Case Study 2: Behavioral Support & Social-Emotional Learning

Overview

This case study explores using embodied AI to support classroom behavioral management and social-emotional learning (SEL).

Scenario

Context: Upper elementary classroom (grades 3–5) with frequent transitions and occasional disruptive behaviors.

Intervention: A humanoid robot leads SEL exercises, models appropriate responses, and privately reminds students about expected behaviors.

System Components

Perception: Passive microphones and gesture detection (on-device only)
Policy: Rule-based triggers with LLM-driven de-escalation scripts
Reporting: Aggregated behavior logs in teacher dashboard (no raw data storage)

Measured Outcomes

Incident Reduction: Target 20–40% decrease over 8 weeks
Teacher Workload: 30–50% reduction in documentation time
SEL Measures: Small to moderate gains on validated self-regulation scales

Evidence Base

Robotics shows effectiveness for behavior rehearsal and SEL modeling. However, high false-positive rates in noisy classrooms can erode trust. Ethical considerations around surveillance and equity are critical.

Implementation Guidance

Validate detection thresholds in simulation
Document consent procedures clearly
Conduct equity audits across student demographics

Acceptance Criteria

Condensed narrative with metrics
Test script demonstrating detection pipeline
Live classroom trial with parental consent

Ethical Considerations: Before deployment, consult school board and seek explicit family opt-in.

Overview​

Scenario​

System Components​

Measured Outcomes​

Evidence Base​

Implementation Guidance​

Acceptance Criteria​