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

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

Join us to discuss teaching social responsibility and justice in Computer Science on Monday 1st March at 2pm GMT

Scales of justice icon made by monkik from flaticon.com

With great power comes great responsibility. [1] Given their growing power in the twenty-first century, computer scientists have a duty to society to use that power responsibly and justly. How can we teach this kind of social responsibility and ethics to engineering students? Join us to discuss teaching social justice in computer science via a paper by Rodrigo Ferreira and Moshe Vardi at Rice University in Houston, Texas published in the sigcse2021.sigcse.org conference [2]. From the abstract of the preprint:

As ethical questions around the development of contemporary computer technologies have become an increasing point of public and political concern, computer science departments in universities around the world have placed renewed emphasis on tech ethics undergraduate classes as a means to educate students on the large scale social implications of their actions. Committed to the idea that tech ethics is an essential part of the undergraduate computer science educational curriculum, at Rice University this year we piloted a redesigned version of our Ethics and Accountability in Computer Science class. This effort represents our first attempt at implementing a “deep” tech ethics approach to the course.

Incorporating elements from philosophy of technology, critical media theory, and science and technology studies, we encouraged students to learn not only ethics in a “shallow” sense, examining abstract principles or values to determine right and wrong, but rather looking at a series of “deeper” questions more closely related to present issues of social justice and relying on a structural understanding of these problems to develop potential socio-technical solutions. In this article, we report on our implementation of this redesigned approach. We describe in detail the rationale and strategy for implementing this approach, present key elements of the redesigned syllabus, and discuss final student reflections and course evaluations. To conclude, we examine course achievements, limitations, and lessons learned toward the future, particularly in regard to the number escalating social protests and issues involving Covid-19.

This paper got me thinking:

Houston, we’ve had your problem!

After paging the authors in Houston with the message above there was radio silence.

Beep - beep - beep [white noise] Beep - beep - beep...

Hello Manchester, this is Houston, Can we join you?

So we’re delighted to be joined LIVE by the authors of the paper Rodrigo Ferreira and Moshe Vardi from Houston, Texas. They’ll give a lightning talk outlining the paper before we discuss it together in smaller break out groups.

Their paper describes a problem everyone in the world has had in teaching ethics in Computer Science recently. How can we make computing more ethical?

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

References

  1. Spider-Man (1962) https://en.wikipedia.org/wiki/With_great_power_comes_great_responsibility
  2. Rodrigo Ferreira and Moshe Vardi (2021) Deep Tech Ethics An Approach to Teaching Social Justice in Computer Science in Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (SIGCSE ’21), March 13–20, 2021, Virtual Event, USA. ACM, New York, NY, USA. DOI:10.1145/3408877.3432449
  3. Jack Swigert (1970) https://en.wikipedia.org/wiki/Houston,_we_have_a_problem