Students with dyslexia face persistent challenges in accessing and processing written content, which can compromise learning opportunities and motivation. While recent advances in generative AI have shown promise in education, few systems explicitly address the needs of dyslexic learners. This paper presents DyslexIA, an AI-powered learning assistant that combines text adaptation, readability-aware fine-tuning, and structured scaffolding techniques to support inclusive learning. The system introduces several innovations: customizable interaction modes, fine-tuned models optimized for readability and active voice, and contextual as well as cumulative concept and mind maps. Moreover, DyslexIA embeds reflective scaffolding inspired by the ICAP framework and Improved Vee Questions, guiding learners beyond comprehension toward constructive engagement. Widely used accessibility features such as text-to-speech, speech-to-text, and customizable fonts are also integrated in a unified environment. Preliminary evaluation with about 25 students and educators shows measurable improvements in readability metrics and highlights the usefulness of summarization, concept mapping, and reflective prompts. These findings, although exploratory, suggest that DyslexIA can serve as a communication bridge between students and teachers and as a step toward empowering dyslexic learners through AI-enabled accessible and reflective learning.