An Introduction to the Special Series on Artificial Intelligence for Students With Learning Disabilities

Abstract

This article introduces and contextualizes four articles in a special series on the use of artificial intelligence (AI) for students with learning disabilities. These four articles explore current issues such as cognitive offloading, AI policies related to learning disabilities, and teaching and learning for students with learning disabilities. This special series aims to provide a broader understanding of AI’s potential, ethical considerations, challenges, and implementation in the field of learning disabilities.

Keywords

artificial intelligence large language model learning disabilities special series

Artificial intelligence (AI) refers to computer systems that have the ability to imitate human cognitive functions (Russell & Norvig, 2021). The first intergovernmental standards for AI were established by the Organisation for Economic Co-operation and Development (OECD) in response to growing interest in the broader societal and policy implications of AI. The OECD provided a formal definition of AI in 2019, and the National Institute of Standards and Technology aligned its own definition with the OECD’s definition in 2023. The OECD updated its definition to reflect rapid technological advancements in AI systems; and AI is now defined as

a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment. (OECD, 2024)

The rapid integration of AI across sectors marks a turning point. In educational settings, AI has already made a notable impact in, for instance, reshaping classroom tasks, enhancing assessment practices, and improving teaching and learning experiences (Baidoo-Anu & Ansah, 2023; Chen et al., 2020; Zafari et al., 2022). As interest in AI continues to grow, many educators and researchers are actively exploring its pedagogical applications in education (Wang et al., 2024), finding that its potential benefits make it an effective teaching and learning tool. Accordingly, AI technology is attracting increasing attention from educators and students, transforming the educational landscape (Luckin & Cukurova, 2019; Walter, 2024). In response to these changes, this special series aims to explore current practices and issues related to the use of AI for students with learning disabilities (LD).

AI Potential and Opportunities in Special Education

In special education, researchers and educators have increasingly found that AI, as a special education technology, can aid in expanding opportunities for students with disabilities (de Freitas et al., 2022; Kohli et al., 2021). Zdravkova et al. (2022) described several goals how AI can support students with disabilities: (a) improved communication, (b) intellectual development, (c) enhanced accessibility, (d) inclusive education, and (e) independent living. In particular, AI-based communication methods, such as augmentative and alternative communication that utilize natural language processing, enhance personalization (e.g., personalized vocabulary sets and communication patterns) and support communication development (e.g., word prediction and linguistic modeling), which improve overall communication efficiency (Farzana et al., 2025). Artificial intelligence tools can also support intellectual development. They can facilitate individualized learning, deliver scaffolded feedback, and support cognitive processes, thereby enabling personalized learning trajectories (Tapalova & Zhiyenbayeva, 2022). AI tools can enhance accessibility to learning by providing individualized intellectual support and reducing cognitive loads. For example, AI-powered assistive technology features (e.g., real-time text-to-speech, language translation, voice typing, and alternative text labeling) can ensure accessible and culturally relevant teaching in class. Furthermore, generative AI tools powered by large language models (e.g., ChatGPT) can aid students in interactions using natural, everyday language (Ekin, 2023; Park & Choo, 2025). In fact, many such AI tools support multimodal communication through text and image inputs, with visual information processed through techniques such as optical character recognition (OCR). Thus, students with less-developed learning strategies and skills, including those with LD, can benefit greatly from AI’s accessibility features (Cain, 2024); and many different AI tools can be used to promote inclusive education and foster greater independence among students with disabilities (Baidoo-Anu & Ansah, 2023; Hussein et al., 2025).

Artificial intelligence can also assist educators in many areas such as designing scaffolded instruction, facilitating personalized learning, making data-informed decisions, and IEP development. Specifically, AI can support the design of scaffolded instruction by enabling educators to provide additional step-by-step guidance and modeling. Utilizing AI tools, educators can easily develop multimedia instructional supports within existing explicit, evidence-based instructional frameworks to clarify complex concepts (Waterfield et al., 2026); they can maximize learning opportunities by using AI to tailor instruction based on students’ strengths, learning needs, preferences, interests, prior knowledge, and goals (Carter et al., 2023; Zhang et al., 2025). Building on this, AI facilitates personalized learning by allowing educators to customize curricular resources, accommodate instructional materials, and adapt response choices for diverse learners (Almuhanna, 2025; Kaswan et al., 2024). For example, educators can create formative assessments with multiple modalities (visual and auditory scaffolds) and an adapted lesson sequence that aligns with students’ readiness and prerequisite knowledge (Center for Applied Special Technology [CAST], 2024). In addition, AI can support educators in adjusting their instructions and interventions in real-time, using data-informed instructional decision-making based on analysis of student performance to providing actionable insights (Bressane et al., 2024). AI tools can also provide significant support in IEP development for special education teachers, who often manage heavy workloads (Coleman & Waterfield, 2026; Goldman et al., 2024).

