{"id":43791,"date":"2025-10-31T14:40:13","date_gmt":"2025-10-31T13:40:13","guid":{"rendered":"https:\/\/media-and-learning.eu\/?p=43791"},"modified":"2026-04-14T11:16:30","modified_gmt":"2026-04-14T09:16:30","slug":"event-report-assessment-in-the-age-of-generative-ai","status":"publish","type":"post","link":"https:\/\/media-and-learning.eu\/subject\/artificial-intelligence\/event-report-assessment-in-the-age-of-generative-ai\/","title":{"rendered":"Event report: assessment in the age of generative AI"},"content":{"rendered":"\n

by Kamakshi Rajagopal<\/strong>, Media and Learning Association, Belgium. <\/p>\n\n\n\n

As Generative AI takes a foothold in our technology systems, assessment of learning has become a critical point in higher education institutions worldwide. MLA\u2019s online webinar, “Assessment in the Age of Generative AI,” brought together leading educators and technologists to explore this paradigm shift. This article looks at the main takeaways of the event for redefining assessment practices in the modern classroom.<\/p>\n\n\n\n

The event was well-attended with about 90participants, about half of whom work at institutions with institutional policies on GenAI use.<\/p>\n\n\n\n

Principles for assessment with AI in higher education<\/strong><\/p>\n\n\n\n

Monika Theron (MLA) presented an overview of the TaLAI \u2013 Teaching and Learning with Artificial Intelligence in Higher Education project<\/a>, which aims to integrate AI ethically into higher education assessment practices. The outcomes of the project aim to empower educators and students in developing AI literacy and competence. Monika sketched how the project will develop policy guidelines and create a digital platform and MOOC for educators, available in 2026. She outlined seven core principles for using AI in assessment, including (i) using a combination of multiple assessment strategies suited for the learning goals, and aligned with the policy context, (ii) requiring human oversight in grading, (iii) critically reviewing quality control of AI-generated content, (iv) aligning GenAI use with learning outcomes and objectives, (v) revising policy regularly to stay up-to-date with new technologies and insights, (iv) communicating transparently on grading criteria, and (vi) being transparent towards students the use of Gen AI in assessment practice. The policy recommendation document can be downloaded here.<\/a><\/p>\n\n\n\n

Indicating that the project is also developing more detailed guidelines, she emphasised that these guidelines need to be living documents and encouraged participants to share their institutional policies and approaches for potential collaboration. This can be done by sending an email to info@media-and-learning.eu<\/a><\/p>\n\n\n\n

AI in educational assessment fairness<\/strong><\/p>\n\n\n\n

Josette Daemen from Leiden University presented her research on fair use of AI in educational assessments, asking how GenAI use can be regulated to promote fairness in assessment practices. She proposed three prerequisites for fair GenAI use in assessment, maintaining that assessment should be valid<\/em>, transparent<\/em> and authentic<\/em>. Using a Design-Performance-Evaluation process for assessment, she went on to describe how the three prerequisites can be made concrete in each step.<\/p>\n\n\n\n

You can find her presentation and a video recording of her talk here<\/a>.<\/p>\n\n\n\n

