Imperial College London has invested in a variety of projects to explore the use of generative artificial intelligence (GAI) and machine learning (ML) in higher education. The main project which I will discuss aims to test the effectiveness of Large Language Models (LLMs) in assisting large-scale qualitative research and consequently whether workflows incorporating LLMs can provide useful and timely feedback to instructors on the impact of their teaching. The focus of the work is on student use of scientific argumentation in lab reports. We have constructed a codebook for scientific argumentation to identify elements of argumentation, the relationships between those elements, and their veracity. Through building a training dataset of over 250 lab reports from students in their first- and second-year lab courses, we aim to analyse the whole set of 1899 lab reports from two cohorts of students by utilizing fine-tuning of open-source LLMs to automate the coding process (Fussell et al., 2025). The specific purpose here is to test how changes to teaching between the two cohorts affected students’ scientific argumentation skills, and, therefore, help us to evaluate the impact of the changes. In addition to this, I will provide an overview of the projects that are currently being undertaken across Imperial related to AI and education. These institutionally funded projects include: (1) the use of GAI in providing feedback to students through the in-house problem sheet web-interface Lambda Feedback; (2) the use of LLMs to analyse and provide in-the-moment feedback to students on the structural elements of their lab reports; (3) using ML to identify at-risk students through analysis of interaction data from Lambda feedback; and (4) collecting data on students’ views about how instructors use GAI in their teaching.
Michael F.J. Fox has worked on a wide range of projects in physics education research, from workforce development for the quantum industry through to analysis of the process of curriculum and culture change in a physics department. His core interest is in what and how students learn in physics teaching laboratories. This has been informed by his own experience as a student in undergraduate teaching labs through to his PhD research on analysing data on plasma turbulence in nuclear fusion reactors. How to teach experimental physics effectively came to the forefront when he was teaching high-school physics, leading to post-doctoral work on assessing student learning in teaching labs using the E-CLASS and MAPLE surveys during his time in the Lewandowski group in Boulder, Colorado. He has recently been appointed as the head of the teaching laboratories in the Department of Physics at Imperial College London, where he has started to implement evidence-based practices.
