Abstract
Inclusion in recruitment remains a critical challenge in the evolving landscape of work, particularly for individuals with neurodivergent cognitive profiles. This study investigates the potential of adaptive Artificial Intelligence (AI) assessments to foster more inclusive hiring practices for neurodiverse talent. Using thematic analysis and simulation-driven data, the research explores how AI systems can be optimized to support equity, reduce cognitive bias, and enhance candidate-job alignment. Grounded in Adaptive Theory, Evidence-Based Rationale, and the Input-Environment-Output (I-E-O) framework, the study models an AI-powered recruitment pipeline using psychometric clustering and retention data. Results reveal that while AI tools can personalise assessments and improve hiring outcomes, they also risk amplifying bias if not calibrated across diverse cognitive profiles. The study emphasizes the importance of neurodiversity-aware model tuning, participatory design involving neurodivergent individuals, and the integration of fairness metrics such as demographic parity and equal opportunity. The findings contribute practical insights and a replicable framework for advancing inclusive hiring through AI-enabled systems.