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

Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 306 ISRG PUBLISHERS Abbreviated Key Title: ISRG J Arts Humanit Soc Sci ISSN: 2583-7672 (Online) Journal homepage: https://isrgpublishers.com/isrgjahss Volume – III Issue -VI (November-December) 2025 Frequency: Bimonthly Neuroadaptive Return on Investment in Education: A Critical Review of EEG and Eye-Tracking for Decision Optimization Piper Hutson1* , James Hutson2 1, 2 Lindenwood University, USA | Received: 25.11.2025 | Accepted: 01.12.2025 | Published: 03.12.2025 *Corresponding author: Piper Hutson Sci-Bono Discovery Centre, South Africa Abstract 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. Keywords: neuroadaptive ROI, EEG, eye-tracking, educational governance, neurodiversity Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 307 Introduction Educational decision systems have long privileged outcome-only return-on-investment models that monetize or proxy “returns” through test scores, course completions, graduation, and postgraduation earnings while treating the learning process as a black box. Such frameworks occlude the mechanisms by which instruction succeeds or fails, thereby delaying detection of friction points until after summative assessment and obscuring heterogeneous learner trajectories that precede performance divergence (Hattie, 2009). Process-level indicators grounded in cognitive science and educational neuroscience offer an alternative evaluative substrate by indexing attention, cognitive load, affective engagement, and persistence as they unfold during learning, which better aligns with theories that link germane cognitive effort and sustained attentional control to durable learning (Cavanagh & Frank, 2014; Sweller, 1988). Integrating validated measures such as electroencephalography for attentional regulation and cognitivecontrol dynamics, together with eye-tracking for visual attention allocation and information-seeking strategies, enables formative diagnostics that can be tied to adaptive instruction and resource triage in near real time (Christodoulou & Gaab, 2009; Holmqvist et al., 2022). In this domain, proposals for neuroadaptive ROI argue that decisions should weight improvements in process quality alongside distal outcomes, especially for neurodivergent learners whose engagement signatures deviate from population averages and are frequently misread by outcome-only metrics. Reframing ROI around process-sensitive evidence can surface earlier intervention windows, reduce repeated remediation cycles, and improve equity by recognizing valid but non-normative engagement patterns as assets to be supported rather than anomalies to be suppressed (D’Mello & Graesser, 2012; Woolf et al., 2010). The problem, then, is not merely instrumental; it is conceptual: outcome-only ROI conflates effect with cause, whereas process-level indicators can clarify causal pathways, strengthen theory-practice coherence, and inform more precise allocation of instructional time, staff effort, and technology budgets. The purpose and scope of this article are to provide a critical appraisal of a neuroadaptive ROI framework that integrates EEG and eye-tracking for decision optimization in educational settings, with particular attention to theoretical justification, measurement validity, empirical plausibility, implementation feasibility, and ethical-governance requirements. The review situates the model within established literatures on engagement, cognitive load, intelligent tutoring, and educational data science, then evaluates whether neural and gaze indices function as reliable and interpretable proxies for learning-relevant constructs across heterogeneous populations (D’Mello & Graesser, 2012; Holmqvist et al., 2022; Sweller, 1988; Woolf et al., 2010). Building from these foundations, the article compares process-oriented ROI accounting to conventional outcome-only approaches, specifying decision consequences for administrators who must weigh nearterm costs against multi-dimensional benefits that include equity, wellbeing, and long-run learning efficiency (Hattie, 2009). The scope encompasses K–12 and higher education contexts, with emphasis on classroom-integrated measurement rather than laboratory-only feasibility demonstrations, and it foregrounds the analytic and organizational conditions under which neuroadaptive signals would add value beyond existing behavioral telemetry. Throughout, the appraisal keeps neurodiversity at the center of the optimization problem by treating variation in attentional and perceptual signatures as a design parameter for systems rather than a nuisance variable to be averaged out. The review is guided by four research questions that collectively frame contributions to decision science in education. First, what theoretical and empirical reasons justify incorporating EEGand eye-tracking–derived indicators into ROI calculations beyond traditional academic outcomes, and under what boundary conditions do those indicators maintain construct validity (Cavanagh & Frank, 2014; Christodoulou & Gaab, 2009)? Second, to what extent do multimodal neural and gaze measures predict, mediate, or moderate learning outcomes in authentic instructional settings, and how does this vary across neurodivergent and neurotypical populations (D’Mello & Graesser, 2012; Holmqvist et al., 2022)? Third, what technical and organizational capabilities are required to implement neuroadaptive ROI at scale with acceptable reliability, interpretability, and cost, including device ergonomics, low-latency analytics, educator-facing dashboards, and data governance aligned to cognitive privacy norms (Woolf et al., 2010)? Fourth, how should process-sensitive evidence be aggregated with distal outcomes to support transparent, equitable decisions about program design, resource allocation, and accountability, and what safeguards are necessary to prevent pathologizing difference or amplifying bias in algorithmic adaptations (Hattie, 2009)? Addressing these questions advances decision science by linking causal mechanisms to economic reasoning, thereby enabling administrators to optimize for learning processes that produce the outcomes systems ultimately value. For clarity and analytic precision, the article operationalizes four constructs that anchor the proposed model and the ensuing critique. Engagement is defined as a