Module 9: Responsible AI, Academic Integrity, Privacy, and Governance - 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.

Redacting Student Information

The stated task is to summarize class-wide performance by assessment item. A synthetic source contains names, student numbers, email addresses, item scores, health notes, and free-text comments. Decide what the authorized analysis actually needs.

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Auditing an Unsafe Grading Workflow

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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Responding to a Fabricated Citation

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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Reviewing a Copyright-Sensitive Resource

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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Handling Suspected AI-Assisted Student Work

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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Redesigning an Assessment for Authenticity

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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Creating an AI-Use Disclosure Statement

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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Designing a Safe Human-in-the-Loop Workflow

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 — Audit an Educational AI Workflow for Governance Risk

Activity goal: Audit one proposed teaching workflow for privacy, integrity, rights, bias, security, disclosure, and human-authority risks, then redesign its controls.

Required evidence and deliverables:

  • Purpose and data-classification statement.
  • Risk register covering privacy, integrity, copyright, bias/accessibility, security, and authority.
  • Permitted/prohibited AI-use or disclosure statement.
  • Revised workflow with control and approval gates.
  • Incident/escalation plan for a material AI error.

Assessment rubric — 25 points

CriterionPointsRequired evidence
Purpose and data handling5The workflow uses minimum necessary data and identifies governing authorization.
Integrity and rights5Academic integrity, copyright, attribution, and provenance are addressed.
Fairness and accessibility5Criteria and access barriers are reviewed rather than relying on generic bias language.
Security and permissions5Untrusted content, prompt injection, and excessive tool access are controlled.
Governance and transparency5Disclosure, human review, escalation, and incident handling are actionable.

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