Exploring Student Interactions with AI-Powered Learning Tools: A Qualitative Study Connecting Interaction Patterns to Educational Learning Theories

Abstract

With the growing use of artificial intelligence in classrooms and online learning, it has become important to understand how students actually interact with AI tools and how such interactions match with traditional ways of learning. In this study, we focused on how students engage with tools like ChatGPT, Grammarly, and Khan Academy, and tried to connect their usage patterns with well-known learning theories. A small experiment was carried out where undergraduate students completed different learning tasks using these tools, and later shared their thoughts through semi-structured interviews. We looked at four types of interaction: directive, assistive, dialogic, and empathetic—and compared them with learning approaches like behaviorism, cognitivism, constructivism, and humanism. After analyzing the interviews, we found five main themes: Feedback and Reinforcement, Cognitive Scaffolding, Dialogic Engagement, Personalization and Empathy, and Learning Agency. Our findings show that how useful an AI tool feels is not just about its features, but also about how students personally connect with it. By relating these experiences to existing educational theories, we have tried to build a framework that can help design better AI-based learning environments. This work aims to support teachers, EdTech designers, and education researchers by giving practical suggestions grounded in real student experiences.

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