On 19 April 2023, I arrived slightly late for an online seminar about ChatGPT and generative AI. This blog post share some of the notes that I made during the session. It might be useful to read this post in conjunction with an earlier blog that was written on the same topic that summarises a workshop organised by the OU Knowledge Media Institute (KMI). These notes are pretty rough-and-ready, since they were edited together a month after the event took place.
Mike Richards, from the School of Computing and Communications, began by summarising some research that he had carried out with a number of colleagues. Five tutors were interviewed. When it comes to reviewing and marking assignments, it was noted that tutors are sensitive to changes in formatting style, voice and vocabulary.
Tutors rely on module teams and central systems for plagiarism detection, but they can and do pick up on things themselves. ALs don’t like referring students to disciplinary processes. They are cautious; they usually have a very high level of suspicion before they contact staff tutors and invoke the academic conduct processes. In the cases where the identify issues, they take opportunities to make a teaching point to students.
Tutors wish to maintain positive relationships with students, but they are worried about the implications of raising academic conduct referrals and potential professional consequences if they raised unwarranted academic conduct concerns. Of course, there are no consequences for tutors. It is, of course, the academic conduct officers who make the decisions.
During the session, I captured the following important points. The first point was that assessment is vulnerable to ChatGPT. Specifically, highly structured essays are vulnerable, but these type of essays are used to develop student skills.
ChatGPT perform less well with anything to do with reflections about learning, since anything that is produced will not sound genuine.
There is a role for ChatGPT (or generative AI) detection software, but there are issues with detection tools, since they present a high rate of false positives. Detectors only gives you a probability that something is synthetic, but doesn’t provide evidence like TurnItIn.
Tutors are very important. They are able to spot synthetic solutions; they can identify bland, superficial, repetitive and irrelevant materials in a way that automated tools cannot. To assist with this, and to help our tutors, the university needs to provide better plagiarism training.
A recognised issue is that ChatGPT will generate superficially compelling references that are completely fake. Asking ALs to scrutinise the referencing would go some way to determine whether a chunk of text has been automatically generated. ChatGPT doesn’t currently do referencing at the moment, but there is a possibility this might change if it is connected with public databases.
The next step of this project is to write up findings and to have conversations with other faculties. There is also a university working group which aims to generate an assessment authoring guide to mitigate against generative AI. There is, of course, the need to do more studies. There might also be the need to adopt subject or discipline specific approaches.
The closing thoughts shared during the seminar are important: we need to teach all students about the consequences of AI. Perhaps there needs to be some Open Educational Resources on the topic, perhaps something on OpenLearn that offers a sketch of what it can and cannot do. A closing point was that there are no ‘no-cost’ options. The university needs to carefully consider the role and purpose of assessments. Doing nothing is not an option.
During the discussion session, I noted down a couple of interesting questions: what question types would cause large language modules to perform sufficiently bad from caring to not caring? Also, what limits its abilities? ChatGPT writes in generalities. Its responses comes from how questions are worded. There is also the issue of concreteness. Assessment tasks are often related to specifics, in terms of activities texts, module materials, and forum posts. If generative AI cannot access the texts that students need to access and critically evaluate to develop their skills, its uses are, of course, limited.
One of the key points that was emphasised was the importance of the tutor. They have such an important role to play in not only identifying instances of potential academic misconduct, but also in educating students about generative AI, and the risks these tools present.
It is also useful to reflect on the point that tutors can spot changes in writing style. There is the possibility that the stylistic quality of generated text is a characteristic that could be used to respond to not only ChatGPT, but also contract cheating. At the time of writing, anti-plagiarism detection tools such as TurnItIn only evaluate individual assignments. In the arms race to ensure academic integrity, the next generation of tools might analyse text across a number of submissions whilst taking into account the characteristics or structure of individual assessments.
I expect there will be a multi-faceted institutional response to generative AI. There will be education: of students, tutors, and module teams. Students will be informed about the ethical risks of using generative AI, and the practical consequences of academic misconduct. Tutors will be provided with more information about what generative AI is, and offered more development to facilitate sessions to help students. Module teams will have an increasing responsibility to develop assessment approaches that proactively mitigate against the development of generative AI. Also, technology will play a role in detecting academic misconduct, and new procedures will be developed to assist academic conduct officers.
An acknowledgement is due to Mike Richards and everyone who took part in aspects of research which is summarised here. A thank you goes to Daniel Gooch, who facilitated the event.