fairness and bias

How AI-Powered Assessment Tools Are Changing Student Evaluations

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Core idea AI-powered assessment tools are shifting evaluation from periodic, manual grading to continuous, data-informed measurement that is faster, fairer, and more personalized—freeing educators to focus on feedback and intervention while giving students immediate, actionable insights. What’s changing and how Evidence and 2025 signals Benefits for stakeholders Guardrails and ethics Practical implementation blueprint What’s next … Read more

The Role of Machine Learning in Predicting Student Dropout Rates

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Core idea Machine learning identifies at‑risk students earlier and more accurately by analyzing patterns across academic, engagement, and socio‑demographic data, enabling timely, targeted interventions that improve retention—especially when models are explainable, fair, and embedded in student support workflows. Why ML works for dropout prediction Evidence and 2024–2025 signals High‑value features to engineer Model choices and … Read more