Statement of research interest
Research focus
My research interests lie at the intersection of artificial intelligence (AI), computer science education (CSEd), and collaborative learning. Specifically, I am interested in AI-mediated and adaptive learning, collaborative and inquiry-based learning, students' self-efficacy and motivation, and the use of AI as a tool for educational research.
Generative AI has quickly become part of how students learn to program. It can explain errors, suggest code, and answer questions on demand, which may help some learners persist through early difficulty. However, the same tools can also let students skip the reasoning, discussion, and productive struggle through which deep learning occurs. In collaborative approaches such as Process Oriented Guided Inquiry Learning (POGIL), where students build understanding by explaining ideas to one another, AI may change who does the thinking within a team.
I am equally interested in how AI shapes the way students see themselves as learners. Self-efficacy, a student's belief in their ability to succeed, and motivation are closely linked to persistence in computing, particularly for students from groups historically underrepresented in the field. Whether AI-mediated support builds or erodes these beliefs remains an open question with important implications for equity.
Across these areas, I am drawn to a common question: how can learning experiences be designed so that AI supports, rather than replaces, students' reasoning, confidence, and engagement with one another? I approach this question from two directions. From a design perspective, I ask how instructors and instructional designers can create activities that account for AI. From a methodological perspective, I ask how AI itself might help researchers study collaborative learning more efficiently and at greater scale.
Background and motivation
My interests grow out of experience in industry, teaching, and faculty development. After six years as a full-stack web developer and business analyst, I taught more than 14 computer science and information systems courses at Florida A&M University (FAMU) and served as a Co-PI on an NSF-funded project on active learning in online synchronous environments. As an instructor, I watched generative AI begin to bypass the productive struggle that inquiry learning depends on. That experience, along with my work in the POGIL Project and as an instructional designer at Florida State University, shapes the questions I pursue.
Research interests
My interests form four interconnected strands, each anchored by a guiding question.
1. AI-mediated and adaptive learning in CS education
I want to understand how novice programmers actually use generative AI while learning, and how AI can adapt its support to the learner rather than supply finished answers. I focus on introductory programming (CS1), where students first form mental models of computation and where unproductive AI use may carry the greatest long-term cost.Guiding question: Under what conditions does AI scaffold, rather than substitute for, novice programmers' reasoning?
2. AI within collaborative and inquiry-based learning
Collaborative learning depends on students explaining, questioning, and building on one another's ideas. When an AI tool joins a POGIL team, it may act as a resource, a shortcut, or something like an additional team member. I want to understand how AI reshapes team roles, discourse, and the inquiry cycle of exploration, concept invention, and application, and how instructors can redesign activities in response.Guiding question: How does AI use change the distribution of thinking within student teams, and which activity designs preserve meaningful collaboration?
3. Self-efficacy and motivation in AI-mediated learning
Students' beliefs about their ability to succeed in computing, or self-efficacy, shape whether they persist, especially in introductory courses and among students from groups underrepresented in the field. AI tools may help students push past early frustration, but they may also weaken confidence if students come to believe they cannot program without assistance. I want to understand how AI mediation influences novice programmers' self-efficacy, motivation, and sense of ownership over their learning.Guiding question: How does AI-mediated support shape CS students' self-efficacy and motivation, and for whom?
4. AI as a tool for analyzing collaborative learning
Research on collaborative learning often relies on qualitative coding of student discourse, which is slow and labor-intensive. Large language models (LLMs) promise to accelerate this work, but their reliability likely varies across constructs. I am interested in where AI-assisted coding agrees with trained human coders, where it diverges, and what those divergences reveal about the constructs themselves.Guiding question: How reliably can AI code theoretically grounded constructs of online collaborative learning?
Across all four strands, I attend to equity: who benefits from AI-augmented learning, and whether these tools widen or narrow existing gaps in computing participation.
Short-term plan (next 1–2 years)
Over the next two years, I will advance these interests through four connected activities that build toward my dissertation.
Present a workshop at the UF AI Summit 2027. I will present the workshop AI & Inquiry Learning: Cultivating Critical Thinkers in an AI-Augmented Classroom, which introduces POGIL-like activities that build AI tasks directly into the inquiry process. The workshop offers faculty and instructional designers practical principles for designing such activities. Sharing work with practitioners keeps my research connected to the instructors who will ultimately use it.
Expand the workshop into a research paper (2026–2027). Building on the workshop, I am co-authoring a paper with FAMU computer science faculty that studies these activities in the classroom. Rather than treating AI as an outside shortcut, the activities ask students to use AI at designated points as part of their team's inquiry. The paper examines how students engage with these built-in AI tasks and how the tasks shape students' reasoning and collaboration. We plan to submit this work to the ACM Conference on International Computing Education Research (ICER) and the American Educational Research Association (AERA) Annual Meeting. Together, the workshop and paper provide an exploratory foundation for strands 1 and 2.
Conduct an AI-assisted Community of Inquiry coding study (2027). The Community of Inquiry (CoI) framework (Garrison, Anderson, & Archer, 2000) describes meaningful online learning through three interdependent elements: social, cognitive, and teaching presence. Using a human-coded discussion dataset, I will prompt an LLM to code the same data and compare AI and human codes separately for each presence, using inter-rater agreement statistics such as Cohen's kappa. I expect agreement to vary by presence; for example, explicit facilitation moves (teaching presence) may be easier for AI to recognize than the affective cues of social presence or the developmental phases of cognitive presence. The results will offer methodological guidance for researchers and shape my own analytic approach.
Develop my dissertation focus (2026–2028). I am currently reading the literature on self-efficacy, motivation, and AI mediation in learning, which I am considering as my dissertation topic. Over the coming year, I will synthesize this reading into a focused literature review, refine my research questions, and identify the theoretical frameworks and measures best suited to studying how AI-mediated support shapes CS students' self-efficacy and motivation. The projects above will let me test these ideas in real classrooms as I move toward a dissertation proposal.
Long-term goals
In the long term, I aim to build a research program that produces evidence-based design principles for AI-augmented collaborative learning in computing. These principles should help instructors cultivate confident, motivated, and critical thinkers rather than passive consumers of AI output. I hope to contribute to both CS education research and the instructional design community, with a sustained commitment to ensuring that the benefits of AI in education reach students who have historically been underrepresented in computing.
Reference
Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105.
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