Assessing Students or Their Ai Teammates? Case Studies in the Gai Era
Year: 2026
Type: conference-paper
Abstract: The widespread adoption of Generative AI (GAI) since 2022 has fundamentally challenged traditional assessment methods in engineering education, exposing the weakness in assuming submitted artefacts reliably represent student thinking. This article's purpose is to provide concrete examples of how GAI has changed formative and summative assessment in engineering courses. Through comparative case studies across five CDIO institutions in Norway, Denmark, Finland, France, and Singapore, we examine GAI implementations involving over 400 students. All cases are characterized using the Traffic Light Model by Arene, which categorizes AI use as required (blue), prohibited (red), reported (yellow), or allowed (green). Our findings reveal that effective GAI integration requires thoughtful assessment redesign-structured implementations with clear pedagogical frameworks demonstrated positive outcomes, whilst less structured approaches can lead to student overreliance and reduced learning. The evidence indicates thoughtful assessment redesign grounded in constructive alignment is required, where AI use decisions are tied explicitly to learning outcomes. Effective integration demands shifting from product-based to processbased assessment methods, emphasizing evaluation of student reasoning, judgement, and capacity to work responsibly with AI.
Source: HAL
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7198762694