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

• URL: http://arxiv.org/abs/2512.00519v1
• Date: Sat, 29 Nov 2025 15:27:37 GMT
• Title: Exploring Student Interactions with AI-Powered Learning Tools: A Qualitative Study Connecting Interaction Patterns to Educational Learning Theories
• Authors: Prathamesh Muzumdar, Sumanth Cheemalapati,
• Abstract summary: This study focuses on how students engage with tools like ChatGPT, Grammarly, and Khan Academy.<n>We looked at four types of interaction directive, assistive, dialogic, and empathetic and compared them with learning approaches like behaviorism, cognitivism, constructivism, and humanism.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Qualitative Study of IT Students' Skill Development: Comparing Online and Face- to-Face Learning Environments [0.3279527871567901]
  This study explores and tries to better understand, specifically the IT student's experiences and perceived skills development in online and face-to-face learning environments.<n>Data was collected through semi-structured interviews by focusing on the student and asking for their personal experience on skill development through online and face-to-face learning environments.<n>Results suggest that face-to-face learning may develop a better communication and collaborative skills more effectively while experiencing a synchronous interaction.
  arXiv  Detail & Related papers  (2026-01-31T16:12:22Z)
• OnlineMate: An LLM-Based Multi-Agent Companion System for Cognitive Support in Online Learning [20.08144763551689]
  We propose OnlineMate, a multi-agent learning companion system driven by large language models (LLMs)<n>OnlineMate is capable of simulating peer-like agent roles, adapting to learners' cognitive states during collaborative discussions, and inferring their psychological states, such as misunderstandings, confusion, or motivation.<n> Experimental results in simulated learning scenarios demonstrate that OnlineMate effectively fosters deep learning and discussions while enhancing cognitive engagement in online educational settings.
  arXiv  Detail & Related papers  (2025-09-18T09:56:45Z)
• Robots and Children that Learn Together : Improving Knowledge Retention by Teaching Peer-Like Interactive Robots [41.94295877935867]
  This study introduces Interactive Reinforcement Learning as a cognitive model for teachable social robots.<n>Children in the LbT condition achieved significantly higher retention gains compared to those in the self-practice condition.<n>This work makes two contributions: (1) it introduces Interactive RL as a pedagogically effective and scalable model for peer-robot learning, and (2) it demonstrates, for the first time, the feasibility of deploying multiple autonomous robots simultaneously in real classrooms.
  arXiv  Detail & Related papers  (2025-06-23T07:51:04Z)
• Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning [42.09560737219404]
  Large Language Models show promise in human-like communication, but their standalone use is hindered by memory constraints and contextual incoherence.<n>This work presents a multimodal, cognitively inspired framework that enhances LLM-based autonomous decision-making in social and task-oriented Human-Robot Interaction.<n>To further enhance autonomy and personalization, we introduce a memory system for selecting, storing and retrieving experiences.
  arXiv  Detail & Related papers  (2025-04-02T10:45:41Z)
• Scaffolding Creativity: Integrating Generative AI Tools and Real-world Experiences in Business Education [0.0]
  This study investigates the intersection of Generative AI tools and experiential learning in business education.<n>Through a case study of an innovative undergraduate course, we examine how students interact with and adapt to various AI modalities.<n>Our findings reveal how this integrated approach enables novice users to overcome creative barriers, accelerates skill acquisition, and creates a dynamic interplay between AI-generated insights and real-world validation.
  arXiv  Detail & Related papers  (2025-01-11T12:31:10Z)
• Exploring User Perspectives on ChatGPT: Applications, Perceptions, and Implications for AI-Integrated Education [40.38809129759498]
  ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training. On one hand, some users view it as a transformative tool capable of amplifying student self-efficacy and learning motivation. On the other hand, there is a degree of apprehension among concerned users.
  arXiv  Detail & Related papers  (2023-05-22T15:13:14Z)
• Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture [15.19996462016215]
  This paper proposes an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent.
  arXiv  Detail & Related papers  (2022-12-14T22:57:44Z)
• Hierarchical Affordance Discovery using Intrinsic Motivation [69.9674326582747]
  We propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties.
  arXiv  Detail & Related papers  (2020-09-23T07:18:21Z)
• Teaching Tech to Talk: K-12 Conversational Artificial Intelligence Literacy Curriculum and Development Tools [9.797319790710711]
  We evaluate our Conversational Agent Interface for MIT App Inventor and workshop curriculum with respect to AI competencies. We found students struggled most with the concepts of AI ethics and learning, and recommend emphasizing these topics when teaching.
  arXiv  Detail & Related papers  (2020-09-11T20:52:46Z)
• Learning and Reasoning for Robot Dialog and Navigation Tasks [44.364322669414776]
  We develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks.
  arXiv  Detail & Related papers  (2020-05-20T03:20:42Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.