Teacher support for implementing ethical GenAI use<\/strong><\/p>\n\n\n\n

Chrissi Nerantzi, John Palfreyman and Maria Pavlopolou (University of Leeds) reflected together in a panel discussion on their use of a teacher support instrument for AI usage in higher education assessments at Leeds University. The traffic light system<\/a>[KR1]<\/a>  presents three categories to navigate GenAI use: (i) Red<\/em> (Do not use GenAI at all)<\/em>, (ii) Amber (GenAI is allowed to support you, but only in the way the teacher allows it) <\/em>and (iii) Green<\/em> (Using GenAI is an expected part of the assignment)<\/em>. The three panellists started their reflective conversation from their mutual student-teacher relationship (as John tutored Chrissi, and Chrissi tutors Maria). The first part of the discussion centred on their individual experiences in working with the traffic light system. Although Maria indicated the many benefits of having such a system to create transparency and guidance, reducing uncertainty on academic integrity, John and Chrissi highlighted the downside as the tool and guidelines are complex, and appear simple and comprehensive, but are ripe for misinterpretation. The second part of the discussion centred on the Amber categories, where ambiguity in use came to the fore again. The panellists emphasised the need to consider assessment as a part of the whole learning experience, and also pointed to the importance of modelling ethical AI use to ensure learners develop empowering habits in GenAI-enhanced learning. Requiring students to document their AI use was named as a good starting point to get them to reflect on their learning behaviours. The last part of the discussion looked at Green assignments, where AI use is mandated. Students found this a breath of fresh air, but the panellists also reflected that such learning design is demanding on the learner, as it requires more critical thinking, creativity, balanced with academic rigour. Student reflection on GenAI use is a must.<\/p>\n\n\n\n

Audience comments<\/strong><\/p>\n\n\n\n

The three presentations lead to many interesting comments on the topic of AI and assessment. A first important point related to the notion of fairness in providing student access to AI technology. Some participants indicated that institutions may choose to avoid AI use, as they are unable to ascertain fair access. Also, some questioned if university-made technology will be realistically able to compete with commercial technologies. A second point of interest centred on the principle of alignment, where AI use needs to be aligned with learning goals and assessment. However, some indicated that thoughtful alignment is often where things fall short, as institutions rush to integrate GenAI in their teaching. Although many liked the ease of use of the traffic light systems, many participants wondered how GenAI use by students can be effectively monitored under the Red assignments. <\/p>\n\n\n\n

You can see the recording of this event together with the presentations of the speaker on the event page.<\/a><\/p>\n\n\n\n

<\/a><\/p>\n\n\n\n

\"\"<\/figure>
\nKamakshi Rajagopal<\/a>, Media and Learning Association, Belgium<\/p>\n<\/div><\/div>\n\n\n\n
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The TaLAI (Teaching and Learning with Artificial Intelligence in Higher Education) project\u2019s primary motivation is to pioneer innovative pedagogical approaches that ethically incorporate AI into higher education, empowering both educators and students to develop AI literacy and competence.<\/p>\n<\/div><\/div>\n\n\n\n

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Funded by the European Union. Views and opinions expressed are solely those of the author(s) and do not necessarily reflect those of the European Union or the DAAD National Agency. Neither the European Union nor the DAAD National Agency can be held responsible for them. Project Number: 2023-1-DE01-KA220-HED-000153155<\/em><\/p>\n<\/div><\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

by Kamakshi Rajagopal, Media and Learning Association, Belgium. As Generative AI takes a foothold in our technology systems, assessment of learning has become a critical point in higher education institutions worldwide. MLA\u2019s online webinar, “Assessment in the Age of Generative AI,” brought together leading educators and technologists to explore this paradigm shift. This article looks […]<\/p>\n","protected":false},"author":7,"featured_media":43793,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_mo_disable_npp":"","footnotes":""},"categories":[362,4,275],"tags":[],"class_list":["post-43791","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-featured-articles","category-higher-education"],"featured_image_src":"https:\/\/media-and-learning.eu\/files\/2025\/10\/education-assessment-in-the-presence-of-AI-scaled.jpg","author_info":{"display_name":"Shirin Izadpanah","author_link":"https:\/\/media-and-learning.eu\/author\/shirin-izadpanah\/"},"_links":{"self":[{"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/posts\/43791","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/comments?post=43791"}],"version-history":[{"count":10,"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/posts\/43791\/revisions"}],"predecessor-version":[{"id":43859,"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/posts\/43791\/revisions\/43859"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/media\/43793"}],"wp:attachment":[{"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/media?parent=43791"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/categories?post=43791"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/media-and-learning.eu\/api-json\/wp\/v2\/tags?post=43791"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}