Generative AI is challenging us to rethink a fundamental question in higher education: Are our assignments truly measuring student learning, or are they measuring something AI can now do for them? In this month’s NEean blog, board members reflect on this timely question and what it means for the future of assessment. From rethinking traditional assignments to exploring new ways to make student learning visible, their perspectives invite us to move beyond simply detecting AI use and toward designing assessments that deepen learning, critical thinking, and authentic demonstration of skills. 

Carina

AI challenges us to re-consider our learning outcomes for specific assignments to ensure that what we are asking students to demonstrate are outcomes that go beyond what can be easily produced by AI without including any evidence of the learning process on the part of the student.  Asking students to document and justify their reasoning or choices in creative production (even if the final product is AI-assisted) gives us authentic evidence of the process of inquiry and creation. We also need to bring students into the conversation about the learning outcomes to reinforce the value they bring to AI-assisted work products. 

Maura

AI challenges teaching and learning to move beyond the knowledge acquisition paradigm.  Learning should be assessed on what students are able to do in teaching environments that mirror the real world as much as possible.  Assessments at the assignment level should aim to ensure students are identifying appropriate and up-to-date information, able to think critically about other and/or missing information and frameworks that are also needed for any given inquiry, drawing accurate conclusions as they make meaning of easy-to-acquire information, making appropriate judgments and decisions, testing assumptions and determinations, and to apply appropriately to case studies and scenarios.  Recall the concept of ‘saturation’ as it applies to a review of the literature in a given field that a disciplinary expert achieves by years of study before starting to write a dissertation or before generating other new knowledge.  In short, AI jumpstarts a student’s knowledge in a given content area, essentially putting the student ‘on par’ with the disciplinary expert who has consumed all the related literature.  What does the dissertation writer do at that point?  Makes an argument that stems from her or his inquiry, gathers information and data, tests assumptions and biases, evaluates and analyzes results, identifies limitations, and thinks critically about implications for policy and practice.  AI challenges us to develop assignments that assess the accurate processing and judicious application of easy-to-acquire information to real world case studies, in other words, the implications for policy and practice. 

 

Denny

The question about AI and assignments doesn’t necessarily make me rethink how to do assessment. Indeed, for me, assessment has always been more about gaining a glimpse into the how a student is developing their thinking processes rather than their content mastery. To put it another way, the higher levels of Bloom’s taxonomy have always motivated my approaches to assessing student work and development. That is, I’ve always viewed the best assessment practices to be ones that reveal the students’ progress on how and why they think rather than what they think. The goal is to reveal their individual (and often unique)  knowledge production, not simply their mastery of content. To be sure, they need the content to demonstrate their proficiency in the analysis and synthesis stages of critical reasoning, but the best assessment protocols and the best assignments compel students to not simply regurgitate the content. Instead, they are compelled to employ that content, applying it in expected and unexpected ways, oftentimes creating new, authentic knowledge. So, if we are creating assignments that can be completed by agentic and/or generative AI, then we are only assessing content mastery, and as argued above, that only scratches the surface of what we really should be striving to assess. 

Jen 

We have a toolbox of wide-ranging direct and indirect measures that we can use to assess student learning and provide us with quantitative and qualitative data to understand the extent to which students have met the learning outcomes. Yet in many ways, AI throws a proverbial wrench into assessing learning. Students now have more tools that they can use to elude assessment, and when they feel stressed or overwhelmed, they might feel more compelled to take the fastest path and prompt AI to complete their work. From what I have read in higher education news reports, heard anecdotally from colleagues, and done myself, many of us have been using in-class blue book exams as a workaround. I have learned that blue book exams serve as a temporary fix, and they limit students’ time to process assessment questions, put together their responses, and revise their answers before handing in their work. We can use AI software as an opportunity to reconsider our assessment measures, make sure they align with our learning outcomes, and authentically assess learning. We can also add AI into our toolbox of assessment measures to teach content knowledge and reinforce the importance of durable skills, such as communication, information literacy, and critical thinking. AI presents us with an opportunity to leverage emerging technologies, double down on our efforts in teaching durable skills, and provide students with the skills to navigate their next pursuits.