Celebrating Seiyon Lee's Ph.D. Candidacy
Seiyon M. Lee successfully passed her Ph.D. qualifying examination on March 13, 2026, advancing to Ph.D. candidacy in Educational Technology at the University of Florida.
We are delighted to celebrate an important milestone for Seiyon M. Lee, who successfully passed her Ph.D. qualifying examination on March 13, 2026, and advanced to Ph.D. candidacy in Educational Technology at the University of Florida.
Centering Human Judgment in AI-Supported Learning
Since joining the VIABLE Lab in Fall 2023, Seiyon has developed a clear and compelling research identity around collaborative learning, visual analytics, explainable AI, and learner agency. Her work is especially attentive to how people make sense of complex learning situations and how AI can be designed to support, rather than overtake, that sense-making process, and it has already surpassed 60 citations on Google Scholar.
Research Storyline
Seiyon's scholarship centers on decision-making in computer-supported collaborative learning environments. Drawing from educational data mining, learning analytics, and human-centered design, she studies how teachers and learners interpret collaboration data, how support tools shape instructional action, and how AI can help people notice meaningful patterns without stripping away their agency.
That storyline gives her work a distinctive coherence. Rather than treating AI as a source of automatic answers, Seiyon's research asks how AI might become a thoughtful partner in interpretation, sense-making, and instructional decision-making, especially in collaborative problem-solving contexts where learning processes are rich, dynamic, and difficult to summarize well.
This is why her recent work has centered so strongly on AI-augmented sense-making in collaborative mathematics learning. She has been developing ways for teachers to interpret complex collaboration processes more meaningfully, while also contributing to related work on feedback and learner support. Taken together, these studies show a clear pattern: Seiyon is interested in helping people make better educational decisions with AI, not in handing those decisions over to AI.
Selected Venues and Research Areas
- AIED - AI-augmented sense-making for collaborative problem solving, teacher interpretation of learning processes, and dashboard-based instructional support
- AAAI - Human-centered and collaborative AI-assisted instructional design
- JEDM - Interpretable feedback frameworks for mathematics learning contexts
- Information and Learning Sciences - Learning experience network analysis and design-based research on learner experience
- EDM - Real-time collaborative learning analytics in mathematics, critical examination of AI-assisted academic writing, and early doctoral work on challenge, support, and learner experience over time
Why This Milestone Matters
Qualifying exams mark the transition from doctoral coursework and collaborative research into sustained independent inquiry. For Seiyon, candidacy comes with an especially strong foundation already in place: a clearly defined problem space, a coherent methodological orientation, and a set of studies that already point toward an important dissertation contribution.
Her work has helped sharpen the lab's thinking about interpretability, agency, and the design of AI systems that support reflection rather than shortcut it. That contribution is particularly important at a moment when collaborative learning environments are generating more data than educators can easily interpret on their own, yet still require deeply human judgment.
Mentorship and Dissertation Committee
Seiyon is chaired by Dr. Anthony Botelho. Her dissertation committee includes Dr. Avery Closser, Dr. Pavlo "Pasha" Antonenko, and Dr. Eric Ragan from the Department of Computer & Information Science & Engineering (CISE).
Dissertation Direction
For her dissertation, Seiyon is planning to extend the ideas developed in her AIED 2025 paper by exploring how AI capabilities can be better integrated to support human-AI collaborative decision-making in computer-supported collaborative learning (CSCL) and collaborative problem-solving (CPS) instructional contexts.
This direction is especially well matched to her committee's interdisciplinary strengths. Together, they bring perspectives relevant to K-12 mathematics learning, learner modeling through educational data mining and learning analytics methods, human-computer interaction, and data visualization. That combination gives Seiyon an excellent foundation for dissertation work that is analytically rigorous, design-sensitive, and directly relevant to real instructional settings.
Looking Ahead
As Seiyon enters the dissertation phase of her Ph.D. journey, we are excited to see how she continues developing research that bridges AI, analytics, and human judgment in collaborative learning environments. Her work points toward a future in which AI is not treated as a replacement for teachers or learners, but as a thoughtfully designed partner in making sense of complex learning processes.
We are proud to celebrate this achievement with Seiyon and look forward to the work ahead. Congratulations, Seiyon!
To learn more about Seiyon's research and publications, visit her team profile page.