Vincent Aleven

Professor of Human-Computer Interaction
Director, Undergraduate Programs in Human-Computer Interaction
Carnegie Mellon University
Google Scholar page

About me

My goal is to advance the science of how people interact and learn with advanced learning technologies. I aim to realize the enormous promise of these technologies to help learners reach their full potential. I am excited to help educate a new generation of scientists and professionals.

For students in the HCII undergraduate programs

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Fall 2020: Students interested in getting involved in research

For a list of available projects for independent studies or research assistantships, please go here.

Overview of research

My group's focus is to develop dynamic learning environments that leverage complementary strengths of humans and AI. The work lies at the intersection of the learning sciences, human-computer interaction, artificial intelligence, and education.

We envision a future in which students, teachers, and AI work together in newly-designed partnerships to co-orchestrate and support personalized learning. Students carry out a dynamic, personalized mix of individual and collaborative learning activities, aimed to help them learn at many levels: They develop skills and knowledge at the domain level, become better at regulating their learning and collaborating with peers, and develop their own personal motivation to learn. They receive guidance not just from the AI and the teacher but also from each other.

As we work towards realizing this vision, we collaborate with teachers, students, and other stakeholders. In many activities, we work to understand their needs, preferences and boundaries. We design, prototype, and pilot-test new technologies with them. We investigate how the new technologies we create affect educational processes and outcomes in real educational settings. Although the work focuses often on middle-school mathematics, the technologies work in other task domains as well. We give examples of technologies we have created to support our vision of the dynamic learning environment of the future.

AI-based tutoring software

We have created AI-based tutoring software for individual and collaborative learning and found them to be effective in classrooms in domains like middle-school mathematics equation solving and elementary school fractions learning. This software is available for free for teachers, students, and parents at the Mathtutor website. We have also created tutoring software that simultaneously supports students' learning at the domain level (e.g., mathematics) and learning to become self-regulated learners (e.g., help seeking, self-assessment, self-directed mastery learning).

Orchestration tools

We have designed, implemented, and evaluated AI-based co-orchestration tools for teachers, including most recently, Lumilo, the dissertation work by Ken Holstein. Lumilo is a real-time, AI-based, mixed-reality teacher awareness tool. By projecting indicators of student progress and struggle in the teacher’s view of the classroom, it guides teachers’ attention, while leaving the teacher in charge of decisions of whom to help and how. With this tool, teachers devote more attention to students who have more to learn, and the class learns better.

Authoring tools

We have over many years created authoring tools that non-programmers can use to create AI-based tutoring software, namely CTAT/Tutorshop. Tutors built with these tools support students in practicing complex problems with many solution steps and strategies. Such problem types are ubiquitous and important, but are challenging for ed tech to support. CTAT-built tutors can easily be embedded in a variety of LMSs and e-learnign platforms. Over the years, we helped many instructors and researchers develop AI-based tutoring software.

Other n-going and future research

We face many open questions. To name just a few: How can individual and collaborative learning best be balanced and supported? How can learning self-regulatory skill be balanced with domain-level learning? How can we best support teachers and students in authoring their own AI-based tutoring software?

We also face new open questions, brought to the foreground by the Covid-19 pandemic: In the new educational scenarios that have arisen, what challenges do K-12 teachers face regarding student self-regulation, motivation and connectedness? How do teachers monitors their students' learning, communicate with students, and give timely feedback? How can co-orchestration tools originally designed for classroom use be adjusted to blended or online scenarios?

We are looking for schools and teachers who might be interested in jointly exploring how to develop new, dynamic learning environments that leverage complementary strenghts of humans and AI. If interested in joining the research or to use our technologies for free, please feel to reach out.