Audits for Trust: An Auditability Framework for AI-Based Learning Analytics Systems

What is it about?

This research introduces a framework for checking if AI systems used in education can be properly audited. Just like financial audits help ensure companies are honest, AI systems need regular checkups too. The authors explain what makes an AI system "auditable" - meaning it can be examined for fairness, accuracy, and ethics. Without proper documentation and transparency, it's hard to know if these systems are working fairly for all students. The framework helps developers build AI educational tools that can be verified by independent reviewers.

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Photo by Stephen Dawson on Unsplash

Photo by Stephen Dawson on Unsplash

Why is it important?

For science, this work addresses a significant gap in AI research by developing a systematic framework for auditability, which helps establish standards in a field where audit quality guidelines have been lacking. It contributes to the growing body of knowledge on responsible AI implementation in educational contexts. For practice, this research is crucial as AI-based learning systems become more prevalent in education. It provides developers and educational institutions with clear guidelines for creating transparent, accountable systems that protect student privacy and prevent discrimination against underrepresented groups. The framework enables regular verification that learning analytics tools remain fair and effective, which builds trust among users and stakeholders.