What is Adaptive AI?
AI that gradually learns about student needs and personalize support
Each students' perceptions and needs are a little different. Their experience is ever evolving, and your communication plans and measurements should be, too. With a solid foundation of evidence-based research, our Adaptive AI utilizes millions of historical student interactions and outcomes to personalize SMS check-ins and interventions to each student’s unique risk factors. While students receive more targeted support, you get the benefit of truly understanding your student population at scale, while intentionally addressing their student success needs.
Proactive Check-Ins
Drive insights through weekly proactive questions strategically timed to achieve maximum impact; including:
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Wellness Checks
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Summer and Winter Check-Ins
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Beginning of Semester Belonging Checks
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Persistence Check-Ins
With an average engagement rate of greater than 60%, our Adaptive AI learns faster by receiving a large pool of responses that continues to grow its understanding and ability to identify intervention needs more proactively.
Surfacing and Prioritizing Intervention Opportunities
Engagement data is immediately analyzed and available in your EdSights dashboard, providing access to meaningful insights at your fingertips, including:
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Student risk levels
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Intervention opportunities
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Student engagement summaries
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Analysis of key drivers impacting your students
Additionally, team members can track automatic and manual interventions on a student-by-student basis and prioritize based on persistence risk levels that are automatically generated by EdSights. These are calculated through a real-time analysis of our conversations with each student and attached to each student record.
Predicting Retention
in Real-Time
Over time, students are automatically assigned risk levels based on their responses to proactive check-ins. Additionally, students at medium and high risk of not remaining at the institution are automatically connected to campus resources that will support them based on the risk driver(s) that are impacting them most as outlined by our Adaptive AI Persistence Framework.
Finally, campus administrators are able to review each students’ interactions and identify both targeted opportunities for follow up as well as institutional trends that reveal persistence patterns and how risk and needs may change across different student groups.