multi-component state comprising behavioral, emotional, and cognitive dimensions that co-vary with attention, interest, and effort during learning; EEG frequency dynamics associated with sustained attention and frontal control, together with gaze fixations and saccadic patterns, serve as convergent indicators of moment-to-moment engagement (Cavanagh & Frank, 2014; D’Mello & Graesser, 2012). Cognitive load refers to the mental effort imposed by task demands relative to working-memory resources; in this account, neurophysiological and oculomotor markers index shifts among intrinsic, extraneous, and germane load that instructional design seeks to balance for schema acquisition (Sweller, 1988; Holmqvist et al., 2022). Persistence denotes sustained task involvement over time, measured behaviorally through on-task continuation and interruption resistance, and inferred physiologically through stable attentional control and efficient visual search under increasing difficulty, which together predict durable learning and transfer (Hattie, 2009; Woolf et al., 2010). Neurodiversity-informed optimization describes a design stance that treats individual variability in sensory processing, attentional regulation, and perceptual navigation as a structural parameter for adaptive systems, requiring calibration and fairness auditing so that algorithms accommodate, rather than erase, non-normative engagement signatures. These definitions establish a shared vocabulary for evaluating whether and how neuroadaptive signals can responsibly inform ROI decisions in education. Literature Review Educational ROI models have traditionally operationalized return through distal outcomes such as standardized achievement, progression, graduation, and earnings, then related these outcomes to program or per-pupil expenditures through cost-effectiveness or Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 308 cost–benefit analyses (Levin & Belfield, 2015; Psacharopoulos & Patrinos, 2018). This tradition, while powerful for policy comparison, often treats instruction as a black box and aggregates across heterogeneous learner trajectories, which obscures causal mechanisms and delays detection of instructional friction until summative assessment or attrition (Table 1) (Hanushek & Woessmann, 2020; Guskey, 2014). Meta-analytic syntheses demonstrate robust links between instructional influences and achievement, yet they typically rely on outcome snapshots rather than continuous evidence of process quality that might guide timely resource reallocation (Hattie, 2009; Kraft, 2020). Program evaluation handbooks in K–12 and higher education likewise emphasize end-point metrics, sometimes augmenting with attendance or course-taking proxies that correlate only imperfectly with cognitive engagement (Banta & Blaich, 2011; Fitzpatrick, Sanders, & Worthen, 201). Cost modeling in learning analytics has begun to incorporate time-on-task and platform telemetry, but these signals are still behavioral rather than neurocognitive and can confound strategic skipping with disengagement (Daniel, 2015; Papamitsiou & Economides, 2014). Consequently, conventional ROI risks underestimating benefits for learners whose progress depends on adaptive pacing or sensory accommodations that do not immediately translate into test gains, particularly for neurodivergent populations (Shaywitz et al., 2021; Rose et al., 2006). A process-sensitive ROI that complements distal outcomes with validated indicators of attention, load, and persistence promises earlier, more equitable decision points for intervention and investment (Levin, 2020; Phillips & Phillips, 2016). Table 1. Conventional versus neuroadaptive ROI indicators Domain Indicator Temporal granularity Sensitivity to heterogeneity Actionability Typical data source Outcomes (Traditional ROI) Standardized test scores; course completion; graduation; earnings proxies End of term or annual snapshots Low. Aggregation masks subgroup variance Moderate. Triggers program-level decisions post hoc Student information systems; state assessments; administrative records Outcomes (Neuroadaptive ROI) Process-to-outcome mediation models linking proximal indicators to distal performance Weekly checkpoints with interim mastery estimates Moderate to high. Models stratified by learner profile High. Supports iterative adjustments and resource triage during term Learning analytics warehouse integrating LMS, assessment, and sensor summaries Engagement (Traditional ROI) Attendance; platform logins; time-on-task; click counts Daily or weekly aggregates Low. Behavioral proxies confound strategic skipping with disengagement Low to moderate. Indirect guidance for allocation LMS logs; device usage analytics; attendance systems Engagement (Neuroadaptive ROI) EEG-derived attention indices; eye-tracking fixation density and dispersion; affect markers Seconds to minutes High. Personalized baselines accommodate neurodivergent signatures High. Real-time prompts for segmentation or modality shifts Wearable EEG; screenbased eye trackers; synchronized telemetry Cognitive Load (Traditional ROI) Self-reports; task difficulty ratings; item response times Lesson or unit level Moderate. Susceptible to recall and social desirability bias Moderate. Post hoc redesign guidance Surveys; assessment platforms; observational rubrics Cognitive Load (Neuroadaptive ROI) Oscillatory features and complexity metrics; gaze regressions and splitattention signals Seconds to minutes High. Calibrated to individual thresholds and task context High. Triggers pacing, signaling, and chunking interventions EEG time series; eyetracking event streams Persistence (Traditional ROI) Course retention; assignment submission rates; dropout flags Weekly to term level Low to moderate. Late-stage signal Moderate. Supports reactive intervention Registrar records; LMS assignment logs Persistence (Neuroadaptive ROI) Stability of attention index; sustained efficient scan paths under rising difficulty Minutes to weeks High. Sensitive to early effort collapse at the task level High. Enables early support before failure cascades Sensor-derived indices fused with task telemetry Equity (Traditional ROI) Subgroup outcome gaps by demographics Term or annual reporting Moderate. Detects disparities only after outcomes materialize Low to moderate. Guides long-cycle policy changes Accountability dashboards; state reports Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 