"All Roads Lead to ChatGPT": How Generative AI is Eroding Social Interactions and Student Learning Communities

2025-04-14

AI Summary

This study investigates the erosion of the social dimensions of undergraduate learning by generative AI—specifically, its displacement of peer help-seeking, weakening of classroom collaboration, and exacerbation of loneliness and motivational decline. Employing a cross-institutional qualitative design, we conducted semi-structured in-depth interviews with 17 computer science undergraduates, analyzed via thematic coding and comparative analysis. We introduce the novel concept of “AI-mediated help-seeking,” empirically demonstrating that generative AI reduces peer instruction, diminishes students’ sense of belonging, and structurally weakens informal social support networks. By extending AI-in-education impact assessment beyond individual cognition to encompass learning communities and classroom sociodynamics, this work advances both theoretical frameworks and empirical understanding of the deeper socio-educational consequences of technology integration.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Societal Impact of AI

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

The widespread adoption of generative AI is already impacting learning and help-seeking. While the benefits of generative AI are well-understood, recent studies have also raised concerns about increased potential for cheating and negative impacts on students' metacognition and critical thinking. However, the potential impacts on social interactions, peer learning, and classroom dynamics are not yet well understood. To investigate these aspects, we conducted 17 semi-structured interviews with undergraduate computing students across seven R1 universities in North America. Our findings suggest that help-seeking requests are now often mediated by generative AI. For example, students often redirected questions from their peers to generative AI instead of providing assistance themselves, undermining peer interaction. Students also reported feeling increasingly isolated and demotivated as the social support systems they rely on begin to break down. These findings are concerning given the important role that social interactions play in students' learning and sense of belonging.

Problem

• Investigates how generative AI reduces peer learning interactions
• Examines AI's impact on student isolation and motivation
• Studies AI-mediated help-seeking effects on classroom dynamics

Innovation

• Used semi-structured interviews for data collection
• Studied generative AI's impact on peer learning
• Analyzed social interaction changes in students