Explainable AI shows positive correlation with cognitive psychology domains in higher education

This meta-analysis examined the relationship between explainable artificial intelligence (XAI) and cognitive psychology domains within higher education settings. The study included 62,000 participants from higher education institutions worldwide, making it a large-scale synthesis of existing research. The primary analysis focused on the correlation between XAI and three principal cognitive psychology domains: cognitive load and neural efficiency, attention and working memory, and metacognition and affective interaction. The comparator was not reported, as this was a correlational meta-analysis rather than a comparative trial.

The main results showed positive pooled correlations across these domains, with effect sizes ranging from r = 0.27 to 0.36 (p < 0.001; 95% CrI 0.27, 0.38). The global hypermean association centered on a positive association (r ≈ 0.32). Consistency across clusters was demonstrated with a mean R of 0.79 (p < 0.001). Model fit diagnostics were satisfactory (CFI = 0.96; RMSEA = 0.042). Moderate heterogeneity was observed (I² ≈ 58%).

Secondary outcomes were not reported. Safety and tolerability were not reported, as this meta-analysis did not involve interventions with adverse events. Limitations were not explicitly listed in the input, but the moderate heterogeneity suggests variability across studies. The correlational nature of the data precludes causal inferences.

Compared to prior research, this meta-analysis provides a comprehensive synthesis of XAI's association with cognitive psychology constructs, supporting its validity in adaptive learning systems. However, no direct comparisons to landmark studies are available from the input.

Methodological limitations include the lack of a comparator group, potential publication bias not assessed, and the moderate heterogeneity. The correlational design means that the observed associations may not reflect causal relationships.

Clinically, these findings suggest that XAI may be a valid tool for enhancing cognitive psychology outcomes in higher education, particularly in adaptive learning systems. However, educators and developers should interpret these correlations cautiously and consider further experimental research to establish causality.

Unanswered questions include the specific mechanisms driving these associations, the impact of different XAI implementations, and whether these correlations translate into improved learning outcomes in real-world settings.