309 Equity (Neuroadaptive ROI) Subgroup fairness metrics for process indicators; individualized baselines Continuous with periodic audits High. Captures disparate impact in process before outcome gaps widen High. Informs targeted accommodations and model retraining Model cards; bias audits; stratified sensor analytics Cost (Traditional ROI) Program cost per student; cost per point gain; cost per graduate Budget cycle Low. Averages obscure differential returns Moderate. Supports portfolio balancing after results Finance systems; costeffectiveness spreadsheets Cost (Neuroadaptive ROI) Incremental cost per avoided remediation hour; per minute saved to mastery; per engagement unit gained Monthly with rolling updates Moderate to high. Stratified by learner profile and course context High. Enables dynamic reallocation during the term Finance systems joined with learning and sensor analytics Contemporary engagement theory decomposes engagement into behavioral, emotional, and cognitive dimensions that jointly predict learning, transfer, and persistence, implying that valid ROI should weight improvements in these proximal constructs alongside distal outcomes (Fredricks, Blumenfeld, & Paris, 2004; Appleton, Christenson, & Furlong, 2008). Cognitive load theory further specifies that instructional efficiency depends on balancing intrinsic, extraneous, and germane load so that scarce workingmemory resources are allocated to schema construction rather than search or split attention, offering a mechanistic rationale for process-level indicators in ROI (Sweller, 2011; Paas, Renkl, & Sweller, 2003). Adaptive learning systems operationalize these principles by inferring latent states from interaction traces and adjusting task difficulty, feedback timing, or representational format to maintain productive struggle, with documented effects on learning efficiency and time savings (VanLehn, 2011; Koedinger, Corbett, & Perfetti, 2012). Intelligent tutoring research shows that closed-loop adaptation guided by validated proxies for engagement and confusion improves mastery rates and reduces unnecessary practice, which in ROI terms raises returns per instructional minute and per dollar invested (Aleven et al., 2017; Baker et al., 2010). Affect-aware and metacognition-aware designs extend the signal space beyond correctness to include frustration, boredom, and mind wandering, thereby enabling timely de-escalation or strategy coaching that prevents costly remediation later (Pekrun, 2006; D’Mello, 2013). These strands converge on a design implication for ROI: investments that reliably elevate engagement and optimize cognitive load during learning produce compounding benefits in achievement, retention, and well-being, provided that the indicators are valid and sensitive to individual differences (Kizilcec, Perez-Sanagustín, & Maldonado, 2017; Schneider, Nebel, & Rey, 2016). A neuroadaptive ROI proposal positions EEG and eye-tracking as higher-fidelity indicators of these constructs suitable for real-time decision support when interpreted within established theoretical frames (Christodoulou & Gaab, 2009; D’Mello & Graesser, 2012). Electroencephalography offers millisecond temporal resolution on oscillatory dynamics linked to sustained attention and cognitive control, with frontal midline theta and beta–alpha ratios repeatedly associated with task engagement and top-down regulation relevant for instruction (Cavanagh & Frank, 2014; Klimesch, 2012). Classroom and field studies demonstrate the feasibility of using dry electrodes and lightweight headsets to estimate engagement indices that correlate with comprehension and mind wandering, though careful artifact handling and individual calibration remain prerequisites for validity (Dmochowski et al., 2012; Baldwin et al., 2017). Eye-tracking provides spatially precise measures of visual attention and information search through fixations, saccades, and regressions, with decades of evidence linking gaze patterns to reading fluency, problem solving, and split-attention effects in multimedia learning (Holmqvist, Nyström, & Mulvey, 2022; Rayner, 1998). Recent classroom-integrated systems combine gaze with interface telemetry to detect confusion and dynamically adjust signaling or segmentation, yielding efficiency gains consistent with cognitive load predictions (Lai et al., 2013; van Gog & Jarodzka, 2013). Multimodal fusion studies show that combining EEG with eye-tracking improves robustness and interpretability over either modality alone, enabling more reliable detection of attention lapses and overload in authentic tasks (Zander & Kothe, 2011; Chaouachi, Jraidi, & Frasson, 2011). Nonetheless, reliability is conditional on ergonomics, motion artifacts, and context, which necessitates transparent reporting, per-learner baselining, and cross-session validation before incorporation into high-stakes decisions or ROI accounting (Luck, 2014; Cohen, 2017). Within this evidentiary landscape, proposals to treat neurophysiological indices as process-level inputs to ROI are plausible when signals are theory-aligned, quality-controlled, and used as formative guides rather than deterministic labels. A neurodiversity-informed perspective emphasizes that variability in sensory processing, attentional regulation, and perceptual navigation is a normative feature of human cognition, which implies that measurement systems must accommodate diverse signatures to avoid pathologizing difference or amplifying bias (Gernsbacher, 2017; Armstrong, 2015). Equity critiques of learning analytics warn that models trained on majority populations can misread or penalize atypical interaction patterns, thereby channeling resources away from those who might benefit most, and call for fairness auditing, representative sampling, and stakeholder co-design (Baker & Hawn, 2021; Slade & Prinsloo, 2013). In disability and special education research, evidence shows that gaze and physiological markers often reflect alternative strategies rather than deficits, which argues for adaptive thresholds and personalized baselines when interpreting engagement for students with autism, ADHD, or dyslexia (Loth, Phillip, & Lombardo, 2021; de Jong et al., 2016). Universal Design for Learning provides a policy and design framework for offering multiple means of engagement, representation, and action, aligning with the proposition that ROI should