AI Atlas — The Global AI Opportunity Atlas

Identifying students at risk of dropping out early

Universities notice disengagement only after failed exams, when interventions are less effective.

EducationRomaniaSeed Admin

Problem

LMS activity, attendance, and assessment signals are siloed; advisors lack ranked outreach lists.

Current workflow

Advisors wait for student-initiated meetings or end-of-term grade reviews.

Consequences

Higher dropout and wasted student debt.

How AI might help

Risk models can prioritize outreach with explanations advisors can act on.

Limits & risks

Proxy variables may reflect socioeconomic bias.

Stigmatization; discriminatory targeting.

Alternatives today

Mandatory advising and early-alert rules.

Rules are coarse; advising capacity limited without prioritization.

Evidence

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Identifying students at risk of dropping out early · AI Atlas