Designing Inclusive Education With AI-Driven Pedagogies and Learning Analytics

This chapter specifies an equity-optimizing sociotechnical stack for inclusive education that fuses UDL-as-compiled accessibility, capability-expanding pedagogy, equity-constrained adaptive mastery, and interpretability-first learning analytics into a single accountable operating model. It translates justice constructs such as targeted universalism, intersectional parity, epistemic justice, and care ethics into backlog-ready design grammars, contestable AI affordances, and privacy-by-design governance with portability, deletion verification, and recourse pathways. The chapter formalizes low-resource resilience through offline-first architectures, edge inference, and progressive enhancement, while constraining personalization via calibration parity, anti-proxy feature governance, and latency-disciplined time-to-support protocols. Five matrices operationalize theory-to-requirements, use-case safeguards, orchestration heuristics, signal-to-action logic, and implementation roadmaps, enabling auditable inclusion dividends across languages, disabilities, and bandwidth ecologies.