Student-AI Interaction

Student-AI interaction — the patterns, processes, and cognitive work in how learners engage with generative AI systems during learning and problem solving. Research here characterizes what students ask of AI, how prompts and dialogues evolve, and how interaction quality relates to learning outcomes, Cognitive Offloading, and Agency.

Student-AI interaction is the observable surface of learners' engagement with generative AI — the questions they pose, the prompts they write, the way they negotiate and verify AI output, and how those patterns shift across task stages and over time. It sits at the intersection of Student Experience, Prompt Engineering, and Learning Analytics, and is central to debates about whether AI use in education represents genuine learning or over-reliance. Where Human AI Collaboration frames the high-level division of cognitive labor between people and models, student-AI interaction is the concrete, measurable enactment of that relationship — the specific inquiries, prompts, and negotiation moves learners make moment to moment.

What students ask AI

A core strand of research measures the types and quality of student inquiries. Studies apply taxonomies of question types — for example the Graesser et al. 18-type taxonomy — to classify student-AI interactions, often using few-shot classifiers to scale the analysis across hundreds or thousands of interactions. Findings indicate that a small subset of question types accounts for the majority of student inquiries, and that the questions students ask change substantially as a task progresses (e.g., Student AI Inquiry Types Cs2 2026). This task-dependence matters: interaction quality is not a fixed trait of the student but is shaped by problem context, Scaffolding, and the affordances of the AI tool.

Interaction quality and learning

A complementary strand links the form of interaction to learning. Shallow or habitually narrow prompts (asking AI to produce the answer rather than to explain, probe, or evaluate) are associated with reduced learning and increased over-reliance, whereas reflective, verification-oriented interaction supports Metacognition and durable understanding. This connects student-AI interaction directly to Intelligent Tutoring design: systems can be built to invite a wider, more productive range of inquiry and to scaffold question-asking rather than merely answering.

From interaction to pedagogy

Characterizing student-AI interaction informs Instructional Design: instructors can notice when students' questioning patterns are narrow or shallow and design interventions that broaden inquiry; Teacher Role shifts toward coaching students to interact productively with AI. It also grounds AI Literacy curricula that treat effective prompting and verification as learnable skills rather than innate abilities.

Connected Concepts

• Human AI Collaboration
• Student Experience
• Prompt Engineering
• Learning Analytics
• Cognitive Offloading
• Intelligent Tutoring
• Metacognition
• Agency
• Generative AI
• LLM
• AI Literacy

Connected Articles

• Enright Staff Perspectives GenAI 2026
• Student AI Inquiry Types Cs2 2026 — Analysis of Types of Inquiries in Student-AI Interaction
• Student LLM Interaction Taxonomy Review 2026 — Student-LLM Interaction Taxonomy Review
• Teacher Authored Prompts Student AI Dialogue — Teacher-Authored Prompts in Student-AI Dialogue
• Constructing Epistemic AI Literacy Student AI Co Programming — Constructing Epistemic AI Literacy
• Icap Cognitive Engagement LLM Agents — ICAP Cognitive Engagement with LLM Agents
• Dura LLM Cs2 — Demystify, Use, Reflect, Assess (DURA): LLM Integration in CS2
• Learnlm Improving Gemini Learning — LearnLM: scenario-guided learner-AI tutoring conversations

• Li Dbagent LLM Educational Agent CS 2026 — LLM-based educational agent (DBagent) in CS education
• Strydom Human Gai Paradigms 2026 — Framing human-AI dynamics: seven GAI engagement paradigms (Strydom 2026)