Human-Centered AI, Metacognition, and Learning Design
My research explores how artificial intelligence can be integrated into education in ways that preserve human agency, support metacognitive development, and strengthen ethical decision-making. I am especially interested in how students experience AI-supported learning, how instructors design for reflection and transparency, and how educational environments can remain human-centered as AI becomes more embedded in academic work.
HAIML is a framework that guides students to learn with AI through active engagement, reflection on how AI shapes thinking, and ethical evaluation of AI use. This model connects experiential learning, metacognitive awareness, and human-centered decision-making.
A key area of my work examines AI-supported grading and feedback systems with instructor review and oversight. This research explores how students perceive feedback quality, clarity, timeliness, and trust when AI is used within a human-in-the-loop model.
My work also investigates how students and educators engage with ethical questions surrounding AI in psychology, education, and the social and behavioral sciences. This includes issues of bias, digital identity, transparency, authorship, accountability, and the psychological impacts of AI systems.
My earlier and ongoing research examines instructor social presence in online learning, with a focus on how empathy, visibility, and communication influence student engagement and persistence. This work continues to inform my approach to AI integration by keeping human connection central to educational design.