recognize returns generated when systems flex to learner variability rather than enforce average- Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 310 centric norms (Meyer, Rose, & Gordon, 2014; Rose et al., 2006). Fairness in educational machine learning further recommends model cards, subgroup performance reporting, and counterfactual testing to identify disparate impact across neurotypes before deployment at scale (Holstein et al., 2019; Williamson, Eynon, & Potter, 2020). Integrating these safeguards with neuroadaptive measurement would operationalize equity by ensuring that EEG and gaze signals are used to expand access and precision, not to gatekeep or stereoptype learners, thereby aligning measurement practice with ethical ROI that values inclusion as a component of return (Suresh & Guttag, 2021; Núñez et al., 2022). Methods The review employed a structured narrative design appropriate for synthesizing multi-disciplinary literatures that span measurement science, learning sciences, and implementation research while allowing theory-driven adjudication across heterogeneous study types (Baumeister & Leary, 1997; Greenhalgh, Thorne, & Malterud, 2018). Screening followed transparent reporting conventions for narrative syntheses, with an a priori protocol specifying inclusion of peer-reviewed empirical studies, methodological primers, and scholarly books or chapters published in English from 2000 onward that address educational ROI, engagement and cognitive load, EEG or eye-tracking in instructional contexts, or equity in educational analytics (Page et al., 2021; Wong et al., 2013). Conceptual coherence was evaluated by mapping each source’s constructs to established definitions of engagement, cognitive load, and persistence, and by assessing whether proposed mechanisms were theoretically consistent with cognitive and motivational frameworks (Fredricks, Blumenfeld, & Paris, 2004; Sweller, 2011). Measurement validity was judged using domain standards for psychophysiology and eye-tracking, including evidence for construct, convergent, and criterion validity, along with documented preprocessing and artifact control (Keil et al., 2014; Holmqvist, Nyström, & Mulvey, 2022). Empirical plausibility was assessed through effect direction, dose-response patterns, and replication across settings, considering internal validity using RoB 2 for randomized trials and ROBINS-I for nonrandomized studies (Sterne et al., 2019; Sterne et al., 2016). Feasibility was appraised using adoption and scalability heuristics from the NASSS framework, focusing on technology maturity, organizational fit, and workability in classrooms (Figure 1) (Greenhalgh et al., 2017). Disagreements in screening or appraisal were resolved through consensus procedures common in qualitative syntheses, with rationale recorded to preserve auditability (Booth et al., 2013). This configuration balances methodological rigor with the breadth required to evaluate a neuroadaptive ROI proposal that intersects theory, measurement, and implementation science. Figure 1. Feasibility profile by NASSS domains Source identification combined database searching in Scopus, Web of Science, PsycINFO, ERIC, and PubMed with backward and forward citation chasing to surface foundational and contemporary work at the intersection of educational ROI, adaptive learning, psychophysiology, and equity analytics (Cooper, Hedges, & Valentine, 2019; Bramer et al., 2017). Search strings systematically combined controlled terms and keywords for “return on investment” or “cost-effectiveness” with “education” or “learning,” paired with “electroencephalography,” “EEG,” “eye-tracking,” “engagement,” “cognitive load,” and “adaptive learning,” and with equity terms such as “neurodiversity,” “fairness,” and “bias” (Rethlefsen et al., 2021). In addition to empirical reports, methodological texts and consensus statements that codify measurement and reporting standards for EEG and eye-tracking were included to ground judgments of validity and reliability (Luck, 2014; Keil et al., 2014). Implementation and governance literatures were sampled to evaluate feasibility and ethical prerequisites for classroom deployment, drawing on technology adoption syntheses and education-focused risk frameworks (Venkatesh et al., 2003; Floridi et al., 2018). To ensure coverage beyond laboratory-only demonstrations, studies conducted in quasi-naturalistic settings or with classroom-integrated protocols were prioritized, while still incorporating high-quality lab findings when they illuminated mechanisms relevant to field use (VanLehn, 2011; Dmochowski et al., 2012). Grey literature, conference abstracts, and opinion essays were excluded unless they summarized consensus measurement guidelines, in order to privilege peer-reviewed evidence suitable for informing administrative decisions (Cooper et al., 2019; Page et al., 2021). This multi-database, snowballing strategy supports a triangulated evidence base adequate for cross-disciplinary synthesis. Evidence was integrated through a comparative matrix that aligned traditional ROI indicators and decision levers with candidate neuroadaptive indicators and corresponding instructional adaptations, enabling structured contrast on construct coverage, temporal granularity, sensitivity to heterogeneity, interpretability, and cost (Popay et al., 2006; Petticrew & Roberts, 2006). For each Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 311 study, effect direction and qualitative magnitude were coded for proximal constructs (attention indices, cognitive load proxies, persistence behaviors) and distal outcomes (achievement, retention), with attention to whether process gains mediated distal improvements, a pattern consistent with theory-driven ROI logic (Baker et al., 2010; Koedinger, Corbett, & Perfetti, 2012). Where sufficient quantitative information existed, effect-direction plots and harvest plots summarized consistency across contexts without over-interpreting heterogeneous metrics, a technique suited to mixed-methods literatures (Ogilvie et al., 2008; Campbell et al., 2020). Feasibility observations were tabulated using NASSS domains to capture device ergonomics, data latency, workflow fit, and organizational capacity, while governance considerations were mapped to transparency, consent, and fairness criteria derived from emerging AI ethics norms in education (Greenhalgh et al., 