In practice, special education teachers can utilize AI tools and features to customize their lessons on many different subjects. For example, educators might use AI while teaching reading comprehension in order to personalize their instruction to meet students’ needs. This could include adapting texts and videos as well as generating vocabulary and comprehension questions (e.g., Diffit). In designing adaptive and customizable features for reading materials, teachers can use apps such as Clusive to provide multiple means of representation (e.g., reading with text-to-speech and text simplification) alongside text annotations. In writing instruction, teachers can use AI tools to help students to conceptualize topics for different genres and to guide students in structuring outlines with diagrams and concept maps (e.g., Napkin AI). Then, once the writing process has begun, educators can track students’ writing processes and check grammar errors and plagiarism using AI apps (e.g., Grammarly). In math and science instruction, teachers can use conversational AI to support students’ mathematical reasoning and to provide tutoring that aligns with grade-level content standards (e.g., Khanmigo). Many other immersive programs are disseminated as open or free educational resources that can be used in various fields of instruction. Examples include simulations (e.g., PhET), metaverses (e.g., CoSpaces Edu), augmented reality (e.g., GeoGebra), virtual reality (e.g., Labster), and mixed reality platforms (e.g., Meta Horizon).

Ethical Issues, Challenges, and Implementation of AI

Despite the growing attention on AI and its potential benefits, ethical concerns and challenges remain regarding the use of AI in special education for students with disabilities, including those with LD (Marino et al., 2023). Several researchers have raised important questions about the impact of AI on cognitive processes (Al Zaidy, 2024; Bauer et al., 2025; Lambert & Stevens, 2024). Students with weak learning strategies may use AI in an output-driven way, prioritizing correct or polished responses over engagement in the cognitive processes necessary for understanding. Use of AI primarily to generate output, rather than to learn, may diminish students’ initiative and reduce their cognitive engagement, leading them to rely on surface-level learning and preventing deeper conceptual understanding (Klar, 2025; Stadler et al., 2024). This concern is especially pronounced in students with LD who often exhibit more passive learning tendencies and limited cognitive and metacognitive strategies (Fletcher et al., 2018; Paglialunga & Melogno, 2025).

Many educators and researchers are concerned that AI can reduce opportunities for critical thinking and offload the learning process to AI (Gerlich, 2025). Students with low cognitive and metacognitive skills, including those with LD, may fail to critically evaluate the accuracy, relevance, and potential bias of AI-generated information (Chakraborty et al., 2025). In fact, two decisions from recent case law, William A. versus Clarksville-Montgomery County School System (2025) and Doe versus University of Michigan (2026), stress the importance of the student’s active engagement in the learning process, stating that this engagement cannot be replaced by AI and stressing the need for guidelines on the appropriate use of AI in education. Gerlich (2025) emphasized the importance of epistemic responsibility in AI use, including transparent and replicable use as well as human validation of AI-generated content.

Both national and global policies have emphasized the importance of defining AI’s role in education. For example, the United Nations Educational, Scientific and Cultural Organization (UNESCO, 2022) underscored issues of safety and security of information, as well as the right to privacy, in the use of AI in schools; and UNESCO also stressed the need for social justice, fairness, and non-discrimination across diverse cultural, linguistic, social, and geographical contexts. The White House (2026), in a recent public policy release, emphasized the need to protect all children across all AI platforms due to concerns about privacy issues and services provided. The same public policy release stated that teachers and students are expected to develop and enhance AI literacy and proficiency to integrate AI effectively into teaching and learning (The White House, 2025). A notice from the U.S. Department of Education (2023) highlighted an additional concern: AI systems can disseminate biased information and widen existing educational gaps. The educational data and algorithms used to train large language models may be biased, meaning that they might not accurately represent the characteristics, performance, cultural, or linguistic diversity of school-age populations (U.S. Department of Education, 2023). The existing digital divide among students across various family, community, and school environments is a notable concern (U.S. Department of Education, 2022). Although AI tools can provide options to personalize and customize the learning process for students at different performance levels and response rates, it is important for educators to receive sufficient training and professional development in using and understanding the features of instructional designs embedded within the AI system (Paglialunga & Melogno, 2025) and in the procedures for using each feature.

AI for Students With LD

Students with LD often demonstrate limited working and long-term memory, immature learning strategies, inflexibility in metacognitive strategies, and slower information processing speed compared to their peers without disabilities (Shin & Bryant, 2015). Technology has been an important means of addressing these challenges for decades. A large body of extant research supports the positive effects of technology use for students with LD (Kim & Xin, 2022; Perelmutter et al., 2017; Shin & Park, 2024). However, research focusing specifically on AI remains scarce, and the topic warrants further empirical investigation (Paglialunga & Melogno, 2025). To date, a few syntheses (Paglialunga & Melogno, 2025; Ulaş et al., 2025) exist that review studies on the use of AI for students with LD, and these syntheses suggest potential benefits from the use of AI. However, several of the reviewed studies exhibit risk of bias and methodological weaknesses that limit the generalizability of their findings (Paglialunga & Melogno, 2025). To support the positive effects of AI use for students with LD, further research is needed. Future research may include more rigorous methodologies and longitudinal studies, as well as discussion of advancements in understanding cognitive processes and the development of systematic policies, teacher training, and ethical standards (Paglialunga & Melogno, 2025; Ulaş et al., 2025). In response, this special series includes research and perspectives focusing on the use of AI to support students with LD.

