Student Learning Assessment Case Study

This case study examines the development of an assurance case for an AI system used to assess student learning and provide personalised feedback.

Background

Educational assessment is fundamental to learning. Traditional assessment methods can be time-consuming for educators and may not provide timely feedback to students. AI systems promise more frequent, personalised assessment while reducing educator workload.

The System

The hypothetical system in this case study:

• Assesses student work (essays, problem sets, projects)
• Provides immediate feedback and suggestions
• Tracks learning progress over time
• Adapts difficulty and content to student needs

Assurance Goals

The primary assurance goal for this system might be:

“The AI-powered learning assessment system fairly evaluates student work and provides helpful feedback that supports learning.”

Key Considerations

Fairness

• Consistent grading across different student populations
• Bias in training data and assessment criteria
• Accessibility for students with different needs

Educational Validity

• Alignment with learning objectives
• Impact on student learning outcomes
• Effect on teaching practices

Privacy

• Protection of student data
• Appropriate use of learning analytics
• Consent and transparency with students and parents

Transparency

• Explainability of grades and feedback
• Clear communication of AI involvement
• Appeals process for contested assessments

Discussion Questions

1. How would you ensure the system provides fair assessments across different student backgrounds?
2. What evidence would demonstrate that the system actually improves learning?
3. How should students and parents be informed about AI involvement in assessment?
4. What human oversight should remain in the assessment process?

Further Reading

• Understanding the Assurance Ecosystem