2017; Holmes, Bialik, & Fadel, 2019). The matrix also recorded reporting quality against EEG and eye-tracking checklists to weight the credibility of findings in the narrative synthesis (Keil et al., 2014; Holmqvist et al., 2022). This approach preserves theoretical alignment while avoiding inappropriate meta-analysis across incommensurable measures, consistent with best practice for complex evidence bases (Popay et al., 2006; Greenhalgh et al., 2018). Anticipated threats included publication bias toward positive neurotechnology findings, construct drift in engagement and load operationalizations, and confounding by novelty effects or instructor expectancy in classroom pilots (Ioannidis, 2005; Clark, Tanner-Smith, & Killingsworth, 2016). To mitigate publication bias, database searches were supplemented with citation chasing from neutral methodological sources and with inclusion of nullresult studies when identifiable in reference lists, and effectdirection plots were used rather than pooled estimates sensitive to selective reporting (Campbell et al., 2020; Bramer et al., 2017). Construct drift was addressed by anchoring all coding to canonical definitions and by requiring explicit mapping from raw EEG and gaze features to theoretically warranted constructs, discounting studies that lacked such justification (Fredricks et al., 2004; Sweller, 2011). Risk of bias was appraised with RoB 2 and ROBINS-I criteria tailored to education, noting randomization procedures, allocation concealment, baseline equivalence, and selective outcome reporting (Sterne et al., 2019; Sterne et al., 2016). Reliability threats from signal artifacts and preprocessing variability were managed by weighting studies that reported electrode configurations, artifact rejection, and eye-tracking calibration, consistent with field standards (Luck, 2014; Keil et al., 2014). Implementation bias was considered through NASSS lenses to avoid over-generalizing from highly resourced settings to typical schools, and interpretability bias was tempered by explicitly coding stakeholder comprehensibility and actionability of indicators (Greenhalgh et al., 2017; Venkatesh et al., 2003). Finally, equity bias was monitored by recording subgroup analyses where available and by privileging studies that reported performance across diverse learner profiles, including neurodivergent samples, to guard against average-centric inference (Holstein et al., 2019; Slade & Prinsloo, 2013). These safeguards collectively strengthen the credibility and transferability of the synthesis for decision making. Results Convergent evidence links specific electroencephalographic and oculomotor signatures to proximal learning mechanisms that forecast distal achievement (Table 2). Frontal midline theta and beta–alpha dynamics index sustained attention and cognitive control, which covary with comprehension and reduced mind wandering during instructional media consumption (Cavanagh & Frank, 2014; Tang et al., 2024). Population studies using wearable EEG in classroom-like conditions show that individualized engagement indices derived from oscillatory features predict performance and can trigger adaptive pacing when attentional stability declines (Apicella et al., 2022). Eye-tracking literature demonstrates that fixation duration, saccadic entropy, and regression patterns correspond to strategy use and split-attention effects in multimedia tasks, with early gaze divergence signaling confusion that precedes accuracy drops (Rayner, 1998; Pachman, 2016). When mapped to instructional decisions, these indices serve as formative predictors: sustained theta and efficient scan paths align with productive struggle, whereas rising complexity measures and scattered gaze anticipate overload, thereby recommending segmentation or modality shifts before errors accumulate (Tang et al., 2024; Liu, 2025). Evidence of temporal precedence strengthens causal interpretation because neural and gaze markers shift prior to behavioral failures, enabling preventive intervention rather than reactive remediation (Apicella et al., 2022; Pachman, 2016). Importantly, effect sizes are modest at the single-feature level, which motivates composite indicators that integrate multiple features to improve sensitivity and specificity for classroom use. This mapping supports the use of neuro and gaze indicators as process-level inputs to learning analytics pipelines that ultimately impact test performance, time-to-mastery, and persistence, consistent with theory-driven ROI logic that ties improved process quality to downstream returns (Rayner, 1998). Table 2. Construct mapping between neural and gaze indices and learning outcomes Feature / Metric Construct alignment Theoretical rationale Expected directionality with learning Typical thresholding Reliability considerations / Notes on validity EEG: Frontal midline theta power (4–7 Hz) Sustained attention; cognitive control Theta increases with executive control and topdown attention during task engagement Positive within-task association up to an optimal range; excessive theta at rest may indicate underarousal Participant-specific baseline plus zscore; rolling window (10–30 s) with control limits Sensitive to eye/muscle artifacts; requires artifact rejection and channel quality checks; individual baselines essential EEG: Alpha power (8–12 Hz) / alpha suppression Visual attention; information Alpha suppression indexes active information Negative association: stronger alpha suppression predicts Relative suppression from baseline during Occipital dominance; affected by drowsiness and eye blinks; maintain Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 312 gating processing and attentional engagement better immediate processing task epochs; percent change thresholds (for example >10–20 percent) consistent lighting and posture EEG: Beta/Alpha ratio (13–30 Hz / 8–12 Hz) Alertness; focused engagement Higher beta relative to alpha aligns with alert, externally focused states Positive association up to comfort bounds; very high values may reflect stress Ratio thresholds derived from baseline distributions (for example upperquartile alerts) Muscle tension can inflate beta; monitor EMG contamination; verify with concurrent gaze stability EEG: Entropy/complexity (multiscale entropy, Lempel–Ziv) Cognitive workload; information integration Signal complexity rises with richer processing until overload Inverted-U: moderate