IPA 2.0: Validation of an Interpretable Emotion-Attention Index for Neuro-Adaptive Learning with AI

Adaptive learning systems increasingly rely on multimodal affective computing, yet many pipelines remain difficult to audit and pedagogically justify. We introduce NAILF (Neuro-Adaptive Artificial Intelligent Learning Flow) and formalise IPA 2.0 as an interpretable continuous index integrating affective valence/intensity with attentional activation into a traceable intermediate signal for neuro-adaptive decision-making. Validation follows a two-level strategy. Study A performs a structured simulation over the full emotion–attention space (108 configurations), demonstrating numerical stability and coherent monotonic behaviour under controlled parameterisation. Study B evaluates external validity on the DIPSEER in-the-wild classroom dataset using subject-wise temporal calibration (lag/windowing/smoothing), hold-out evaluation, and explicit anti-leakage auditing. Across evaluable subjects (n = 172), Fisher-z aggregation shows a small but significant association between IPA 2.0 and an external engagement criterion (rglobal = 0.166, 95% CI [0.017, 0.308]). A heterogeneous strong-signal subset (n = 25, reval ≥ 0.50) supports personalised calibration as a core design principle. We discuss practical implications: IPA 2.0 is not a sole predictor, but an auditable signal that can gate, rank, and explain adaptive interventions under real-world noise and label–signal asynchrony.