Teaching Physics and AI 25/26

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.

The primary goal of physics teaching is to educate human beings to science and its nature. However, as technology continues to evolve, artificial intelligence (AI) is gaining prominence not only in technical and scientific domains but also in education. Recently, the rise of AI has sparked both concern and enthusiasm among educators, affording new opportunities and challenges for teaching and learning.
As we navigate this fast-evolving landscape, several questions arise: What competences, practices, and aspects of human development do we aim to foster, and how does the availability of AI-based systems impact their teaching and learning? How should we, as educators, frame AI-based systems and our – and our students’ – interactions with them? Is it even possible to “collaborate” with AI in meaningful ways? The panelists, all experts in physics education research, will bring a variety of expertise and perspectives, from technical considerations to ethical implications.