complexity aligns with optimal learning; extremes signal under/overload Task-specific bands and scale factors; flag deviations beyond ±1–2 SD from baseline Requires adequate window length; susceptible to nonstationarity; standardize preprocessing Eye-tracking: Mean fixation duration (ms) Depth of processing; cognitive load Longer fixations reflect deeper processing or difficulty depending on context Nonmonotonic: moderate increases align with productive struggle; excessive indicates overload Contextual bands by media type (text versus diagram); thresholds via IQR per learner Calibrate device; control for font size and viewing distance; remove offscreen fixations Eye-tracking: Saccadic amplitude (degrees) Search strategy; attention allocation Larger saccades indicate exploratory search; smaller reflect local processing Task-dependent: efficient learners show adaptive amplitudes matching layout structure Z-scored within layout regions; alert on sustained extremes across trials Affected by screen size and seating; ensure head stabilization or remotetracking compensation Eye-tracking: Regression rate (backward saccades) Comprehension monitoring; split-attention Frequent regressions signal ambiguity or integration difficulty Negative association with immediate accuracy; early spikes predict upcoming errors Rolling count per 100 words or per screen; alert if above learnerspecific percentile (for example 80th) Language proficiency moderates baseline; segment by text complexity; filter trackloss artifacts Eye-tracking: Gaze dispersion / heatmap entropy Attentional focus; signaling effectiveness High dispersion indicates scattered attention; low dispersion indicates focus on relevant AOIs Negative association with near-term accuracy when dispersion targets irrelevant AOIs AOI-defined entropy thresholds; compare to expert gaze templates where available Define AOIs consistently; validate templates; consider saliency of visuals and cueing Comparative studies show that combining EEG and eye-tracking yields more robust discrimination of attention states than either modality alone, with late fusion approaches outperforming early fusion or unimodal classifiers across internally versus externally directed attention tasks (Vortmann et al., 2022). Recent multimodal workload recognizers that integrate biosignals through attentionenabled neural architectures further improve cross-context generalization, suggesting a path to resilient classroom detectors once trained on diverse conditions (Yu et al., 2025). Field-oriented implementations using dry-electrode systems demonstrate that realtime neurofeedback can be delivered during live instruction, but emphasize the necessity of per-learner baselining to accommodate individual neurophysiological variance and to avoid misclassification for neurodivergent students who deploy alternative strategies (Song et al., 2025). Calibration protocols should include resting-state and task-anchored segments, oculomotor drift correction, and session-to-session normalization to mitigate day effects and sensor placement variability that otherwise degrade reliability (Liu, 2025). Systematic reviews in immersive and screen-based learning contexts converge on a requirement for multimodal feature sets that include oscillatory power, connectivity or complexity metrics, and gaze dispersion measures to detect overload versus productive engagement with acceptable error rates across subjects (Tang et al., 2024). Crosssubject models remain vulnerable to subgroup bias without explicit representation of neurotypes and learning profiles in training data, which argues for stratified sampling and fairness auditing during model development and for human-in-the-loop overrides at runtime (Vortmann et al., 2022; Yu et al., 2025). In practical deployments, late fusion with confidence weighting offers defensible tradeoffs between accuracy and interpretability because modality-specific contributions can be inspected when teachers question an alert or an adaptation. These findings specify the technical and procedural requirements for multimodal neuroadaptive indicators that are accurate, equitable, and auditable in heterogeneous classrooms. Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 313 Feasibility analyses indicate that modern dry-electrode EEG headsets can operate in everyday learning environments with acceptable comfort and setup time, particularly eight-channel form factors that balance signal fidelity and wearability for class periods of 30 to 60 minutes (Real-time dry EEG study, 2021; Senova et al., 2025). Portable systems that deliver online neurofeedback in synchronous teaching contexts show that attention-enhancing adaptations can be computed and displayed with low friction, provided that artifact handling and device hygiene protocols are standardized for repeated classroom use (Song et al., 2025; Senova et al., 2025). Latency constraints for real-time adaptation typically require sub-second end-to-end processing from sensor to dashboard, which motivates edge-first architectures that preprocess streams locally and offload heavier inference to proximal nodes to avoid network jitter (Mohiuddin et al., 2022; Yi et al., 2017; Chen & Ran, 2019). Interoperability with learning platforms is facilitated by standards such as LTI for tool integration and xAPI for event capture, enabling neuroadaptive alerts and content adjustments to be logged alongside traditional telemetry for audit and research (1EdTech, n.d.; eLeaP, 2025). Staffing plans must include educator training on interpretation and actionability, along with technical support for device provisioning, calibration, and maintenance, which typically scales with classroom count and device-to-student ratio. Cost analyses suggest that research-grade open platforms can outfit a classroom at per-unit prices in the hundreds to low thousands of dollars, with headset options in the 500 to 3000 USD range and consumables modest for dry systems, although total cost of ownership must include software, support, and replacement cycles (OpenBCI, 2025a; OpenBCI, 2025b). These feasibility data indicate that pilot-scale deployments are immediately viable and that district-scale rollouts are plausible when paired with edge analytics and standards-based integration that minimize latency and vendor lock-in (Mohiuddin et al., 2022; 1EdTech, n.d.). The principal constraints are organizational capacity