The AI Risk Measurement Scale (ARMS) was designed by Katharina De Vita (Manchester Metropolitan University, UK) and Gary Brown (University of Greenwich, UK). The ARMS tool was created to assist faculty in assessing the risk of students misusing generative AI (genAI) in their assignments. 

ARMS classifies assessments into five categories, ranging from 'very low' to 'very high' risk, according to the probability and impact of genAI misuse on academic integrity. Each risk level is accompanied by descriptions and examples, enabling educators to better gauge the potential for genAI use in various academic tasks. 

We encourage yo to use the ARMS tool when designing assessments and other academic activities to evaluate the risk of generative AI misuse. This tool provides clear risk levels and examples to support integrity in academic work.

However, there exists a third category of assignments,  that we may call AI-INCOMPATIBLE ASSIGNMENTS. These are traditional assignments where the use of AI tools is prohibited by the professor, as this would undermine the intended purpose of the exercise.

It's important to recognize that currently no reliable proctoring tools can prevent or detect the use of AI by students, and it remains uncertain if there ever will be. This entials that accurate detection of AI use in AI-Incompatible assignments is not a possibility at this time, especially, if completed at home, as in-person proctoring is not an option in that case. 

Consequently, faculty members are advised to reconsider and possibly re-structure these AI-Incompatible assignments into AI-proof or AI-integrated formats.  

We are here to assist you in reorienting your assignments to align with your learning objectives! Please contact us at learning.innovation@ie.edu to schedule an office hours meeting on Zoom.

Designing assignments with AI is pretty simple. Remember as always that, the richer the information you give to the system, the better the results will be. The prompt on the left, for instance, details exhaustively all the different parameters ChatGPT-4 needs to consider in order to elaborate an assignment (right image).

Traditionally, students have been required to complete class readings before attending sessions, where professors would then elaborate on key points and provide deeper insights. However, with the growing availability of AI tools that can help students synthesize and explain texts, a new approach is emerging—one that flips this conventional sequence with readings. 

As we explained in our Useful AI tools section Blackboard Ultra has introduced an AI system to facilitate generating rubrics (link here).

With AI making rubric creation more accessible, their role can now extend beyond outlining assessment criteria for students. One practical use is streamlining the feedback process. In Blackboard Ultra, once you have generated a rubric table, you can start by selecting the text from one of the cells and use it as a template. Then, with the support of your favorite AI platform, you can rephrase it in multiple ways in no time. 

AI tools enable users to create lesson plans so you distribute evenly the contents of your session. This will also ensure you allocate time for for students' participation in a more controlled way.

The example shown in the video below we used the following prompt: 

You are teaching an undergraduate course on International Relations divided into 15 sessions, each of them about 80 minutes long; session 4 is devoted to the European Union, its origins, development, structure and legal and institutional instruments;

Students have only an introductory knowledge of International Relations, mainly their history and I World War and II World War and early Cold War phases.

Create a lesson plan for Session 4; break down all the issues we should be discussing during that 80-minute session, including a few breaks for students participation and Q&A. 

No sentences, only bullet points. 

While one straightforward approach is to restrict access to AI technology with the tools available at IE, such as SMOWL and RESPONDUS a more ambitious aim is to design assignments that inherently defy AI usage. Once accomplished, this would render the use of AI either inconsequential to the assignment's objectives or, if AI is permitted, ensure that its utilization enriches the educational experience of students, rather than impoverishing it.

Here are some valuable recommendations:

1. AI doesn't have to be utilized all the time, much like a delicious ingredient doesn't need to be present in every recipe. It's perfectly acceptable to limit its use in selected sessions and switch to 'offline mode' when advised.

2. Stay vigilant for inconsistencies in content, as chatbots may not be aware of the materials covered in your course. Additionally, AI responses often tend to sound somewhat mechanical and verbose, although this is gradually improving as technology advances.

3. Utilize exam review sessions to your advantage as a professor, you can make use of them too! In case of doubt, don't hesitate to call in a student to assess their ability to support the arguments presented in their work. It's a good practice to inform students in advance that this possibility exists.

4. If AI is to be incorporated proactively in your course, design cumulative work that allows you to track students' progress under your guidance. Additionally, consider formal presentations of findings to ensure that students truly absorb the knowledge, regardless of the sources they utilized along the way. 

The 2025 AI Index Report, published by Stanford University's Institute for Human-Centered AI, has been released. This Report offers a comprehensive, data-driven look at the state of artificial intelligence across education, policy, research, and public sentiment worldwide. With a focus on availability of Computer Science programs and other forms of training, Chapter 7 (pp.365-394), which is devoted to education, analyzes how AI is growing -and where it still falls short- on a global context.