Odisee, a prominent university of applied sciences in Belgium, has announced it will no longer require traditional final dissertations for many of its programmes. The decision comes as artificial intelligence tools increasingly influence how students complete academic work, prompting a broader rethink of assessment methods across European higher education.

Background on Odisee and the Shift in Assessment Practices

Odisee operates multiple campuses in Flanders and focuses on applied sciences and professional training. Its move away from conventional written dissertations reflects growing concerns that generative AI can produce entire papers with minimal student input. University representatives noted that traditional formats make it difficult to verify authentic student effort when AI assistance is readily available.

Why Traditional Dissertations Are Being Phased Out

With AI capable of generating coherent, well-structured text on demand, educators at Odisee argue that the classic dissertation no longer reliably demonstrates individual learning or critical thinking. The institution is exploring alternative formats that emphasise oral presentations, project-based work, and live demonstrations of skills. These approaches aim to maintain academic rigour while adapting to technological realities.

AI's Impact on European Higher Education Assessment

Across Europe, universities face similar challenges. Regulatory bodies and ministries in countries like Belgium, the Netherlands, and Germany are reviewing guidelines on AI use in student work. Odisee's decision highlights a trend toward assessments that are harder for AI to replicate fully, such as those requiring real-time interaction or practical application of knowledge.

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Stakeholder Perspectives on the Change

Faculty members at Odisee have expressed support for the shift, citing the need to focus on competencies that AI cannot easily replicate. Students have mixed reactions, with some welcoming more interactive evaluation methods and others concerned about increased workload in alternative formats. Administrators emphasise that the goal is to preserve the value of degrees in an era of rapid technological change.

Alternative Assessment Methods Being Adopted

Odisee is piloting portfolio assessments, collaborative projects, and viva-style oral examinations. These methods encourage students to demonstrate understanding through discussion and application rather than solely through written output. Similar experiments are underway at other European institutions seeking to balance innovation with integrity.

Challenges and Opportunities in the Transition

Implementing new assessment models requires investment in training for staff and clear communication with students. Odisee is working with educational technology partners to develop tools that support authentic assessment while deterring misuse of AI. The transition also opens opportunities to align curricula more closely with workplace demands for problem-solving and adaptability.

Broader Implications for European Universities

Odisee's approach may influence policy discussions at the European level, including within frameworks supported by the European Higher Education Area. Other universities of applied sciences are monitoring the outcomes closely, considering how to update their own regulations on final projects and theses.

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Future Outlook for AI in Higher Education

As AI capabilities continue to evolve, European higher education institutions will likely accelerate experimentation with hybrid assessment models. Odisee's experience provides a practical case study for balancing technological adaptation with the core mission of fostering genuine student learning and professional readiness.

Practical Steps for Institutions Considering Similar Changes

Universities looking to revise assessment practices can begin by reviewing existing AI policies, consulting with faculty and student representatives, and piloting small-scale changes before full implementation. Collaboration across institutions can help share best practices and reduce duplication of effort.