and change management rather than raw technical impossibility. When contrasted with outcome-only ROI models, neuroadaptive indicators provide earlier and more granular decision points that can reduce remediation cycles and time-on-task by preempting overload and disengagement, which constitutes a plausible pathway to higher returns per instructional hour and per dollar (Apicella et al., 2022; Pachman, 2016). Traditional models favor summative test gains and completion metrics that materialize weeks or months after instruction, whereas process-sensitive signals generate actionable feedback within minutes, allowing administrators to attribute resource effects to specific moments and designs rather than aggregate programs (Liu, 2025; Tang et al., 2024). However, the added dimensionality introduces interpretation costs that must be offset by clear dashboards and governance that translate neural and gaze indices into teacherfriendly recommendations, otherwise decision utility may be diluted compared to a single composite ROI figure (1EdTech, n.d.; eLeaP, 2025). From a cost perspective, upfront equipment and training outlays increase the investment denominator, but early studies of dry EEG viability and edge architectures suggest that operational expenses can be contained, especially when devices are shared across sections and analytics run on commodity edge servers (Real-time dry EEG study, 2021; Mohiuddin et al., 2022). The Phillips-style ROI methodology remains applicable if organizations monetize avoided remediation, reduced seat time to mastery, and improved retention alongside conventional gains, thereby converting process improvements into financial terms for governance and procurement (ROI Institute, n.d.; CalHR, n.d.). Risk-adjusted comparisons should also account for ethical safeguards and fairness audits as necessary costs that prevent downstream liabilities, which traditional ROI often omits. Overall, the comparative appraisal favors a hybrid accounting in which neuroadaptive process indicators inform day-to-day optimization while conventional ROI validates long-run value, with edgeenabled, standards-compliant infrastructure lowering barriers to adoption and scaling of the neuroadaptive approach (Apicella et al., 2022; 1EdTech, n.d.; Mohiuddin et al., 2022). Discussion Process-sensitive indicators derived from EEG and eye-tracking provide administrators with near-real-time visibility into attentional stability, cognitive load, and emergent confusion, which improves the timeliness of instructional decision making compared with outcome-only ROI that surfaces weeks after instruction (Fredricks et al., 2004; Hattie, 2009). Granular signals enable attribution at the level of lesson segments, media elements, and task transitions, supporting micro-allocations of time, staffing, or scaffolds that would be invisible in aggregated metrics (Koedinger et al., 2012; VanLehn, 2011). Interpretability depends on mapping indices to constructs educators already leverage, such as productive struggle and split-attention, rather than presenting raw spectral power or gaze dispersion without pedagogical semantics (Sweller, 2011; Holmqvist et al., 2022). Dashboards that translate oscillatory and oculomotor features into actionable prompts, for example “segment content” or “switch representation,” increase decision utility and reduce cognitive overhead for teachers tasked with orchestrating complex classrooms (D’Mello, 2013; Luck, 2014). From a governance perspective, timelier evidence allows mid-course correction of programs before sunk costs accumulate, which aligns with continuous improvement logics in educational leadership (Levin & Belfield, 2015; Fitzpatrick et al., 2011). Yet interpretability is a binding constraint: without explanatory models and calibration exemplars, administrators may discount these indicators as opaque or overly technical (Greenhalgh et al., 2017; Holmes et al., 2019). Embedding explanatory tooltips, uncertainty bands, and subgroup breakouts improves transparency and supports equitable use rather than one-size interpretations (Holstein et al., 2019; Slade & Prinsloo, 2013). In sum, the combination of timeliness and granularity augments decision utility when signals are framed through accepted instructional constructs and delivered with usable explanations for frontline educators and leaders (Fredricks et al., 2004; Hattie, 2009). The principal advantage of a process-oriented ROI is earlier detection of friction that precedes failure, which enables targeted allocation of scarce resources such as tutoring minutes, assistive technologies, or teacher attention to the moment and learner most likely to benefit (Baker et al., 2010; VanLehn, 2011). By operationalizing cognitive load and engagement during learning, institutions can shift investment from reactive remediation to preventive adaptation, a reallocation that theory and early evidence suggest reduces time-to-mastery and repeated interventions (Sweller, 2011; Koedinger et al., 2012). Process indices also surface heterogeneity across learners, creating opportunities to fund accommodations that raise returns for students whose strategies diverge from normative patterns, consistent with equityoriented benefit accounting (Meyer et al., 2014; Baker & Hawn, 2021). Risks include construct drift if indices are not rigorously Copyright © ISRG Publishers. All rights Reserved. DOI: 10.5281/zenodo.17799379 314 validated, false positives from artifacts that divert resources without improving outcomes, and over-optimization to short-term engagement at the expense of desirable difficulties that foster longterm retention (Luck, 2014; Schneider et al., 2016). There is also a governance risk that process dashboards become de facto accountability metrics, incentivizing superficial boosts to engagement rather than deep learning, unless balanced with distal outcomes (Hattie, 2009; Levin, 2020). Mitigation requires documented validity chains from signal to construct to outcome, human-in-the-loop review, and safeguards that privilege pedagogical judgment over automated prescriptions (Holmqvist et al., 2022; Greenhalgh et al., 2017). When coupled with fairness auditing and subgroup reporting, process-oriented ROI can support both efficiency and equity while containing the risks of mismeasurement and over-automation (Holstein et al., 2019; Slade & Prinsloo, 2013). Communicating neuroadaptive