Vincent Aleven

Vincent Aleven is a Professor in the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, where he leads the CATS Lab focused on developing 'smart classrooms' through human-AI collaboration in educational settings. His work bridges learning sciences, human-computer interaction, artificial intelligence, and education to create dynamic learning environments that leverage complementary strengths of learners, educators, and AI systems.

His research centers on intelligent educational systems with core interests in cognitive modeling, metacognitive skill development, and case-based reasoning. Key initiatives include AI-based tutoring software for individual/collaborative learning (e.g., middle-school math equation solving), tools supporting self-regulated learning (help-seeking, self-assessment), teacher orchestration dashboards, and browser-based authoring tools like CTAT/Tutorshop that lower development barriers for intelligent tutoring systems. Current projects investigate smartphone-based homework solutions, gamified tutoring interfaces, and comparative effectiveness against platforms like Khan Academy.

Scientific Awards: No specific awards mentioned in source materials.

Professor Aleven advises graduate students including Kexin Bella Yang and Conrad Borchers. His research is supported by multiple grants, notably a $2M award for testing AI-based mobile tutoring software in underserved middle-school contexts. Major projects encompass:

• MEME Project: Boosting math outcomes through motivation and self-regulation
• Metacognition and Motivation: Pittsburgh Science of Learning Center initiative
• Rapid Tutor Development: Streamlining cognitive model creation
• Learning-Oriented Dialogue: Enhancing engagement via collaborative problem-solving

The CATS Lab operates through deep collaboration with teachers and students in real classrooms, currently exploring how smartphone accessibility, targeted tutor redesign, and parental involvement optimize homework effectiveness. Future directions address AI support for tutors/parents, balancing self-regulatory development with domain learning, and democratizing tutor authoring for educators.