Neuroadaptive Return on Investment in Education: A Critical Review of EEG and Eye-Tracking for Decision Optimization

This article advances a critical synthesis of a proposed neuroadaptive return on investment framework that integrates electroencephalography and eye-tracking into educational decision systems. The analysis situates neuroadaptive ROI within scholarship on neurodiversity, engagement, and adaptive learning, arguing that process-level indicators of attention, cognitive load, and persistence merit inclusion alongside conventional outcome metrics in investment models. Methodological scrutiny examines construct validity for neural and gaze indices, requirements for multimodal fusion, calibration across heterogeneous learner profiles, and threats to internal and external validity in classroom contexts. Evidence from pilot implementations suggests feasibility for real-time pacing, friction-point detection, and targeted resource triage, although durability of effects, generalizability across settings, and population-level heterogeneity remain insufficiently established. Implementation feasibility is assessed in relation to hardware ergonomics, analytics latency, platform interoperability, educator preparation, and cost structures, with emphasis on human factors that condition uptake and fidelity. Ethical analysis foregrounds informed consent and assent, purpose limitation, data minimization, confidentiality, algorithmic bias auditing, equity safeguards, and mental privacy protections as prerequisites for responsible deployment. The review proposes a staged research agenda that includes preregistered classroom trials, longitudinal outcome tracking, independent cost-effectiveness analyses, robustness and fairness testing, educator professional development, and participatory governance with neurodivergent communities. Taken together, findings indicate that neuroadaptive ROI offers a credible pathway for learner-centered optimization of educational investment, conditional on rigorous validation, transparent reporting, and ethically grounded infrastructure that preserves agency and trust throughout the data lifecycle.