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Effective infectious disease control requires understanding transmission dynamics and behavioral factors that influence non-pharmaceutical interventions (NPIs) such as social distancing and quarantining. To investigate these dynamics in a real-life setting, we conducted a two-week gamified epidemic experiment at Wenzhou-Kean University, China, involving nearly 1,000 students. A Bluetooth-enabled app was used to simulate the spread of a virtual pathogen and collect real-time behavioral and epidemiological data. We developed a Susceptible-Exposed-Asymptomatic-Infected-Removed model with time-varying transmission rates to incorporate behavioral feedback and assess the effectiveness of interventions. Our results show that NPIs alone may not be sufficient unless coupled with high compliance driven by elevated risk perception. Participants who adhered strongly to NPIs significantly reduced transmission. Additionally, superspreader statistics and a heavy-tailed transmission tree structure mirroring patterns seen in biological outbreaks provides support for the external validity of the data. This studyreveals the importance of integrating adaptive human behavior and heterogeneity into epidemiological models and highlights the potential of our app-based epidemic experiments as tools for studying outbreak dynamics in real-life settings.