Module 6: AI-Assisted Grading, Scoring, and Feedback - Worked Examples

Agentic Engineering for Teaching Professionals

These examples move from bounded AI assistance toward reusable teaching workflows. Adapt every prompt to the participant's actual subject, institution, learner level, evidence, and authorized tools rather than treating any template as universally correct.

Checking an Objective Response Set

Four one-point items have a verified key of B, D, A, C. A synthetic learner responds B, C, A, blank. The instructor's supplied rule gives zero for a wrong or blank response and uses no negative marking.

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Scoring with an Analytic Rubric

Apply this exercise to a specific teaching context and preserve the learner level, governing evidence, and institutional constraints that make the result valid.

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Evaluating a Short Essay

Apply this exercise to a specific teaching context and preserve the learner level, governing evidence, and institutional constraints that make the result valid.

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Scoring a Student Report

Apply this exercise to a specific teaching context and preserve the learner level, governing evidence, and institutional constraints that make the result valid.

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Evaluating a Mathematical Solution with Partial Credit

Apply this exercise to a specific teaching context and preserve the learner level, governing evidence, and institutional constraints that make the result valid.

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Recognizing an Alternative Correct Solution

Apply this exercise to a specific teaching context and preserve the learner level, governing evidence, and institutional constraints that make the result valid.

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Generating Rubric-Linked Feedback

Apply this exercise to a specific teaching context and preserve the learner level, governing evidence, and institutional constraints that make the result valid.

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Human Review of an AI-Proposed Grade

Apply this exercise to a specific teaching context and preserve the learner level, governing evidence, and institutional constraints that make the result valid.

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Hands-On Lab — Calibrate an AI-Assisted Grading Workflow

Activity goal: Use a rubric and synthetic/de-identified student samples to compare human scoring with an AI proposal, then define an educator-review protocol.

Required evidence and deliverables:

  • A verified rubric or expected-solution packet.
  • Three benchmark responses with educator criterion scores.
  • AI-proposed criterion scores with cited evidence.
  • A disagreement log and revised scoring instruction.
  • A final feedback sample and appeal/escalation rule.

Assessment rubric — 25 points

CriterionPointsRequired evidence
Grading context5Rubric, expectations, alternate methods, and scoring rules are complete.
Evidence linkage5Every proposed score is traceable to specific submission evidence.
Technical/criterion reasoning5Partial credit and alternative valid methods are handled defensibly.
Calibration5Human–AI disagreements are analyzed and used to improve the workflow.
Feedback and authority5Feedback is actionable and the educator remains the final decision maker.

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