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Discussing How AI Should Support Assessment

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Cuộc hội thoại

How should AI support assessment without replacing academic judgement?

AI nên hỗ trợ việc đánh giá như thế nào mà không thay thế phán đoán học thuật?

Câu trả lời hay:

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.

AI nên hỗ trợ việc đánh giá bằng cách xử lý những nhiệm vụ giới hạn, có thể kiểm tra được, thay vì đưa ra các quyết định học thuật cuối cùng. Nó có thể giúp xem phản hồi có nhất quán không, nhận ra các mẫu chung trong một tập lớn bài làm hoặc đánh dấu những trường hợp cần giáo viên xem xét kỹ hơn. Tuy nhiên, quyền quyết định cuối cùng vẫn nên thuộc về giáo viên có chuyên môn, vì đánh giá không chỉ là nhận diện mẫu. Việc này còn bao gồm diễn giải ý định, tính độc đáo, các chuẩn mực chuyên môn và cách sinh viên phản hồi với một nhiệm vụ cụ thể. Ví dụ, AI có thể nhận ra rằng hai nhận xét về bằng chứng là không nhất quán, nhưng giáo viên mới là người phải quyết định liệu lập luận của sinh viên có thực sự thuyết phục hay không. Vì vậy, vai trò hữu ích của AI là mang tính tư vấn và minh bạch. Nó có thể củng cố phán đoán của con người, nhưng không nên thay thế trách nhiệm đối với các chuẩn mực hay kết quả.

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

Câu trả lời hay:

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?

Câu trả lời hay:

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?

Câu trả lời hay:

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.