# The Effects of Generative AI on Computing Students' Help-Seeking Preferences

arXiv: https://arxiv.org/abs/2401.02262 · HTML: https://mlsanity.com/paper/2401.02262/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2024.

## Built on by (most cited first, 23 of 23)

- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
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- [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024)
- [Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course](https://mlsanity.com/paper/2406.06451/) (2024)
- [Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses](https://mlsanity.com/paper/2410.06865/) (2024)
- ["Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models](https://mlsanity.com/paper/2403.09409/) (2024)
- [The Evolving Usage of GenAI by Computing Students](https://mlsanity.com/paper/2412.16453/) (2024)
- [ChatGPT in Data Visualization Education: A Student Perspective](https://mlsanity.com/paper/2405.00748/) (2024)
- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails](https://mlsanity.com/paper/2504.11146/) (2025)
- [Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems using Large Multimodal Models](https://mlsanity.com/paper/2412.11088/) (2024)
- [Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners](https://mlsanity.com/paper/2412.12800/) (2024)
- [The Role of Generative AI in Software Student CollaborAItion](https://mlsanity.com/paper/2501.14084/) (2025)
- ["Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbots](https://mlsanity.com/paper/2603.00057/) (2026)
- [Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective](https://mlsanity.com/paper/2410.08899/) (2024)
- ["All Roads Lead to ChatGPT": How Generative AI is Eroding Social Interactions and Student Learning Communities](https://mlsanity.com/paper/2504.09779/) (2025)
- [Integrating Generative AI in Hackathons: Opportunities, Challenges, and Educational Implications](https://mlsanity.com/paper/2401.17434/) (2024)
- ["I Just Need GPT to Refine My Prompts": Rethinking Onboarding and Help-Seeking with Generative 3D Modeling Tools](https://mlsanity.com/paper/2603.29118/) (2026)
- [Exploring the Value of Diverse LLM Explanations in Introductory Programming](https://mlsanity.com/paper/2606.28882/) (2026)
- [To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks](https://mlsanity.com/paper/2606.30549/) (2026)
- [From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMs](https://mlsanity.com/paper/2607.08009/) (2026)
- [Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks](https://mlsanity.com/paper/2608.17919/) (2026)

## Abstract (arXiv)

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.