laboratories advancing work in these areas:
Large Language Models (LLM) and their ability to improve the teaching of collaborative argumentation, and summaries of legal cases for lay users
- Center for Text Analytic Methods in Legal Studies, Kevin Ashley (PI)
Learning sciences approaches to Quantum Information Science (QIS) and technology in k-12
- Quantum Information Science (Q-Jam), Raquel Coehlo (PI)
Co-design and development, testing, and scaling of learning-sciences-infused uses of emergent technologies in teaching and scholarship
- Learning Sciences and Emergent Technologies Hub (LSET), Cross University Collaboration
Natural Language Processing (NLP) research applied to educational applications such as writing evaluation systems, dialogue tutors, and teacher dashboards.
- The Pittsburgh EducaTional And Language Technologies Lab, Diane Litman (PI)
AI use skills for faculty, students and staff that provide a foundation for effective AI education in courses and special training programs
- AI Power Up: Accelerate Your Teaching with AI, Walter Schneider (PI)
Web-based writing environments that use computational linguistics to score essays and give formative feedback to learners and from learners for successful learning from peers
- Learning from Peer Assessment, Christian Schunn (PI)
Educational technologies that facilitate social learning, customizable to the contexts in which they are used
- Future Adaptive Collaborative Educational Technologies (FACET) Lab, Erin Walker (PI)
Researchers Associated with this Area
Kevin Ashley
Raquel Coelho
Richard Correnti
Alan Lesgold
Diane Litman
Lindsay Clare Matsumura
Walter Schneider
Christian Schunn
Angela Stewart
Erin Walker