CASE STUDY

1. The Challenge: A Post-Pandemic Crisis

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Chronic absenteeism in the U.S. has nearly doubled compared to pre-pandemic times, jumping from 1-in-6 to 1-in-3 students. This created an even wider achievement gap. Researchers attribute this surge to a combination of shifting cultural norms, intensified mental health struggles, the lingering effects of the pandemic on family stability, and student disengagement.

• The Problem: Attendance officers were drowning in "fragmented data"—scattered emails, physical notes, and siloed software. They could see who was missing and sometimes the reported reason, but they couldn't identify the underlying reasons or trends nor take action fast enough to stop the trend.

• The Shift: We needed to evolve TalkingPoints from a family communication app into a targeted intervention engine that identifies root causes before a student drops out of the system.

2. Discovery: Mapping the "Overworked" User

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Through 1:1 sessions with a variety of stakeholders, including school principals, attendance clerks, and families we discovered that the current attendance workflow was time-consuming and frustrating for everyone involved. 

• Insight 1: Messaging fatigue is real. Schools needed a way to scale empathy without increasing their manual workload or over-messaging the families.

• Insight 2: Early patterns are predictive. Students missing 10% of school in September are likely to be chronically absent for the entire school year.

• Strategic Partnership: We collaborated with Attendance Works to ensure that TalkingPoints makes it seamless for schools to follow research-backed attendance strategies, like sending out "empathetic attendance messages".

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3. The Solution: An AI-Powered Intervention Suite

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We designed a three-tier ecosystem that turned passive data into active participation.

• The Actionable Dashboard: Rather than just showing lists, we designed customizable student segments. Clerks could set "Smart Thresholds" to trigger automated, culturally-attuned messaging cadences based on real-time data.

• AI-Driven Root Cause Analysis: We leveraged AI to parse thousands of family-school messages. The system suggests why a student is absent (e.g., transportation issues, health, housing instability). We built a Human-in-the-Loop (HITL) interface, allowing admins to verify, reject, or override AI suggestions to ensure data integrity.

• The Student "Journey" Profile: A holistic view of a student's attendance history. This included AI-driven celebration moments—nudging admins to send positive reinforcement when a struggling student showed improvement, not just when they failed.

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