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Generative AI models, specifically large language models (LLMs), have made strides towards the long-standing goal of text-to-code generation. This progress has invited numerous studies of user interaction. However, less is known about the struggles and strategies of non-experts, for whom each step of the text-to-code problem presents challenges: describing their intent in natural language, evaluating the correctness of generated code, and editing prompts when the generated code is incorrect. This paper presents a large-scale controlled study of how 120 beginning coders across three academic institutions approach writing and editing prompts. A novel experimental design allows us to target specific steps in the text-to-code process and reveals that beginners struggle with writ- ing and editing prompts, even for problems at their skill level and when correctness is automatically determined. Our mixed-methods evaluation provides insight into student processes and perceptions with key implications for non-expert Code LLM use within and outside of education.
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Non-Expert Programmers in the Generative AI Future
Generative AI is rapidly transforming the practice of programming. At the same time, our understanding of who writes programs, for what purposes, and how they program, has been evolving. By facilitating natural-language-to-code interactions, large language models for code have the potential to open up programming work to a broader range of workers. While existing work finds productivity benefits for expert programmers, interactions with non-experts are less well-studied. In this paper, we consider the future of programming for non-experts through a controlled study of 67 non-programmers. Our study reveals multiple barriers to effective use of large language models of code for non-experts, including several aspects of technical communication. Comparing our results to a prior study of beginning programmers illuminates the ways in which a traditional introductory programming class does and does not equip students to effectively work with generative AI. Drawing on our empirical findings, we lay out a vision for how to empower non-expert programmers to leverage generative AI for a more equitable future of programming.
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- PAR ID:
- 10525385
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400710179
- Page Range / eLocation ID:
- 1 to 19
- Format(s):
- Medium: X
- Location:
- Newcastle upon Tyne, United Kingdom
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3663384.3663393
