This study examines the gap between digitalization agendas and school realities, where connectivity constraints, limited devices, and uneven support restrict pedagogical innovation. It evaluates the Model for Integrative and Predictive Smart Teaching Adaptation, integrating artificial intelligence, learning analytics, and Internet of Things components, within a holopraxic cycle of diagnosis, design, implementation, evaluation, and readaptation driven by field feedback. Over a semester, 120 rural teachers participated in a quasi-experimental study combining a competence questionnaire, interviews, and system usage logs. Baseline competence was comparable between groups, and gain was defined on a zero to one hundred scale as post minus pre. The experimental group showed a median gain of 6.25 points, whereas the control group remained at 0.00; the common-language effect size was 0.765. Engagement was sustained with peaks (twenty-five to thirty-seven sessions per week), indicating selective appropriation rather than linear growth. Results support improvement and emphasize adoption conditions: teacher agency, ethical trust, and institutional sustainability, operationalized through pseudonymization and bias auditing.