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Skills application

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Building a compliance plan​

In this skills application, you’ll step into the role of a compliance team for LearnSmart, an EdTech company using machine learning to personalise student learning paths. You’ll analyse how regulations and governance frameworks apply to this system, then design key compliance artifacts including a risk matrix, audit checklist, and escalation workflow.

Learning this skill supports more trustworthy and auditable ML practices by helping teams embed compliance from the start, clarify accountability, and align system design with legal and ethical standards.

Scenario: LearnSmart compliance case​

LearnSmart is an EdTech platform that uses machine learning to generate personalised learning paths for students. The system analyses quiz performance, learning preferences, and engagement data to adapt content and suggest next steps in each student’s journey. It also integrates with third-party APIs for educational content, progress analytics, and behavior tracking.

As part of the internal compliance team, your job is to ensure that LearnSmart’s ML system aligns with key data privacy regulations and governance standards.Recent concerns from educational partners have highlightedrisks around fairness in recommendations, lack of clarity about how student data is used, and potential gaps in audit readiness.

Your team must develop a compliance plan that proactively addresses these issues and prepares LearnSmart for external review.

Activity instructions​

Review the LearnSmart compliance case​

With your group, review the LearnSmart ML system compliance case. Identify two applicable regulations and explain how each shapes data handling, transparency, or ongoing monitoring.Choose a governance framework and describe how it supports responsible ML practice in this EdTech context.

Develop your compliance plan​

Collaborate with your group to a develop a compliance plan for the LearnSmart system. Your plan should include:

  • Two applicable regulations and how they influence system design, data handling, or oversight.
  • One governance framework and how it supports responsible ML practices.
  • A basic risk matrix with three risks, risk owners, and mitigation strategies.
  • An audit checklist outlining essential documents and review processes.
  • An escalation workflow detailing detection triggers, response actions, and responsible roles.

Share your findings​

Submit your LearnSmart ML system compliance plan. It should reflect your group’s thinking around regulatory alignment, governance, risk mitigation, and audit readiness.

Regroup​

Return to the main session after 30 minutes to discuss key takeaways and insights from the activity.