Document Type
Presentation
Publication Date
7-2026
Keywords
artificial intelligence, wearable technology, predictive AI, machine learning, confidence, stress management, biosensing, personalized AI, public speaking, responsible AI, adolescent education, AI literacy
Disciplines
Education | Engineering | Social and Behavioral Sciences
Abstract
This group project presents ConfidenceAI, a conceptual wearable and predictive artificial intelligence ecosystem designed to help teenagers build confidence and manage stress in high-pressure situations such as classroom participation and public speaking. The system combines wrist-based biosensing with a personalized machine-learning model that uses physiological signals, including heart rate and heart rate variability, electrodermal activity, skin temperature, and movement, to learn an individual's baseline patterns and anticipate heightened stress responses. Rather than relying on universal thresholds, ConfidenceAI is designed to personalize the timing, method, and delivery of interventions based on each user's physiological patterns and prior responses. The proposed ecosystem incorporates predictive interventions, an AI confidence coach, public-speaking rehearsal and feedback, and gamification to support confidence-building over time. The project also emphasizes privacy through on-device processing and positions ConfidenceAI as a confidence and stress-management tool rather than a medical device or diagnostic system.
Community Engaged/Serving
Part of the UMass Boston Community-Engaged Teaching, Research, and Service Series. //scholarworks.umb.edu/engage
Recommended Citation
Paul, Denzil; Kong, Xuejun; Wang, Raymond; Lopes, Flora Fleith; Yang, Zhengjia; Chu, Brain; Wang, Weijian; and Chen, Leo, "ConfidenceAI: A Wearable and Predictive AI Ecosystem" (2026). Paul English Applied Artificial Intelligence (AI) Institute Publications. 41.
https://scholarworks.umb.edu/ai_pubs/41
Rights
© 2026 The Confident Hero
Comments
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