Explore Public's Perspectives on Generative AI in Computer Science (CS) Education: A Social Media Data Analysis

This research-to-practice full paper aims to analyze the public's comments on generative artificial intelligence (GAI) in computer science (CS) education, by the BERT-based model and Large Language Model (LLM) approaches to sentiment analysis and contextualize the results within broader educational and technological landscapes. Artificial intelligence (AI) has played a crucial role in advancing technical development throughout many areas. Evidence points toward the likelihood of major developmental breakthroughs unfolding soon in those sectors. Education is one such area. While there is certainly a possibility for hype and unfulfilled promises, the advent of available GAI platforms, such as ChatGPT, has caused a surge of scholarly interest in the impact of these technologies on CS education. Amid the growing debate, both the potential benefits and concerns of GAI in this sector are increasingly coming to the fore as people grapple with the tradeoffs associated with these technologies when applied in education settings. One can imagine the range of conversations around the topic, but that is difficult to use as input for policymakers and administrators without a more concrete understanding. To wit, there remain open questions about which benefits and concerns people tend to focus on when discussing GAI in education. This large-scale qualitative study addresses that gap by exploring the public's perspectives on GAI in CS education. We engage in this work by collecting and analyzing data from social media platforms, specifically Reddit comments. The social media dataset was analyzed using machine learning (ML) techniques to identify topics based on sentiment analysis. The study's objective was to document and characterize the public's perspectives concerning the general characteristics of GAI, its features related to learning, and its usability in educational settings. Through sentiment analysis using Large Language Models (LLM), the study revealed an overall positive public perception toward using generative AI in CS education, with over 57% of comments being favorable, while also identifying prominent topics of interest and concerns, such as the potential benefits of personalized learning support and automated grading, as well as issues like academic dishonesty, perpetuation of biases, over-reliance on AI hindering critical thinking, displacement of human instructors, and the need for updated curricula. The insights gleaned from the analysis will be instrumental in computing educators gaining a more profound comprehension of GAI 'srole in education and the subsequent development of GAI -enriched curricula.