Virtual Booth

INTRODUCTION

SKYE builds on deploying a social robot to collect facial and body-language data during one-on-one teacher–student interactions. SKYE addresses this gap by integrating multiple users, it empowers teachers with real-time emotional data and connects them with counselors through the same platform. Unlike adult or workplace-focused solutions, SKYE is purpose-built for the school context, making mental health support simpler and more accessible for students and staff alike.

METHODOLOGY

The robot interacts with students while analyzing their emotional cues. SKYE’s core consists of a classroom robot outfitted with cameras and motion sensors. The robot will analyze body posture and gestures as indicators of anxiety or sadness. All data are processed in real time, then summarized into an app dashboard. After each teacher–student session, the app shows a timeline of the student’s detected emotions and highlights concerning trends.

FINDINGS AND DISCUSSIONS

Although SKYE has not yet been field-tested, evidence from related work suggests its potential benefits and challenges. On the positive side, robots can unlock students’ willingness to reveal hidden emotions. We expect SKYE to similarly uncover subtle distress signals. By logging these emotional cues, the system would allow counselors to track a student’s mood over time, supplementing in-person support. In practice, a teacher could receive a gentle notification if a student’s emotional profile indicates rising anxiety, enabling a quick check-in or reminder, long before problems escalate.

CONCLUSION

SKYE introduces a new principle, integrating a socially aware robot into the school setting to support early mental health interventions. In principle, the system can generalize to any context where teachers need early warning of hidden student distress. By combining real-time emotion detection with an easy teacher–counselor app, SKYE empowers educators to notice subtle emotional issues sooner and to follow up with kindness and support.

REFERENCES

Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. M. Spector et al. (Eds.), Handbook of research on educational communications and technology (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3185-5_5