Project Overview
Experiences of Learning to Code
Joe Marsh Rossney 
This page provides a concise overview of the project aims, methods and headline results.
1 Introduction
This study sought to understand how the experiences of undergraduate physics students taking programming courses at the University of Edinburgh have been changing due to the sudden availability of Generative Artificial Intelligence (GenAI) systems, such as OpenAI’s ChatGPT and Microsoft/GitHub’s CoPilot. The essential aim was to produce primary evidence from students’ perspectives that can be used by Course Organisers who are intending to update their programming courses to incorporate limited use of GenAI in a manner that is purposeful, responsible and critical.
The premise of our study is that the experiences and perspectives of the 2024 cohort of undergraduate students are (perhaps uniquely) rich in information that can help educators to navigate a path towards the explicit inclusion of GenAI in the teaching of introductory programming.
Programming courses in the School of Physics & Astronomy are heavily based on project work, either through formative mini-projects or larger projects tied to substantial fractions of the overall course credit. This study might also be framed as an inquiry into the use of GenAI for student-driven project work, albeit with the programming focus.
2 Methods
The main inquiry took the form of a series of semi-structured interviews with 24 student participants, whose experiences span the periods before and after the advent of GenAI.
Candidate interviewees were recruited through a survey (N=172), with 24 students selected for interview based on a selection process aiming to achieve uniform sampling across yeargroups and a dichotomised proxy for self-identified gender.
The sign-up survey also included Likert-type questions, allowing us to capture a broader snapshot of student attitudes towards GenAI in relation to programming courses.
The interviews were conducted by non-faculty members of the project team, and were held over Microsoft Teams, with the recordings saved to the University of Edinburgh’s secure storage.
Student interviews were broadly guided towards an exploration of certain themes identified as potential indicators of deep learning, such as project ownership, while being responsive to new themes emerging from the student participants. The interviews were then transcribed and coded; portions of text containing pertinent information were ‘tagged’ with one or more discrete codes that correspond to themes contained in the text, with a new code created every time a new theme is encountered.
3 Key Findings
To do.
Acknowledgements
The authors would like to thank Kristel Torokoff for playing an instrumental role in securing financial support for this project via the School of Physics and Astronomy. We would also like to thank Kristel Torokoff and Joe Zuntz for conversations that helped to shape this project.
Financial support
We gratefully acknowledge that funding for this Principle’s Teaching Award Scholarship (PTAS) project was provided by the University of Edinburgh Development Trust.
JMR was directly supported by both PTAS and the School of Physics & Astronomy at the University of Edinburgh. SH was supported by PTAS. PGGE was supported by the School of Physics & Astronomy through the Career Development Summer Scholarship programme.
Correspondence
joemar@ceh.ac.ukfor enquiries related to the project, website, code and data.
Reuse
Copyright
Citation
@online{MarshRossney2025,
author = {Marsh Rossney, Joe and Hogarth, Sarah and Gabriel Garcia
Elizondo, Polux and Galloway, Ross and Smith, Britton},
title = {Experiences of {Learning} to {Code:} {Perspectives} of
{Undergraduate} {Physics} {Students} in 2024},
date = {2025-08},
url = {https://ExpLrnCode-2024.github.io/},
langid = {en},
abstract = {This site provides access to research materials and
outputs produced during the \_“Experiences of Learning to Code”\_
project, which was run by a staff-student collaboration in the
School of Physics \& Astronomy at the University of Edinburgh from
June-\/-December 2024. The study sought to understand how the
experiences of undergraduate physics students taking programming
courses have been changing due to the sudden availability of
Generative Artificial Intelligence (GenAI) systems. The main inquiry
took the form of a series of semi-structured interviews with 24
student participants, whose experiences span the periods before and
after the advent of GenAI.}
}