The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers

• URL: http://arxiv.org/abs/2405.17739v1
• Date: Tue, 28 May 2024 01:48:28 GMT
• Title: The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
• Authors: James Prather, Brent Reeves, Juho Leinonen, Stephen MacNeil, Arisoa S. Randrianasolo, Brett Becker, Bailey Kimmel, Jared Wright, Ben Briggs,
• Abstract summary: Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Many novices are now programming with generative AI (GenAI) Our findings show an unfortunate divide in the use of GenAI tools between students who accelerated and students who struggled.
• Score: 1.995977018536036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounter multiple metacognitive difficulties while programming. Novices are typically unaware of how these difficulties are hindering their progress. Meanwhile, many novices are now programming with generative AI (GenAI), which can provide complete solutions to most introductory programming problems, code suggestions, hints for next steps when stuck, and explain cryptic error messages. Its impact on novice metacognition has only started to be explored. Here we replicate a previous study that examined novice programming problem solving behavior and extend it by incorporating GenAI tools. Through 21 lab sessions consisting of participant observation, interview, and eye tracking, we explore how novices are coding with GenAI tools. Although 20 of 21 students completed the assigned programming problem, our findings show an unfortunate divide in the use of GenAI tools between students who accelerated and students who struggled. Students who accelerated were able to use GenAI to create code they already intended to make and were able to ignore unhelpful or incorrect inline code suggestions. But for students who struggled, our findings indicate that previously known metacognitive difficulties persist, and that GenAI unfortunately can compound them and even introduce new metacognitive difficulties. Furthermore, struggling students often expressed cognitive dissonance about their problem solving ability, thought they performed better than they did, and finished with an illusion of competence. Based on our observations from both groups, we propose ways to scaffold the novice GenAI experience and make suggestions for future work.

Related papers

• How Do Programming Students Use Generative AI? [7.863638253070439]
  We studied how programming students actually use generative AI tools like ChatGPT. We observed two prevalent usage strategies: to seek knowledge about general concepts and to directly generate solutions. Our findings indicate that concerns about potential decrease in programmers' agency and productivity with Generative AI are justified.
  arXiv  Detail & Related papers  (2025-01-17T10:25:41Z)
• Insights from the Frontline: GenAI Utilization Among Software Engineering Students [19.31786879151898]
  Generative AI (genAI) tools have become ubiquitous in software engineering (SE) We explore the academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students.
  arXiv  Detail & Related papers  (2024-12-20T07:30:51Z)
• Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools [4.352985782794601]
  In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. New data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. New class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time.
  arXiv  Detail & Related papers  (2024-12-19T11:01:11Z)
• Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
  We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E) We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
  arXiv  Detail & Related papers  (2024-07-09T23:13:26Z)
• Probeable Problems for Beginner-level Programming-with-AI Contests [0.0]
  We conduct a 2-hour programming contest for undergraduate Computer Science students from multiple institutions. Students were permitted to work individually or in groups, and were free to use AI tools. We analyze the extent to which the code submitted by these groups identifies missing details and identify ways in which Probeable Problems can support learning in formal and informal CS educational contexts.
  arXiv  Detail & Related papers  (2024-05-24T00:39:32Z)
• Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models [54.58108387797138]
  We investigate the effectiveness of prompt learning in code intelligence tasks. Existing automatic prompt design methods are very limited to code intelligence tasks. We propose Genetic Auto Prompt (GenAP) which utilizes an elaborate genetic algorithm to automatically design prompts.
  arXiv  Detail & Related papers  (2024-03-20T13:37:00Z)
• Identifying and Mitigating the Security Risks of Generative AI [179.2384121957896]
  This paper reports the findings of a workshop held at Google on the dual-use dilemma posed by GenAI. GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. We discuss short-term and long-term goals for the community on this topic.
  arXiv  Detail & Related papers  (2023-08-28T18:51:09Z)
• Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption [0.0]
  We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses. By identifying and rectifying GenAI's errors, students enrich their learning process. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
  arXiv  Detail & Related papers  (2023-08-23T17:20:37Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)
• Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
  Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps. We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches. We explore this process with 43 AI practitioners and real end-users.
  arXiv  Detail & Related papers  (2022-11-12T21:54:05Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.