Collaborative Assessment Models

I have never seen such drastic changes in university education as what has happened during the past two years because of generative AI technologies. Especially student assessment is now a completely different activity than what it used to be. I am starting to think that this requires a complete paradigm change in student assessments. We should not merely measure individual student capabilities but start evaluating student-AI teams and the result of the collaboration between AIs and students. Traditional university assessments are designed to measure individual student knowledge, skills, and critical thinking. Exams, essays, and projects typically emphasize personal effort and originality, aiming to cultivate independent thinkers. While this model has worked well for centuries, it now feels increasingly disconnected from the realities of the digital age. AI tools like ChatGPT, DALL-E, and others can produce sophisticated outputs, ranging from code and essays to data analysis and creative designs. Denying students access to these tools in assessments not only misrepresents their future work environments but also hinders their ability to develop critical skills for the AI-integrated workplace. The workplace of tomorrow will not reward individuals who can outperform AI but those who can work with AI to achieve exceptional outcomes. Universities must therefore adapt assessments to evaluate how well students integrate AI tools into their workflow to address complex, real-world problems, how critically they evaluate AI outputs for accuracy and bias, and how creatively and effectively they use AI to enhance their projects and generate novel solutions. Furthermore, students’ understanding of ethical considerations, including data privacy, transparency, and responsible innovation, must also become a focal point of assessment. Transitioning to a model that evaluates collaboration between students and AI requires innovative approaches. Assignments could explicitly require AI assistance, such as asking marketing students to develop campaigns with the help of AI tools, assess their viability, and justify their strategic decisions. Grading systems might prioritize the process over the final product, evaluating how students choose and use AI tools, iterate based on feedback, and address errors in AI-generated outputs. Open-book exams could allow AI use, with students evaluated on their ability to interpret, critique, and expand upon AI-generated content. Simulated workplace scenarios, where students work as part of a team with AI, could also become a powerful tool to measure real-world readiness. However, this transition is not without its challenges. See the comment section for more. Have you already started to assess the results of student-AI collaboration or do you still consider the individual capabilities of students as the main thing to assess in university education? #AI #education #assessment #grading #capabilities

Participatory assessments are a transformative approach to understanding the nuanced needs, priorities, and vulnerabilities of communities. This Guidance on Participatory Assessments, developed by experts at Catholic Relief Services, presents a robust framework for integrating participatory methods into the project design and assessment process. By centering the voices of the most marginalized and vulnerable households, this guide ensures that project designs are both relevant and responsive to the diverse realities of the people they serve. The document outlines a step-by-step process, from meticulous assessment planning and the application of participatory tools—such as transect walks, mapping, and focus group discussions—to the thorough analysis of findings using structured methodologies like problem trees and comparative matrices. It emphasizes flexibility, inclusivity, and ethical considerations, equipping practitioners to navigate complex community dynamics and foster meaningful engagement. Tailored for humanitarian professionals and development practitioners, this resource empowers its audience to conduct assessments that go beyond data collection, creating opportunities for collaboration, empowerment, and sustained community-led change. By adopting these approaches, practitioners can craft interventions that are not only impactful but also grounded in the lived experiences and aspirations of the communities they aim to uplift.

If you think differently about assessment, keep reading. Curious how students and teachers can be partners in assessment, keep reading. Assessment has long been high stakes. Today, timed exams, in-the-moment oral assessment, with the threat of being suspected of using AI only intensify the emotional forces students navigate in higher ed. That is why I am thrilled to read this new paper by Gerald Decelles III (Norway) and Catherine Bovill (Scotland) in the latest issue of the International Journal of Students as Partners (IJSaP). Their work invites us to think differently about assessment anxiety, not as an individual deficit but as a shared condition shaped through relationships and design. They show how co-creation shifts responsibility from remediation by the student to what can be done in collaboration with teachers. Their mixed-methods study highlights four interconnected insights: 1. the influence of assessment type, 2. the importance of knowing the assessor, 3. how co-creation supports students to develop agency over assessment, and 4.how co-creating assessment can reduce anxiety while building trust, respect, community, and belonging. “Co-creation may also move the responsibility for remediation of anxiety from what the student needs to do to what can be done in collaboration with the teacher.” This paper is one of several thoughtful contributions in the new issue, spanning research articles, case studies, and reflective essays. Together, they remind us that teaching, learning, and assessment are never just technical. It is deeply relational. Links in the comments.

Rethinking Assessment in the Age of AI If artificial intelligence can complete an assessment as well as (or better than) our students, it’s time to ask: Are we assessing the right things? The rise of AI tools has forced us to reconsider what authentic learning and assessment should look like. If traditional assessments—essays, multiple-choice tests, or basic problem-solving—can be easily completed by AI, then we need to shift our focus toward process, creativity, and deeper engagement. At Nanjing International School, we’ve always felt this was the needed approach. Back in 2023 Kasson Bratton created the P.R.O.M.P.T.S. AI #Assessment model to guide assessments that embrace #AI as a reality while maintaining academic integrity and meaningful learning. 🔹 Prioritize Process – Frequent milestones, co-created timelines, and formative feedback 🔹 Remain Relevant – Leverage recent resources, student interests, and lived experiences 🔹 Offer Alternatives – Use diverse formats like video, podcasts, interviews, and design projects 🔹 Make It Personal – Conferring routines, student-led task revisions, and collaborative assessments 🔹 Plan to Portfolio – Shift from high-stakes exams to ongoing, varied demonstrations of learning 🔹 Try It Out – Use AI to test assessments, spark creativity, and refine task design 🔹 Source It – Engage students in discussions about AI’s role and responsible use in learning This isn’t about banning AI; it’s about designing assessments that AI can’t do for students—only with them. By focusing on #criticalthinking, #creativity, and #realworld #application, we ensure that #learning remains #relevant in a world where AI is here to stay. How is your school rethinking assessment in the AI era? #AIinEducation #Assessment #EdTech #ArtificialIntelligence #FutureOfLearning #SchoolLeadership #InnovationInEducation