When designing assessments for your course, it is important to consider the vulnerability and resilience of each assessment type in relation to the use of generative AI. A useful tool for the evaluation of your assessment is the AI Risk Measure Scale (ARMS), developed by the University of Greenwich (De Vita and Brown, 2023).
| AI Risk Measure Scale (ARMS) | Description | Examples | |
| 1 | Very low | Highly unlikely AI can be used to produce this type of assessment. |
Assignments that embed authenticity in the design (e.g., field trip + reflective report), assignments that allow establishing the identity of the person (e.g., presentation, in-person exams). Subjective assignments that require personal reflection or creative thinking, such as personal narratives or artistic projects. These types of assignments are typically based on the student's opinions and insights, which are difficult to replicate using AI. |
| 2 | Low |
Potential use of AI to produce the assessment. Very unlikely to have a significant impact on quality and / or originality. |
Assignments that draw on unique teaching material (e.g., novel cases produced by the tutor. Assignments that have clear guidelines, such as solving math problems or coding exercises, where AI could assist but the student's approach or or solution is what is being evaluated as the main focus of the assignment. |
| 3 | Moderate |
Moderate likelihood of AI use to produce the assessment. Could have moderate impact on quality and / or originality. |
Assignments where AI could be used to assist students in completing the assignment, but the final work would still require the student's critical thinking, analysis and interpretation. Assignments that require a more complex analysis of a topic, e.g., critical analysis essay or scientific report. Students may use AI tools to help with data analysis, visualisation or interpretation in some areas. but the writing and argumentation are largely based on the student's understanding and critical thinking. |
| 4 | High |
Easy to use AI to produce the assessment. Could have significant impact on quality and / or originality. |
Assignments that focus on well-published company case studies (e.g., Innocent, Apple. Boohoo, Starbucks etc.) and rather generic topics (e.g., advantages and disadvantages of FDI) which students can easily obtain through AI bots. Assignments that involve sophisticated algorithms or complex modelling such as financial forecasting, predictive analytics or image recognition, where students could use AI to generate both results and insights / commentary. |
| 5 | Very High |
Very easy to use AI to produce the assessment. Will have a significant impact on quality and / or originality. |
Assignments that require students to produce summaries or abstracts of published articles, reports or research papers, this includes research proposals. These assignments require no input / modification from students and can be entirely produced by AI. Assignments that involve large scale data processing, such as machine learning projects or artificial intelligence simulations, where students could rely entirely on AI to create the work. |
CPD activity for AI and authentic assessment is provided through the Centre for Excellence in Learning and Teaching and can be booked through the CPD calendar.
We have undertaken an evaluation of AI vulnerability of the most commonly used assessment types at Suffolk, using ARMS. Key insights from the evaluation are:
| AI Risk | Assessment type | |
| 1 - very low risk | In person exam (oral or written), OSCE / practical, practical assessment, class discussion, simulation, simulation, observation, research participation, physical artefact. | |
| 2 - low | Digital artefacts, reflection, job application / interview. | |
| 2-3 low / moderate | 4 - high | Dissertation, report, portfolio, presentation, video, podcast, blog, discussion post, policy briefing, peer assessment. |
| 4 - high | 5 - very high | Essay, case study, research proposal, multiple choice test (online), book review |
Course teams have already been asked to consider each of their assessments and their vulnerability or resilience, and where needed to complete a course modification. We strongly encourage course teams to reflect and evaluate the effectiveness of their chosen assessments as part of the marking and moderation process.