The Learning Institute's communities of research are working groups made up of LI members engaged in complementary, interdisciplinary scholarship. Each community is led by a faculty facilitator. Explore our active groups below.

AI-Assisted Computing Education

Computing education is undergoing a fundamental transformation as AI reshapes what it means to learn, practice, and teach programming and software development. This community addresses a central question: how can we design AI-assisted computing education from K–12 through higher education that is personalized, project-based, and motivationally effective, while preparing learners for the competencies the industry actually demands? 

This overarching question organizes four interconnected research threads: 

  • Industry-oriented competence framework for computing education in the AI era 
  • AI-assisted personalized learning in project-based computing education 
  • Motivational redesign of programming courses 
  • The role of vibe coding in supporting programming and software development ability 

Facilitator: Dr. Jingwen He, Department of Learning Technologies 

K-12 Education Research Practice Partnerships for North Texas

There is a persistent and well-documented gap between education research produced in university settings and the instructional and organizational practices that actually occur in K–12 classrooms. North Texas school districts face complex, evolving challenges that would benefit enormously from rigorous, co-produced research, including around student achievement, educator retention, curriculum alignment, and equitable resource distribution. Yet most district leaders lack the time, infrastructure, or academic connections to access or apply that research effectively. This community is centered on closing that gap through formalized Research-Practice Partnerships (RPPs) that connect higher education faculty in North Texas with K–12 district partners across North Texas to co-design research questions, share data, and produce findings that are immediately actionable in schools. 

Facilitator: Dr. Darrell Hull, Department of Educational Psychology

AMIS: AI-Driven Metacognitive Support for STEM Problem-Solving

The central theme of this community is to investigate how AI can serve as a metacognitive partner in STEM education. While generative AI is often used for rapid answer generation shortcuts, its potential to facilitate self-regulation and strategic reflection remains under-explored. AMIS focuses on transitioning AI from a crutch to a scaffold, helping students monitor and evaluate their own thinking processes while navigating complex, ill-structured STEM tasks. 

Facilitator: Dr. Ji Hyun Yu, Department of Learning Technologies 

Language and Communication: AI-Mediated Learning, Emotion, and Student Success

How can AI-mediated language and communication environments improve student learning, engagement, equity, and emotional wellbeing across diverse learners, and how can higher education instructors design, assess, and implement these environments responsibly?  

This community and its research problem: 

  • Centers students 
  • Focuses on learning processes, not just tools 
  • Integrates language, communication, cognition, emotion, and AI 
  • Invites experimental, data-driven, and design-based research 
  • Aligns directly with the LI’s mission to inform curricular and pedagogical practice 

Facilitators: Dr. Jiyoung Yoon, Department of World Languages, Literatures, and Cultures  & Dr. Ryan Boettger, Department of Technical Communication