Grounded in socially shared regulation of learning (SSRL), this paper investigates how joint mental effort (JME) and joint visual attention (JVA) serve as process-level indicators of shared regulation in pair programming and how AI-driven adaptive feedback can strengthen these processes. We present three eye-tracking studies involving 182 dyads engaged in collaborative debugging tasks. Study 1 examines natural collaboration and shows that high-performing dyads exhibit significantly higher JME and JVA, a greater prevalence of productive high-JME-high-JVA episodes, and a stable causal relationship in which JME predicts JVA. Study 2 evaluates reactive adaptive feedback based on real-time deviations in JME and/or JVA. Results show that combined feedback targeting both dimensions yields the strongest improvements in performance, regulatory coherence, and cognitive-to-attentional causality, outperforming single-channel feedback. Study 3 introduces proactive, forecast-based feedback using machine-learning predictions of future collaboration states. Proactive support further enhances performance and sustains shared regulation by anticipating breakdowns before they manifest. Across studies, causal modeling reveals that cognitive alignment systematically drives attentional coordination in successful collaboration, while mismatches between effort and attention characterize unproductive regulation. Methodologically, this work integrates dual eye-tracking, pupillometry, episode-based analysis, and causal inference to capture SSRL as a dynamic, emergent process. Conceptually, the findings position AI not as an automated controller, but as an intelligence-augmenting co-regulator that supports learners' capacity to coordinate effort, attention, and understanding together.

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