## 3 Methods
### Facial Expression Recognition
The dynamic fluctuations and recovery patterns in engagement scores observed in both cohorts underscore the value of real-time stimulus adaptation in maintaining attention and promoting emotional regulation in neurodivergent individuals. ### Impact Assessment and Statistical Evaluation

To evaluate the impact of BIFL-based adaptations, we conducted a statistical analysis using the following:

* Paired \(t\)-tests (\(t\)(39)) to assess whether the mean difference between pre- and post-intervention conditions was statistically significant. * We used \(p\)-values to estimate the probability that observed differences could have occurred by chance, with \(p<0.05\) considered statistically significant. * Cohen's \(d\) to quantify the effect size, indicating the magnitude of the difference. The effect sizes were interpreted as follows: around 0.2 (small), 0.5 (medium), and above 0.8 (large). Our results are summarized in Figure 14.

Figure 13: Average engagement scores over time for ASD and ADHD groups. ADHD group maintained slightly higher engagement throughout. Peaks suggest optimal alignment of stimulus type and concentration periods. The findings demonstrate significant improvements across cognitive, emotional, and task performance metrics:

* Ease of play: Improved from 4.2 to 4.6 on a 5-point scale (self-reported questionnaire) (\(t(39)=8.94\), \(p<0.001\), \(d=1.41\)). * Task completion time: Decreased by 30%, reflecting improved efficiency. * Negative emotions: Decreased by 20% compared to the baseline in the first week (\(t(39)=8.94\), \(p<0.001\), \(d=1.41\)). * Positive emotional responses: Increased by 25% (\(t(39)=10.54\), \(p<0.001\), \(d=1.67\)). * Memory task performance:
* Improved by 18% for ASD participants (\(t(39)=8.94\), \(p<0.001\), \(d=1.41\)). * Improved by 30% for ADHD participants (\(t(39)=8.94\), \(p<0.001\), \(d=1.41\)). * Reasoning task performance:
* Improved by 22% for ASD participants (\(t(39)=8.94\), \(p<0.001\), \(d=1.41\)). * Improved by 28% for ADHD participants (\(t(39)=8.94\), \(p<0.001\), \(d=1.41\)). * Speed of memory task completion: Increased by 22% (\(t(39)=8.94\), \(p<0.001\), \(d=1.41\)). These findings confirm that BIFL enhances neuro-affective engagement by enabling real-time, personalized stimulus selection tailored to user performance and emotional responses. ## 6 Conclusions and Future Work

This paper introduces a neuro-adaptive framework, Bayesian Immediate Feedback Learning (BIFL), designed to improve cognitive and emotional engagement in neuro-divergent children through multimodal, real-time feedback. By integrating EEG-based concentration assessment, facial emotion recognition, and game performance, the system dynamically selects and delivers customized stimuli--visual, auditory, or textual--during interactive gameplay. Our experimental results involving 40 children (25 with ADHD and 15 with ASD) over a four-week intervention revealed significant improvements: concentration increased by 22.4%, emotional engagement by 24.8%, and task performance by 32.1%. Statistical analysis confirmed the robustness of these gains (\(p<0.001\), [PERSON]'s \(d>1.4\)). Figure 14: Statistical significance of neuro-affective improvements across children. Bars indicate changes for different measures, with positive improvements in blue and reductions in red. Each bar includes \(p\)-value and [PERSON]’s \(d\) effect size, showing enhancements across tasks. Importantly, the ADHD group responded most positively to auditory stimuli, while the ASD group showed increased engagement with structured visual cues. The BIFL framework successfully adapted its stimulus strategy over time, supported by uncertainty-aware weighting of input modalities and real-time engagement scoring. These findings demonstrate that personalized neuro-affective interaction can support cognitive training in diverse developmental profiles and holds promise for scalable therapeutic applications.