Why it is often not suitable to use Artificial Intelligence (AI) to do assessments?

Question

BlackTom AI · Accepted Answer

Question restatement: Why is it often not suitable to use Artificial Intelligence (AI) to do assessments?

Option 1: Unable to check provenance of work
This concern highlights that AI-generated outputs may lack transparent authorship trails or verifiable source origins. If the system cannot reliably show where ideas or data came from, teachers cannot verify integrity or assess potential plagiarism, making trust in the assessment questionable.

Option 2: Have to individually check reference provided to ensure accuracy
AI can generate citations or references, but these may be incorrect, fabricated, or outdated. Instructors must verify each reference to prevent the spread of misinformation and to ensure the assessment relies on trustworthy sources. This makes automated checks time-consuming and undermines the efficiency goal of using AI in assessments.

Option 3: AI isn’t peer reviewed
Many AI models and their outputs aren’t subjected to formal peer review in the same way as scholarly work. This means there can be unrecognized biases, errors, or unsupported assertions in AI-generated content, which is a risk when used for evaluative or high-stakes assessment tasks.

Option 4: All answers
If each of the previous three concerns is valid, then it follows that a comprehensive statement like All answers captures the overall limitation: provenance concerns, need for manual verification of references, and the lack of formal peer review all contribute to the unsuitability of relying solely on AI for assessments. Recognizing multiple overlapping issues reinforces why AI is not a drop-in solution for evaluating student work.

Note on reasoning approach: The analysis above explores common critiques of AI use in assessment—focusing on traceability, verification burden, and reliability due to non-peer-reviewed origins. Understanding these points helps educators weigh the trade-offs between convenience and rigor in assessment design.

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