{"id":38284,"date":"2026-04-21T09:00:00","date_gmt":"2026-04-21T07:00:00","guid":{"rendered":"https:\/\/www.uni.lu\/fhse-en\/?post_type=news&p=38284"},"modified":"2026-07-09T17:50:08","modified_gmt":"2026-07-09T15:50:08","slug":"bringing-ai-into-test-development-with-foundations-first","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/fhse-en\/news\/bringing-ai-into-test-development-with-foundations-first\/","title":{"rendered":"Bringing AI into test development \u2014 with foundations first"},"content":{"rendered":"\n
\n

Over the past years, LUCET has developed cognitive models that help explain how students solve assessment items in the annual school-monitoring programme \u00c9preuves standardis\u00e9es (\u00c9pStan)<\/a>, making item difficulty more predictable and potential cultural or linguistic bias easier to spot at the design stage. That work also laid the groundwork for what comes next: bringing AI into the \u00c9pStan test-development process in a way that holds up scientifically. <\/p>\n\n\n\n

That order matters. AI promises speed and scale, but on its own those gains stay shallow and can undercut the quality criteria that make an assessment trustworthy. With a cognitive-model foundation in place for parts of our item development, we can now start using AI operationally as a tool that augments our test developers rather than replacing their judgement. <\/p>\n\n\n\n

Preparing for the European AI Act<\/strong> <\/p>\n\n\n\n

Educational assessment is classified as high-risk<\/strong> under the European Union’s AI Act. To meet that standard, LUCET has built a governance framework and published a white paper, From Promise to Practice<\/em><\/a>, offering operational guidance for the responsible use of generative AI in large-scale assessment. It turns abstract regulatory principles, such as human oversight, transparency, fairness, data protection, and AI literacy, into concrete commitments tied to the psychometric quality-assurance practices we already follow. An internal LUCET strategy then translates this into day-to-day rules for the \u00c9pStan test-development workflow with special consideration of avoiding AI-based copyright infringement. Together they put Luxembourg among the early movers in Europe working to make the AI Act practicable for education, rather than merely compliant on paper. <\/p>\n\n\n\n

AI as a principled extension, not a shortcut<\/strong> <\/p>\n\n\n\n

These cognitive foundations are also what make AI integration scientifically defensible. In a study published in Large-scale Assessments in Education<\/em><\/a>, LUCET researchers tested whether GPT-4 could produce German reading-comprehension texts good enough for national assessment. Crucially, the texts weren’t generated freely: they were built from a Text Analysis Cognitive Model (TACM) <\/strong>\u2014 a structured template, defined by a subject-matter expert, that fixes the cognitive and linguistic features a text must have. <\/p>\n\n\n

\n \n\"Text <\/figure>\n\n\n

In a blind review, 89 readers rated human- and AI-written passages on readability, correctness, coherence, engagement, and fitness for assessment without being told which was which, and could not reliably tell them apart. But the results were not uniform: giving the model an example text to work from yielded strong informative<\/em> passages, while human authors kept the edge for narrative<\/em> texts, where coherence and engagement matter most. <\/p>\n\n\n\n

A parallel SWOT analysis with our own test developers named the benefits, such as efficient first drafts, adapting content across difficulty levels, easing multilingual demands, alongside the risks that keep a human in the loop: factual errors, cultural and pragmatic bias, and the pull to trust a fluent-sounding text without scrutinising it. <\/p>\n\n\n\n

That is the approach in one word: augmentation and oversight. AI extends expert judgement and draws its quality from the cognitive foundations underneath it. The research continues, and operational use in the \u00c9pStan workflow is now beginning. <\/p>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":301,"featured_media":38360,"template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false},"news-category":[4],"news-topic":[10,12],"organisation":[147,170],"authorship":[301],"acf":[],"yoast_head":"\nBringing AI into test development \u2014 with foundations first - 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