The Effects of Generative AI on Computing Students' Help-Seeking Preferences
Irene Hou, Sophia Metille, Zhuo Li, Owen Man, Cynthia Zastudil, Stephen MacNeil
TL;DR
The paper examines how foundational computing students incorporate generative AI into their help-seeking practices, comparing AI-assisted support with traditional resources. Using a mixed-methods approach (survey n=47 and interviews n=8), it finds rapid AI adoption but not a complete replacement for existing resources, with usage highly task-dependent and influenced by perceived quality, latency, and trust. Key contributions include insights into when AI tools like ChatGPT add value (notably for iteration and ideation) and how student experience shapes trust and effectiveness, along with implications for classroom pedagogy and guidance on prompt design. The findings suggest that integrating generative AI into computing education can enhance learning if accompanied by scaffolding that teaches users to formulate effective requests and critically evaluate AI feedback, thereby shaping the future of help-seeking in AI-rich learning environments.
Abstract
Help-seeking is a critical way for students to learn new concepts, acquire new skills, and get unstuck when problem-solving in their computing courses. The recent proliferation of generative AI tools, such as ChatGPT, offers students a new source of help that is always available on-demand. However, it is unclear how this new resource compares to existing help-seeking resources along dimensions of perceived quality, latency, and trustworthiness. In this paper, we investigate the help-seeking preferences and experiences of computing students now that generative AI tools are available to them. We collected survey data (n=47) and conducted interviews (n=8) with computing students. Our results suggest that although these models are being rapidly adopted, they have not yet fully eclipsed traditional help resources. The help-seeking resources that students rely on continue to vary depending on the task and other factors. Finally, we observed preliminary evidence about how help-seeking with generative AI is a skill that needs to be developed, with disproportionate benefits for those who are better able to harness the capabilities of LLMs. We discuss potential implications for integrating generative AI into computing classrooms and the future of help-seeking in the era of generative AI.