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Building student AI literacy at scale
Authors
Erica Cargill
Associate Dean, Aberdeen Business School
Dr Shonagh Douglas
Accounting and Finance Senior Lecturer, Aberdeen Business School
Jenny Blair
Online Learning Developer, Aberdeen Business School
Like many business schools, at Aberdeen Business School we have faced challenges with the misuse of generative AI software tools. Whilst action is being taken to embrace generative AI and effectively equip students with AI related skills (an increasingly important business school graduate attribute), the threat to academic integrity resulting from its misuse remains. A first step in combating this misuse is to ensure students are clear on the school’s approach to generative AI in assessment.
Students are bombarded with information throughout their studies, with communication on AI policies and guidance now forming a key part of this. This often results in information overload, with detail which is not directly relevant at the point of delivery being overlooked or sometimes completely forgotten. Our own experiences of dealing with AI misuse identified an information gap for some students. To combat this, we developed a short mandatory training intervention for all business school students.
Cognisant of the backwash effect which determines that assessment drives learning, the intervention was designed as a short Moodle quiz, which was attached to an assignment Dropbox, whereby the Dropbox would not be released until the quiz was satisfactorily completed. The quiz had five questions covering key concepts, with a multiple-choice approach to the assessment of each question. Completion time was expected to be around 10 minutes. Key areas of inclusion, or considerations for those who would like to follow a similar approach are:
Our experience identified that students understood the parameters around the use of well-known tools like ChatGPT and Microsoft Co-Pilot. However, there are many other AI tools, often embedded within other pieces of software, which students had less understanding of. Ensure that the quiz appraises students of what AI is and the many different forms it can take. Once this is established, the school’s policy towards the use of AI can be explained.
Give guidance on the different levels of AI use and be clear about what action the student needs to take at each level. Perkins et al. (2023) provide a useful scale for this.
Explain referencing requirements in relation to AI use in assessment.
If using AI detection software, explain how this will be used and alleviate concerns around false detection.
Seeing multiple versions of a document which demonstrates how that work has developed over time provides a useful assurance of integrity. Encouraging students to keep file versions, or making this part of the assignment brief, can be useful for some assignments.
If using a similar Dropbox linked quiz, the dissemination of this is also important. Whilst the set-up forces completion before submission, the desired completion would ideally be before coursework writing commences. To encourage this, we created QR codes for the quiz which were shared with the Module Coordinators who then explained the purpose of the quiz and encouraged completion within the classroom.
Finally, whilst the students were asked to complete the quiz only once (within one module), it was important that they understood the information contained within applies to all modules. This required careful communication from both course and module leaders.
Over 1,000 students have completed the quiz at Aberdeen Business School to date. The usefulness of the quiz in terms of helping students understand how Generative AI can and should be used within their assessments is as follows:
The 2024/25 academic year will require further development of AI literacy in the student body, but the quiz has served as a useful first step in raising awareness in both students and staff.
Reference:
Perkins et al., 2024. The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. (2024). Journal of University Teaching and Learning Practice, 21(06). https://doi.org/10.53761/q3azde36