Case Study 3
From Guesswork to Graduation: AI Puts Student Success on Autopilot for a Globally Recognized Online University

Driving student success with predictive AI and proactive interventions

The Question: Could AI help identify at-risk students early and help them take corrective measures to set them up for success?

No predictive tools to flag at-risk students accurately.
Reactive support interventions causing delays in assistance.
Limited analytics on student engagement trends, hindering proactive decision-making.
Inefficiencies in aligning resources with student needs to optimize outcomes.

About the Client

The client, a globally recognized online university serving undergraduate and postgraduate students worldwide, faced challenges in sustaining student pass rates and engagement levels. Identifying at-risk students early was critical, but existing processes lacked precision and timeliness. Challenges included:

The Answer

AI-driven Predictive Models: Analyzed historical and real-time student data to identify patterns associated with success or failure.
Automated Early Alerts: Flagged at-risk students to advisors, enabling timely and targeted support.
Dynamic Dashboards: Equipped administrators with a real-time view of engagement trends and performance metrics.
Personalized Interventions: Provided actionable recommendations for tailored learning resources and support efforts.
Continuous Model Optimization: Improved predictive accuracy over time by incorporating new data and insights.
Integrated Collaboration: Streamlined communication between academic supervisors and support teams.

The institution implemented an AI-powered predictive analytics system designed to assess student success likelihood and trigger timely interventions.

The Outcomes

The predictive AI model transformed how the institution supported its students, leading to measurable improvements in outcomes.

15% increase in student pass rates, stemming from earlier and focused interventions.
Proactive retention efforts reduced student dropout rates, ensuring stable enrollments.
Enhanced educator productivity, allowing staff to focus on personalized teaching rather than reactive problem-solving.
Increased visibility into student performance trends, fostering data-driven decision-making across departments.

The university creates more personalized and efficient interventions by proactively identifying risks, elevating outcomes, and fostering long-term student satisfaction.