Generative AI feedback loops drive cognitive engagement and equity via the digital Hawthorne effect

The scalable acquisition of classroom process data is chronically hindered by the trilemma of cost, privacy, and technological accessibility, particularly in low-resource environments. Here, we present a zero-hardware, privacy-preserving generative AI agent that operates via browser-based edge computing and Large Language Models (LLMs) to provide real-time formative assessment of classroom discourse. In a 16-week quasi-experimental study involving 12th-grade students (N = 98), we investigated the system’s impact on pedagogical dynamics and academic outcomes. Results demonstrate that continuous, non-intrusive AI visualization triggered a sustained "Digital Hawthorne Effect," culminating in a 376.9% increase in active classroom participation. "Furthermore, AI-quantified substantive engagement emerged as a positive protective factor, effectively buffering students against severe academic regression."Controlling for baseline abilities, the experimental group achieved significantly higher academic outcomes (partial η² = 0.063) while effectively preventing severe academic regression. Notably, the intervention exhibited a pro-equity "catch-up effect," yielding a 29% higher marginal academic benefit for low-achieving learners compared to their high-achieving peers. By replacing costly biometric surveillance with edge-processed semantic analysis, this study reframes generative AI as a low-cost environmental variable. Ultimately, our findings provide a scalable, frugal innovation framework to foster human-AI triadic symbiosis and advance global educational equity.