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

TL;DR

This study investigates how generative AI affects social interactions and learning communities in undergraduate computing education. Through 17 semi-structured interviews across seven R1 universities and analyzed with reflexive thematic analysis, the authors find that GenAI frequently mediates help-seeking, reducing direct peer interactions and contributing to feelings of isolation. The results highlight impacts on mentorship, motivation, and sense of belonging, suggesting that AI use can erode informal learning cultures and the hidden curriculum. The work emphasizes the need for strategies that balance AI integration with preserving essential human social interactions to sustain meaningful learning outcomes.

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.