Why it happens

AI grading models produce feedback based on what they are told to look for. When grading instructions are vague — or entirely absent — the model falls back on generic academic language. Phrases like "good effort," "consider developing this further," or "your answer addresses the question" appear when the AI has no specific criteria to anchor its comments.

The root cause is almost always one of these:

• No rubric criteria in the instructions (just "grade this on a scale of 1–5").
• Instructions describe the assignment but do not describe what a good or poor answer looks like.
• Point values are missing, so the AI cannot anchor partial-credit reasoning.
• No guidance on feedback tone, length, or specificity.

Instruction fixes

Rewrite your grading instructions in the Grading Instructions panel to include:

1. Explicit criteria for full credit: Describe exactly what a complete, correct answer includes. Do not just say "mentions the main idea" — say "identifies the main idea and connects it to at least one supporting detail from the text."
2. Partial credit definition: State what earns partial credit. "Mentions the main idea but provides no supporting detail earns 1 of 2 points."
3. No-credit definition: Briefly describe responses that earn zero points. "Off-topic responses, restated question only, or blank submissions."
4. Feedback tone: Add a tone directive. "Write feedback in a direct, encouraging tone. Keep comments to 2–3 sentences."

See: Grading Instructions Panel: Write Better Prompts.

Model and settings

If instructions are strong but feedback is still shallow, check:

• Model capability: More capable models in the AI Engine panel produce more nuanced output. If you are using a small or faster model primarily for speed, feedback quality may be reduced. Try a higher-tier model for assignment types that need richer comments.
• Grading persona: The persona setting in the Grading Settings panel affects tone and style. Experiment with different persona options to find the match for your classroom voice.
• Temperature / creativity settings: If available, slightly increasing output variety can break repetitive feedback patterns.

Before and after

Before (vague instruction): "Grade this short-response question on photosynthesis out of 3 points."

After (specific instruction): "Grade this short-response question on photosynthesis out of 3 points. Full credit (3): identifies light, water, and CO2 as inputs AND glucose and oxygen as outputs. Partial credit (2): identifies all inputs but misses one output, or vice versa. Partial credit (1): identifies at most 2 correct components with no clear input/output distinction. Zero: off-topic or blank. Write feedback in 2 sentences. First sentence states what the student did well or missed. Second sentence gives one specific improvement."

The "after" version gives the AI a complete scoring model and a feedback format to follow, resulting in specific, actionable comments for each student.

FAQ

Why does AI grading feedback sound vague or generic?

Generic feedback usually means the grading instructions did not give the AI enough specifics. Without explicit rubric criteria, point values, and example responses, the AI falls back on generalized academic language. Write more specific instructions that describe exactly what earns full, partial, and no credit.

How do I write AI grading instructions that produce specific feedback?

Include: (1) the exact criteria for full credit, (2) what constitutes a partial response, (3) a concrete example of what a strong answer includes, and (4) the tone or voice you want in feedback comments.

Does the AI model choice affect feedback quality?

Yes, but instructions quality is the bigger factor. Even strong models produce generic feedback when given vague instructions. Fix instructions first, then consider upgrading the model if shallowness persists.