In the first article, Seung and Basham (2026) conceptually examine how generative AI (GenAI) may influence learning through the lens of cognitive offloading. In this conceptual review, the authors discuss the potential benefits of GenAI in reading and writing, such as reducing cognitive load and providing scaffolding, while also emphasizing risks when overreliance limits cognitive engagement, self-regulated learning, and long-term skill development. The authors state that students’ use of GenAI is a value-based decision influenced by many factors (Gilbert, 2024) such as performance goals, task difficulty, academic self-efficacy, and perceptions toward GenAI. Drawing on students’ cognitive and motivational profiles, the paper suggests that students with LD may be especially vulnerable to excessive offloading. Based on the review, Seung and Basham’s paper suggests a conceptual model of value-based GenAI offloading decisions and outcomes for students with LD.

In the second article, written by Shin et al. (2026), the Information and Communication Technology Committee of the Council for Learning Disabilities identified current AI policies in U.S. education for students with LD and proposed actionable recommendations. Of the 12 AI policy documents in education released between 2015 and 2025, only two (National Center for Learning Disabilities, 2024; W.A. v. Clarksville/Montgomery County School System, 2024) specifically addressed LD. These two policy documents uniquely addressed topics such as AI-based student activity monitoring, student-centered technology for dyslexia support, and workplace accommodations and task assignments. The LD policy lacked topics on AI-driven risk assessment, legal risk management, and ethical guidelines. Through Delphi surveys of 17 experts who support and advocate for the rights of individuals with LD, Shin et al. proposed five thematic categories: inclusive and personalized learning; ethics, equity, and inclusion; student empowerment and AI literacy; assessment and research; and educator preparation. Applying both large language model (LLM)-based topic modeling and experts’ validation and ranking of the top 10 policy items important for students with LD, Shin et al. emphasized a pressing need for actionable AI policy recommendations in education for stakeholders, including student empowerment and AI literacy, inclusive and personalized learning, and educator preparation.

In the third article, Goldman et al. (2026) explored the use of ChatGPT by examining instructional handouts on disabilities, including LD, which were prepared by preservice teachers. In this pilot study, the preservice teachers were assigned to either a treatment group, which used ChatGPT, or a control group, which used traditional searches, and the handouts produced by the two groups were evaluated for accuracy using a rubric aligned with the course textbook. The findings showed that handouts created by the treatment group were at least as accurate as handouts created by the control group; and that in some cases, the handouts created by the treatment group were more closely aligned with textbook content than handouts created by the control group. In particular, handouts on the topic of LD which were created by the treatment group met all criteria on the rubric, while those created by the control group met only half of the rubric’s criteria. The participants also reported generally positive perceptions of ChatGPT’s usefulness and feasibility. This study suggests that AI has the potential to support preservice teachers’ knowledge acquisition and organization of disability-related information. However, the study also notes that AI-generated content requires careful review, verification, and contextualization to ensure credibility and appropriate use as an instructional support.

Finally, Price et al. (2026) discussed the use of LLMs (i.e., ChatGPT and Claude) by teachers of mono- and multilingual students with reading-specific LD to aid in generating passages of varying lengths, complexities, genres, and topics. The team compared the quality of third-grade Oral Reading Fluency (ORF) passages generated by LLMs to validated third-grade ORF in English and Spanish. They evaluated the quantitative readability of LLM-generated passages and the conceptual diversity and co-occurrence of themes within them. The team examined text sets (23 texts per set in English; seven per set in Spanish) using Coh-Metrix and Leximancer, and they discovered that passages generated by LLMs demonstrated high variability across languages regardless of prompts. The researchers also noted several significant differences in readability between DIBELS ORF and the LLM-produced ORF, and they suggested that practitioners use an established form of ORF instead of LLM-generated ORF when possible. Finally, the team noted that Claude LLM-based methods supported a greater thematic spread when generating culturally responsive passages across a range of diverse topics, while standardized ORF was more promising for producing more tightly integrated passages with stronger links between co-occurring concepts.

Summary

The four articles in this special series address current issues and potential applications of AI in education for teaching students with LD, with diverse academic, cognitive, cultural, and linguistic needs, across different age groups, and for teachers teaching diverse learners. The intent was to broaden awareness of AI’s potential, ethical issues, challenges, and implementation across the field. We hope these articles will augment transparent conversations about responsible AI use in schools and the community, enhancing current evidence-based practices and differentiated instruction for students with diverse learning needs, including those with LD.

Footnotes

ORCID iDs

Jiyeon Park

Mikyung Shin

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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