Tag Archives: ChatGPT

Join us to discuss the use of AI in undergraduate programming courses on Monday 4th Nov at 2pm GMT (UTC)

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Co-pilots still need pilots, but what’s the relationship between them? CC BY icon from flaticon.com

Students of programming are often encouraged to use AI assistants with little consideration for their perceptions and preferences. How do students perceptions influence their usage of AI and Large Language Models (LLMs) in undergraduate programming courses? How does the use of tools like ChatGPT and GitHub CoPilot relate to students self-belief in their own programming abilities? Join us to discuss a paper by Aadarsh Padiyath et al about this published at ICER 2024. [1] From the abstract:

The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM’s technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students’ social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students’ self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students’ use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students’ perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course.

There’s also an accompanying article and blog post to go with this paper. [2.3]

All welcome, as usual, we’ll be meeting online joining details at sigcse.cs.manchester.ac.uk/join-us

References

  1. Aadarsh Padiyath, Xinying Hou, Amy Pang, Diego Viramontes Vargas, Xingjian Gu, Tamara Nelson-Fromm, Zihan Wu, Mark Guzdial, Barbara Ericson (2024) Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course ICER ’24: Proceedings of the 2024 ACM Conference on International Computing Education Research – Volume 1, Pages 114 – 130 DOI:10.1145/3632620.3671098 (non-paywalled version at arxiv.org/abs/2406.06451)
  2. Aadarsh Padiyath (2024) Do I have a say in this or has ChatGPT already decided for me? blog post at computinged.wordpress.com
  3. Aadarsh Padiyath (2024) Do I Have a Say in This, or Has ChatGPT Already Decided for Me? XRDS: Crossroads, The ACM Magazine for students, Volume 31, Issue 1, Pages 52 – 55, DOI:10.1145/3688090 (paywalled version only)

Join us to discuss the ability of generative AI to pass exams on 4th December at 2pm GMT

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CC-licensed exam image from flaticon.com

How good is generative AI at passing exams? What does this tell us about how we could design better assessments? Join us on Monday 4th December at 2pm GMT (UTC) to discuss a paper on this by Joyce Mahon, Brian Mac Namee and Brett Becker at University College Dublin published at UKICER earlier this year. [1] From the abstract:

We investigate the capabilities of ChatGPT (GPT-4) on second level (high-school) computer science examinations: the UK A-Level and Irish Leaving Certificate. Both are national, government-set / approved, and centrally assessed examinations. We also evaluate performance differences in exams made publicly available before and after the ChatGPT knowledge cutoff date, and investigate what types of question ChatGPT struggles with.

We find that ChatGPT is capable of achieving very high marks on both exams and that the performance difference before and after the knowledge cutoff date are minimal. We also observe that ChatGPT struggles with questions involving symbols or images, which can be mitigated when in-text information ‘fills in the gaps’. Additionally, GPT-4 performance can be negatively impacted when an initial inaccurate answer leads to further inaccuracies in subsequent parts of the same question. Finally, the element of choice on the Leaving Certificate is a significant advantage in achieving a high grade. Notably, there are minimal occurrences of hallucinations in answers and few errors in solutions not involving images.

These results reveal several strengths and weaknesses of these exams in terms of how generative AI performs on them and have implications for exam design, the construction of marking schemes, and could also shift the focus of what is examined and how.

We’ll be joined by the papers lead author Joyce, who will give us a lightning talk summary of her paper to start our discussion. All welcome, as usual we’ll be meeting on zoom details at sigcse.cs.manchester.ac.uk/join-us

References

  1. Joyce Mahon, Brian MacNamee and Brett A. Becker (2023) No More Pencils No More Books: Capabilities of Generative AI on Irish and UK Computer Science School Leaving Examinations. In The United Kingdom and Ireland Computing Education Research conference (UKICER 2023), September 07–08, 2023, Swansea, Wales UK. ACM, New York, NY, USA, 7 pages. DOI: 10.1145/3610969.3610982

Join us on zoom to discuss the implications of programming getting easier, Monday 15th May at 2pm BST

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Webcomic via reddit.com/r/ProgrammerHumor

Programming is hard, or at least it used to be. AI code generators like Amazon’s CodeWhisperer, DeepMind’s AlphaCode, GitHub’s CoPilot, Replit’s Ghostwriter and many others now make programming easier, at least for some people, some of the time. What opportunities and challenges do these new tools present for educators? Join us on Zoom to discuss an award winning paper by Brett Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather and Eddie Antonio Santos at University College Dublin, the University of Auckland and Abilene Christian University on this very topic. [1] We’ll be joined by two of the co-authors who will present a lightning talk to kick-off our discussion, for our monthly ACM journal club meetup. Here’s the abstract of his paper:

The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on overcoming otherwise mitigating the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.

All welcome, as usual we’ll be meeting on zoom at 2pm BST (UTC+1), details at sigcse.cs.manchester.ac.uk/join-us. Thanks to Sue Sentance at the University of Cambridge for nominating this paper for discussion.

See also linkedin.com/posts/duncanhull_ai-codewhisperer-alphacode-activity-7051921278923915264-7i_5

References

  1. Brett A. Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, Eddie Antonio Santos (2023) Programming Is Hard – Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation in Proceedings of the 54th ACM Technical Symposium on Computer Science Education: SIGCSE 2023, pages 500–506, DOI: 10.1145/3545945.3569759

Join us to discuss using AI to solve simple programming problems on Monday 3rd April at 2pm BST

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CC licensed pilot icon from flaticon.com

Maybe you wrote that code and maybe you didn’t. If AI helped you, such as the OpenAI Codex in GitHub Copilot, how did it solve your problem? How much did Artificial Intelligence help or hinder your solution? Join us to discuss a paper by Michel Wermelinger from the Open University published in the SIGCSE technical symposium earlier this month on this very topic. [1] We’ll be joined by Michel who will present a lightning talk to kick-off our discussion. Here’s the abstract of his paper:

The teaching and assessment of introductory programming involves writing code that solves a problem described by text. Previous research found that OpenAI’s Codex, a natural language machine learning model trained on billions of lines of code, performs well on many programming problems, often generating correct and readable Python code. GitHub’s version of Codex, Copilot, is freely available to students. This raises pedagogic and academic integrity concerns. Educators need to know what Copilot is capable of, in order to adapt their teaching to AI-powered programming assistants. Previous research evaluated the most performant Codex model quantitatively, e.g. how many problems have at least one correct suggestion that passes all tests. Here I evaluate Copilot instead, to see if and how it differs from Codex, and look qualitatively at the generated suggestions, to understand the limitations of Copilot. I also report on the experience of using Copilot for other activities asked of students in programming courses: explaining code, generating tests and fixing bugs. The paper concludes with a discussion of the implications of the observed capabilities for the teaching of programming.

All welcome, as usual we’ll be meeting on zoom, details at sigcse.cs.manchester.ac.uk/join-us. 

References

  1. Michel Wermelinger (2023) Using GitHub Copilot to Solve Simple Programming Problems in Proceedings of the 54th ACM Technical Symposium on Computer Science Education Pages SIGCSE 2023 page 172–178 DOI: 10.1145/3545945.3569830