Predictive Artificial Intelligence in SEND Systems: Evaluating Early Identification, Risk, and Governance Implications

The application of predictive artificial intelligence (AI) within Special Educational Needs and Disabilities (SEND) systems represents one of the most consequential and underexplored frontiers in contemporary educational data mining. This study investigates the deployment of machine learning algorithms to identify learners who may require additional support, evaluating the reliability, validity, and fairness of such predictive systems within the United Kingdom educational framework. Drawing on a structured literature synthesis spanning 2015–2025, a comparative analysis of seven machine learning algorithms including Random Forest, XGBoost, Support Vector Machine, and Neural Networks was conducted using datasets simulated from real-world SEND contexts. Results demonstrate that ensemble methods, particularly Random Forest and XGBoost, achieve the most consistent cross-validation performance (accuracy 74.3–77.5%), while simpler models offer greater interpretability. However, significant concerns emerge regarding false positive and false negative classification rates across protected demographic groups, with girls, learners from minority ethnic backgrounds, and those with masking conditions systematically underidentified. Governance analysis reveals critical gaps in the UK regulatory landscape, including an absence of mandatory explainability standards and algorithmic fairness requirements specific to SEND AI tools. This study proposes a five-pillar governance framework encompassing legal compliance, transparency, equity, human oversight, and data quality as a basis for the responsible deployment of predictive AI in SEND settings. Findings underscore that algorithmic decision support should complement rather than replace professional judgement, with human-in-the-loop protocols essential to ethical implementation.