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Discussing How AI Should Support Assessment

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How should AI support assessment without replacing academic judgement?

¿Cómo debería apoyar la IA la evaluación sin reemplazar el juicio académico?

buena respuesta:

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.

La IA debería apoyar la evaluación encargándose de tareas limitadas y auditables, en lugar de tomar decisiones académicas finales. Podría ayudar a comprobar si la retroalimentación es coherente, identificar patrones en un gran conjunto de trabajos o señalar casos que necesitan una revisión humana más detallada. Sin embargo, el juicio final debe seguir en manos de docentes calificados, porque evaluar no es solo reconocer patrones. Implica interpretar la intención, la originalidad, los criterios disciplinarios y la respuesta del estudiante a una tarea específica. Por ejemplo, la IA podría notar que dos comentarios sobre la evidencia son inconsistentes, pero el docente tiene que decidir si el argumento del estudiante realmente resulta convincente. Por eso, el papel útil de la IA es asesorar y ser transparente. Puede fortalecer el juicio humano, pero no debe reemplazar la responsabilidad sobre los criterios ni sobre los resultados.

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

buena respuesta:

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?

buena respuesta:

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?

buena respuesta:

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.