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How can educational institutions identify students at risk of academic failure before it's too late?

In their research presented at AIPU 2026, Nazim Kazar and Camille Salinesi explore how attendance, behavior, and academic performance can be transformed into meaningful early warning signals.

By bringing together established research, the article proposes a structured framework for detecting academic risk, supporting timely interventions, and laying the groundwork for more intelligent, data-driven learning systems.

Click here to read the full research article and discover how early detection can help shape the future of education.