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Investigating Learner-AI Interactions, Self-Regulated Processes and Learning Experiences of Graduate Students in Education Technology
Research Focus: Self-Regulated Learning (SRL), Learner-AI Interactions, Learning Experience DesignInvestigating Learner-AI Interactions, Self-Regulated Processes and Learning Experiences of Graduate Students in Education Technology
Problem Statement: Higher education is experiencing a rapid shift driven by Generative Artificial Intelligence (GAI), transitioning technology from passive content delivery mechanisms toward active, reciprocal learning partnerships. However, GAI's conversational ease and immediate generation capabilities also introduce the threat of epistemic passivity and false self-efficacy. In AI-augmented environments, self-regulation serves as the active mechanism that protects and asserts human agency, autonomy, and intellectual ownership. Moreover, without contextual understanding of Learner-AI interactions, educational stakeholders face challenges in appropriate adoption of emerging technologies and design of effective learning experiences.
Research Development: Grounded in Self-Regulated Learning (SRL) and other constructivist theories, this multi phased exploratory research employs questionnaire, think-aloud and interview to provide deep, process-oriented evidence to explain how internal self-regulatory processes interact with the external scaffolding of GAI during complex learning tasks.
Implications: The aim is to explore interaction patterns, self-regulatory behaviors, emergent cognitive challenges, or other usability issues. The complex data set will be translated into visual maps, profiles and findings to directly contribute to expand SRL in AI-enhanced learning environments and inform future learning design.
Learner-AI Interaction Patterns in Information Problem-Solving and Writing: Toward Understanding Human–AI Epistemic Engagement
Research Focus: Information Problem-Solving (IPS), Learner–AI Interactions, Emerging technology as Cognitive ToolsLearner-AI Interaction Patterns in Information Problem-Solving and Writing: Toward Understanding Human–AI Epistemic Engagement
Problem Statement: Generative AI (GAI) has opened new possibilities for tutoring systems that supports authentic problem-solving and knowledge construction. Information problem-solving (IPS), in which students seek, evaluate, and synthesize information to complete ill-structured tasks, is central to learning. Unlike rule-based Intelligent Tutor Systems, GAI generates context-sensitive responses through conversation and existing literature has conceptualized GAI in multiple roles, including tutor or cognitive tool without comprehensively mapping processes through which students and GAI interact during IPS. While prior research has examined students’ IPS behaviors when using search engines, whether GAI transforms these behaviors remains insufficiently understood.
Research Development: Grounded in constructivist theory and problem-solving literature, this study examines how students interact with a custom-built GAI tutor during an authentic problem-solving and writing task by analyzing data collected through think-aloud, interaction and prompt logs, final essays, and interviews. Initial case studies identified various Learner-AI interaction patterns including the use of GAI as an information provider or tutor with direct instructions, instead of cognitive tool that supports learners' cognitive and metacognitive processes. The findings complicate assumptions that GAI as tutor role will automatically produce effective tutoring highlighting the system constraints in this context. Ongoing follow-up research with richer and broader dataset, suggests that the role of GAI as a tutor, tool, or cognitive partner emerges through interactions as learner and AI negotiate control and ownership over problem framing, inquiry, and knowledge construction.
Implications: Since GAI’s educational effects vary across disciplines and contexts, detailed evidence of these interaction processes is needed to design scaffolding tools that preserve students’ ownership of inquiry while using GAI to support knowledge construction. This research will also inform the development of an agentic AI system focusing on supporting knowledge construction and providing adaptive scaffolding for learners at different cognitive and epistemological levels.
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