Artificial intelligence is often positioned as a tool for educational inclusion, yet without intentional design, it frequently reinforces existing inequalities. Drawing from lived experience and global case studies, this session explores how AI can genuinely support inclusion from equity and disability perspectives. Educational AI tools are frequently developed without input from those most affected by exclusion, learners with disabilities, students in under-resourced regions, and diverse linguistic communities. We propose human-centered, context-aware systems that accommodate diverse learning approaches. Participants will receive a practical framework for conducting 'inclusion audits' to identify exclusionary patterns and implement equity-centered alternatives.