Shift your approach from enforcement to assessment design
How to keep assessments focused on learning outcomes in the age of GenAI.
Depending on your context, you may ask your students not to use GenAI or ask them to disclose when they have used it. Keep in mind that these practices depend on students’ willingness to adhere to these directives when the rules around this kind of technology are not enforceable (Corbin et al, 2025).
Another way to approach assessments in the time of GenAI is to design effective assignments that focus on what you want students to learn in the way of skills, knowledge and values, be these outcomes related to AI or not. Pedagogically sound assessments are not designed primarily to prevent academic misconduct but rather to gauge whether learning has occurred and to what extent.
Example
Suppose you want your students to develop critical thinking and research skills, you might design an assessment that is scaffolded, or submitted in stages, such as:
- an outline or brainstorming activity
- an annotated bibliography
- research notes and drafts instead of only a final research paper
Students can also be asked to reflect on their learning over the course of the different stages in a journal or a cover page that precedes each submission.
In this example, since you value process over product, the weighting of a high-stakes final product, such as a paper or project, would be distributed across the stages (i.e., developing research questions and finding appropriate sources). So, instead of the final product (the paper) being worth 30%, the three distinct moments of the process (research questions, appropriate source search, and paper) can be worth 10% each.
Given that there will be more work submitted, consider using rubrics for marking and limit the feedback to one or two elements.
Helpful frameworks
To help you in the process of designing or modifying your assessment, there are frameworks that assist in discerning what skills GenAI is competent in and how to develop your assignments around the human-centred learning you’d like to see in your students:
- Oregon State University has developed Bloom’s Taxonomy Revisited, a table that details decidedly human skills and shows how GenAI tools can supplement the learning process. The table can serve as a reference for evaluating and considering changes to learning outcomes or assignments.
- The AI Assessment Scale (AIAS) is a practical framework to guide the appropriate and ethical use of GenAI in assessment design. In describing progressive levels of integration, it allows for more purposeful evidence-based decision making.
The AI Assessment Scale
1. No AI
The assessment is completed entirely without AI assistance in a controlled environment, ensuring that students rely solely on their existing knowledge, understanding, and skills
You must not use AI at any point during the assessment. You must demonstrate your core skills and knowledge.
2. AI Planning
AI may be used for pre-task activities such as brainstorming, outlining and initial research. This level focuses on the effective use of AI for planning, synthesis, and ideation, but assessments should emphasise the ability to develop and refine these ideas independently.
You may use AI for planning, idea development, and research. Your final submission should show how you have developed and refined these ideas.
3. AI Collaboration
AI may be used to help complete the task, including idea generation, drafting, feedback, and refinement. Students should critically evaluate and modify the AI suggested outputs, demonstrating their understanding.
You may use AI to assist with specific tasks such as drafting text, refining and evaluating your work. You must critically evaluate and modify any AI-generated content you use.
4. Full AI
AI may be used to complete any elements of the task, with students directing AI to achieve the assessment goals. Assessments at this level may also require engagement with AI to achieve goals and solve problems.
You may use AI extensively throughout your work either as you wish, or as specifically directed in your assessment. Focus on directing AI to achieve your goals while demonstrating your critical thinking.
5. AI Exploration
AI is used creatively to enhance problem-solving, generate novel insights, or develop innovative solutions to solve problems. Students and educators co-design assessments to explore unique AI applications within the field of study.
You should use AI creatively to solve the task, potentially co-designing new approaches with your instructor.
The AI Assessment Scale (AIAS) was developed by Mike Perkins, Leon Furze, Jasper Roe, and Jason MacVaugh. First introduced in 2023 and updated in Version 2 (2024), the Scale provides a nuanced framework for integrating AI into educational assessments.
Disclosure notice: This resource was developed and written by a human without Generative AI assistance and was revised based on peer feedback. Microsoft Copilot was used in the formatting of the references, and its accuracy was checked.