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Discussing How AI Should Support Assessment

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How should AI support assessment without replacing academic judgement?

Comment l’IA devrait-elle soutenir l’évaluation sans remplacer le jugement académique ?

Bonne réponse:

AI should support assessment by handling limited, auditable tasks rather than making final academic decisions. It might help check whether feedback is consistent, identify patterns across a large set of scripts or flag cases that need closer human review. However, the final judgment should remain with qualified teachers, because assessment is not just pattern recognition. It involves interpreting intention, originality, disciplinary standards and the student's response to a particular task. For example, AI might notice that two comments about evidence are inconsistent, but a teacher has to decide whether the student's argument is actually persuasive. The useful role for AI is therefore advisory and transparent. It can strengthen human judgment, but it should not replace responsibility for standards or outcomes.

L’IA devrait soutenir l’évaluation en prenant en charge des tâches limitées et vérifiables, plutôt qu’en prenant les décisions académiques finales. Elle peut aider à vérifier si les retours sont cohérents, à repérer des tendances dans un grand nombre de copies ou à signaler les cas qui demandent un examen humain plus approfondi. En revanche, le jugement final doit rester entre les mains d’enseignants qualifiés, car évaluer ne consiste pas seulement à reconnaître des schémas. Cela implique d’interpréter l’intention, l’originalité, les critères disciplinaires et la réponse de l’étudiant à une tâche précise. Par exemple, l’IA peut remarquer que deux commentaires sur les preuves ne sont pas cohérents, mais c’est à l’enseignant de décider si l’argument de l’étudiant est vraiment convaincant. Le rôle utile de l’IA est donc consultatif et transparent. Elle peut renforcer le jugement humain, mais elle ne doit pas remplacer la responsabilité liée aux critères ou aux résultats.

What is the main risk if AI becomes part of grading or feedback?

Bonne réponse:

The central risk is that students may receive feedback that sounds authoritative but lacks real understanding. AI can produce polished comments, but polish is not the same as insight. For example, a student might be told to "develop the argument further" even when the real problem is that they misunderstood the evidence or answered a slightly different question. Because the comment sounds professional, the student may accept it without receiving useful guidance. This is especially dangerous in high-stakes assessment, where feedback affects confidence and future choices. I would not say human feedback is always excellent, but weak human feedback can at least be questioned through a responsible person. AI feedback risks sounding final while being shallow, generic or misaligned with the task.

How would you answer the argument that AI makes assessment more efficient and consistent?

Bonne réponse:

Efficiency and consistency are real advantages, especially in large courses where teachers are under pressure and students wait too long for feedback. AI may help identify uneven marking, summarize common problems or reduce repetitive administrative work. I would not dismiss those gains. However, consistency is not enough if the system consistently misses nuance. For example, an AI tool might apply the same surface-level comment to many essays, while failing to notice that each student's problem is different. That is efficient, but not educationally rich. The question should be whether AI saves time in ways that improve human feedback, not whether it simply reduces labor. If efficiency weakens interpretation, the university has gained speed at the expense of judgment and learning.

What should universities avoid doing as AI assessment tools become more powerful?

Bonne réponse:

Universities should avoid treating AI assessment as innovation by default. A powerful tool can still weaken education if it reduces feedback to prediction, classification or risk management. For example, a system that quickly labels students as at risk may help staff prioritize support, but it may also narrow how teachers see those students. Similarly, automated feedback may look modern while making assessment less personal and less intellectually demanding. The long-term danger is that universities start designing assessment around what tools can measure, rather than around what students need to learn. AI should be adopted only when it strengthens educational purposes that the institution has already defined. Novelty is not a sufficient reason to change how students are judged or supported.