I am a Ph.D. candidate in Curriculum and Instruction, specializing in Educational Technology, at the University of Florida, with a minor in Research Evaluation and Methodology. I expect to graduate in Spring 2027. My research lies at the intersection of artificial intelligence in education, multimodal learning analytics, educational data mining, and the learning sciences. I focus on designing responsible, human-centered AI applications for K-12 learners and educators and modeling learning, AI literacy, collaboration, and persistence in STEM+C and AI education using multimodal data.

At the University of Florida, I conduct research with the NSF-funded AI Institute for Innovative Intelligent Technologies for Education (INVITE), the CS Everyone Center, and the VIABLE Lab. I have also completed two research internships at Educational Testing Service, where I developed and evaluated AI-supported approaches to educational assessment and teacher-facing score reporting systems. I earned my M.Ed. in Human Development and Psychology from Harvard University and my B.S. in Education from the University of Oregon.

Recent Updates

I plan to graduate in Spring 2027 and am currently on the job market.

What I Do

My research examines how AI can be integrated into education in ways that are effective, responsible, and grounded in how students learn. My overarching goal is to create AI-enhanced learning and assessment experiences that help diverse learners develop knowledge, AI literacy, persistence, collaboration skills, and the ability to engage critically with AI.

I pursue this goal through three interconnected areas. First, I investigate how to assess and foster learners’ AI literacy competencies by using multimodal data to understand how they interpret, evaluate, and interact with AI systems and by designing learning environments that support the development of these competencies.

Second, I develop and apply advanced analytical methods, including causal inference with propensity score weighting, sequential and network analyses, and multimodal learner models that integrate dialogue, learning logs, eye-tracking, and facial-expression data using machine learning and large language models. These approaches allow me to evaluate the effectiveness of educational interventions and examine learners’ critical thinking, persistence, help-seeking, and problem-solving processes during interactions with AI.

Third, I study collaborative learning, examining how students communicate, coordinate, and learn together, and how AI-supported tools can provide educators with timely, interpretable, and actionable insights.