Assessing the Provenance of Student Coursework

The Higher Education sector is mobilising vast resources in its response to the use of Generative AI in student coursework. This response includes institutional policies, training for staff and students and AI detection tools. This paper is concerned with one aspect of this fast-moving area; the assessment of the provenance of a piece of written student coursework. The question of the provenance of student work is a surprisingly complex one, which, in truth can only ever be answered by the student themselves. As academics we must understand the difference between checking for plagiarism and generative AI use. When assessing a student's possible use of generative AI there is no ground truth for us to test against and this makes the detection of AI use a completely different problem to plagiarism detection. A range of AI detection tools are available, some of which have been adopted within the sector. Some of these tools have high detection rates, however, most suffer with false positive rates meaning institutions would be falsely accusing hundreds of students per year of committing academic offences.

This paper explores a different approach to this problem which complements the use of AI detection tools. Rather than examining the work submitted by a student, the author examines the creation and editing of the that work over time. This gives an understanding how a piece of work was written, and most importantly how it has been edited. Inspecting a documents history requires that it is written on a cloud-based platform with version history enabled. The author has created a tool which sits on top of the cloud-based platform and integrates with the virtual learning environment. The tool records each time a student digitally touches their work, and the changes are recorded. The tool interface gives an overview for a cohort, with the ability to delve more deeply into an individual submission.

The result is an easily accessible interactive history of a document during its development, giving some kind of provenance to that document. This history of construction and editing, shows how a piece of written work has been crafted over time, providing useful evidence of academic practice. Data on the points where students digitally touch their work can also be useful beyond questions of academic practice. The Author gives an example of using a data-driven approach to give formative feedback and discusses how data-driven approaches could become common in teaching practice.