Abstract

Artificial intelligence (AI) is increasingly integrated into K–12 educational settings, offering potential benefits for students with dyslexia and the educators who support them. This article examines AI applications through the lens of cognitive science and structured literacy, arguing that AI holds promise when used to enhance access to text, strengthen diagnostic precision, and support instructional responsiveness. When aligned with evidence-based reading instruction, AI tools can reduce barriers to written expression, expand access to grade-level content, and support teachers in implementing targeted interventions. However, effective implementation requires careful alignment with the science of reading, thoughtful professional development, and strong ethical guardrails. Implications for classroom practice, teacher support, and district leadership are discussed.

Dyslexia and Evidence-Based Instruction

Dyslexia is a neurobiological learning difference characterized by difficulties with accurate and/or fluent word recognition, decoding, and spelling (Lyon, Shaywitz, & Shaywitz, 2003). These challenges stem from deficits in phonological processing rather than deficits in intelligence or motivation (Shaywitz, 2020). Research in cognitive science has consistently demonstrated that skilled reading depends on the development of automatic word recognition built through systematic connections between phonology, orthography, and meaning (Ehri, 2014; Seidenberg, 2017).

Students with dyslexia benefit from explicit, systematic, and cumulative instruction in phonemic awareness, phonics, fluency, vocabulary, and comprehension (National Reading Panel, 2000). Structured literacy approaches, such as Orton-Gillingham, emphasize diagnostic teaching, scaffolded practice, and repeated opportunities to build orthographic mapping. AI technologies do not replace these instructional principles; however, they may amplify access, provide targeted practice, and support teachers in analyzing student performance data.

AI Tools Supporting Students with Dyslexia

AI-powered accessibility and literacy tools can reduce barriers that often prevent students with dyslexia from fully engaging with grade-level curriculum.

Text-to-speech tools such as Microsoft Immersive Reader, Kurzweil 3000, and Learning Ally audiobooks allow students to access complex texts while continuing to develop decoding skills. Research suggests that reducing the cognitive load associated with decoding can support students’ comprehension (Snowling & Hulme, 2021).

AI-supported reading tutors such as Amira Learning and adaptive literacy platforms such as Lexia provide structured practice in phonics and fluency while offering immediate corrective feedback. These systems analyze oral reading errors and adjust difficulty levels based on student performance patterns. While such tools cannot replace explicit instruction, they can increase practice opportunities and provide useful progress monitoring data.

AI tools can also support written expression. Students with dyslexia often experience challenges in spelling and transcription, which can prevent them from accurately demonstrating their underlying language comprehension and reasoning abilities (Berninger & Wolf, 2016). Speech-to-text tools (e.g., Google Voice Typing) and AI-assisted writing platforms allow students to express ideas more fluently while teachers continue to provide instruction in sentence construction, morphology, and writing structure.

AI Supporting Teacher Decision-Making

AI technologies can also support teachers by synthesizing assessment data and identifying patterns that may otherwise be difficult to detect. For example, AI systems can analyze decoding assessments, oral reading recordings, and spelling inventories to highlight recurring phoneme–grapheme confusions or fluency patterns.

Generative AI tools may assist teachers in drafting decodable passages aligned with specific phonics patterns, generating cumulative review exercises, or summarizing student progress data. When used thoughtfully, these tools can reduce teacher workload while preserving instructional coherence.

However, AI outputs require careful professional judgment. Teachers must evaluate AI-generated materials and analysis to ensure alignment with structured literacy principles.

Equity and Ethical Considerations

Despite its promise, AI integration raises important ethical and equity considerations. Schools must address disparities in technology access, ensure strong student data privacy protections, and avoid overreliance on automated systems. AI tools should support—not replace—teacher expertise and human relationships in the classroom.

Professional development is critical. Teachers need support in understanding both the science of reading and the capabilities and limitations of AI systems. Without such guidance, technology adoption risks reinforcing ineffective instructional practices rather than improving outcomes for students with dyslexia.

Conclusion

Artificial intelligence offers meaningful opportunities to improve educational access and strengthen instructional support for students with dyslexia. When aligned with structured literacy and grounded in cognitive science, AI tools can enhance accessibility, provide targeted practice, and assist teachers in analyzing student learning data. However, technology alone cannot solve persistent literacy challenges. Effective implementation depends on knowledgeable educators, evidence-based instruction, and thoughtful leadership that prioritizes equity, instructional integrity, and professional judgment.

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References

Berninger, V. W., & Wolf, B. J. (2016). Teaching students with dyslexia and dysgraphia: Lessons from teaching and science. Baltimore, MD: Brookes Publishing.

Ehri, L. C. (2014). Orthographic mapping in the acquisition of sight word reading, spelling memory, and vocabulary learning. Scientific Studies of Reading, 18(1), 5–21.

Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2003). A definition of dyslexia. Annals of Dyslexia, 53, 1–14.

National Reading Panel. (2000). Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction. Washington, DC: National Institute of Child Health and Human Development.

Seidenberg, M. (2017). Language at the speed of sight: How we read, why so many can’t, and what can be done about it. New York, NY: Basic Books.

Shaywitz, S. (2020). Overcoming dyslexia (2nd ed.). New York, NY: Knopf.

Snowling, M. J., & Hulme, C. (2021). Annual research review: Reading disorders revisited—The critical importance of oral language. Journal of Child Psychology and Psychiatry, 62(6), 635–653.