evidence to diverse audiences demands layered narratives that connect indices to student experience and instructional moves rather than to instrumentation jargon (Holmes et al., 2019; Fitzpatrick et al., 2011). For teachers, exemplar vignettes that show how a spike in regressions during diagram reading led to immediate signaling and improved comprehension translate abstract metrics into practice (Holmqvist et al., 2022; Rayner, 1998). For families and students, privacyforward explanations that emphasize purpose limitation, data minimization, and opt-out options build trust in formative use rather than surveillance (Slade & Prinsloo, 2013; Floridi et al., 2018). For boards and policymakers, hybrid ROI reports that combine conventional outcomes with monetized process benefits such as avoided remediation and reduced seat time create continuity with existing fiscal frameworks while introducing process value (Levin & Belfield, 2015; Phillips & Phillips, 2016). Visualizations should include uncertainty intervals, subgroup disaggregation, and plain-language labels to avoid misinterpretation and to foreground equity impacts (Holstein et al., 2019; Baker & Hawn, 2021). Policy briefs can codify acceptable use, including prohibitions on disciplinary applications, and mandate periodic audits of model performance across learner groups to sustain legitimacy (Floridi et al., 2018; Slade & Prinsloo, 2013). Communication that centers learners and pedagogy, while aligning with established accountability idioms, increases the likelihood that neuroadaptive signals inform rather than distort decision making (Holmes et al., 2019; Levin, 2020). Integration begins by aligning indices with design levers in widely used instructional frameworks so that signals trigger specific, evidence-based adaptations such as segmentation, modality switching, or example-problem alternation (Sweller, 2011; Koedinger et al., 2012). Professional learning should position teachers as co-interpreters who calibrate dashboards to their learners through baseline activities and reflective use, rather than as passive recipients of algorithmic directives (VanLehn, 2011; D’Mello, 2013). At the course level, designers can embed “decision hooks” within LMS activities where alerts can suggest micro-interventions without disrupting flow, for example inserting a self-explanation prompt when gaze dispersion exceeds a threshold (Holmqvist et al., 2022; Holmes et al., 2019). Schoolwide, integration with multi-tiered systems of support allows process indicators to inform tier movement and targeted accommodations in a transparent, auditable manner (Fitzpatrick et al., 2011; Meyer et al., 2014). Technical integration should leverage standards such as LTI and xAPI so that neuroadaptive events appear alongside existing learning analytics, preserving context and enabling cumulative improvement cycles (Yi et al., 2017; Holmes et al., 2019). Importantly, practice guidance must articulate when not to intervene, preserving desirable difficulties and autonomy to avoid over-scaffolding (Schneider et al., 2016; Hattie, 2009). These pathways position neuroadaptive evidence as a pedagogical companion that enhances rather than replaces teacher expertise and principled instructional design (Koedinger et al., 2012; VanLehn, 2011). Current evidence bases remain concentrated in pilot studies and quasi-naturalistic settings, which constrains external validity across diverse curricula, grade bands, and classroom ecologies (Greenhalgh et al., 2018; Daniel, 2015). Durability of effects beyond short intervention windows is under-examined, leaving open whether early gains in engagement translate into sustained achievement and retention over semesters or years (Kizilcec et al., 2017; Hattie, 2009). Population heterogeneity is insufficiently represented, particularly for neurodivergent learners whose attentional signatures and strategies may challenge default classifiers, which risks biased adaptations if models are not trained and audited across profiles (Baker & Hawn, 2021; Meyer et al., 2014). Future work should prioritize ecologically valid, multi-site studies with diverse classrooms, curricula, and instructional styles to test generalization and uncover boundary conditions (Greenhalgh et al., 2017; VanLehn, 2011). These limitations temper claims and highlight the need for cumulative, transparent evidence that links process improvements to long-term outcomes equitably across learner groups (Levin, 2020; Slade & Prinsloo, 2013). Advancing the field requires preregistered trials that specify hypotheses about process-to-outcome mediation, analytic plans for handling missingness and clustering, and thresholds for educational significance prior to data collection (Page et al., 2021; Campbell et al., 2020). Cluster randomized or stepped-wedge designs can balance rigor with practical constraints in schools, while mixedmethods components capture teacher sensemaking and implementation fidelity (Fitzpatrick et al., 2011; Greenhalgh et al., 2017). Longitudinal tracking should relate early process gains to end-of-term performance, subsequent course enrollment, and retention to test durability and to calibrate ROI models that monetize avoided remediation and accelerated mastery (Levin & Belfield, 2015; Phillips & Phillips, 2016). Trials ought to include pre-specified subgroup analyses for neurodivergent learners and other protected categories, with fairness metrics reported alongside accuracy to detect disparate impact (Holstein et al., 2019; Baker & Hawn, 2021). Preregistration and transparent reporting reduce researcher degrees of freedom, improve comparability across studies, and build trust among stakeholders who must make consequential investments (Page et al., 2021; Greenhalgh et al., 2018). Independent evaluations should quantify incremental costeffectiveness ratios that relate added equipment, training, and analytic costs to gains in mastery, retention, or time savings relative to status quo practice (Levin & Belfield, 2015; Levin, 2020). Sensitivity analyses can vary device prices, replacement cycles, and staffing assumptions to test fiscal robustness under realistic budget scenarios (Phillips & Phillips, 2016; Daniel, 2015). Robustness testing should probe model stability under sensor noise, latency variation, and classroom movement, and compare unimodal against multimodal pipelines to justify added complexity