Specific learning disabilities (LD) affect more than a third of U.S. special-education students, yet maternal perspectives on diagnosis remain underexplored. Guided by family systems theory, this research surveyed 24 mothers of children with dyslexia, dysgraphia, or dyscalculia and conducted in-depth interviews with nine. Quantitative findings revealed low-to-moderate perceived stress and moderate-to-high self-compassion; higher self-compassion was associated with lower stress. Qualitative analysis identified three themes: (a) emotional and cognitive turbulence for the maternal experience; (b) family experience; and (c) impact of race, culture, and socioeconomic status. The results underscore the need for family-centered approaches in educational and support services, as well as more inclusive policies addressing the needs and strengths of diverse families. The study contributes to the limited literature on mothers’ experiences with LD and suggests directions for future research and intervention.
In this study, we present results of a national survey of 285 secondary special education teachers’ (SETs) literacy instructional time use and the materials and instructional strategies they use for word reading instruction. We report overall results as well as disaggregated results for middle (Grades 6–8) and high school (Grades 9–12) SETs. Participants reported spending more time addressing language comprehension skills than word recognition skills. Of all word recognition skills, they allocated the largest proportion of instructional time to oral reading fluency. SETs endorsed evidence-based practices for word reading instruction with greater frequency than practices without empirical support. Middle school SETs reported spending more time teaching word reading skills and using word reading instructional practices with greater frequency than high school SETs. We discuss these results in the context of prior research on evidence-based practices for word reading instruction.
Many students experience challenges in learning mathematics, including English language learners (ELLs) from diverse cultural and linguistic backgrounds. Although mathematics interventions may demonstrate statistically significant outcomes, assessing social validity allows intervention consumers to evaluate the acceptability and feasibility of the intervention. This literature review examined 11 single-case design studies published between 2004 and 2024 to analyze how studies involving ELLs with learning disabilities or difficulties assessed social validity. In this study, social validity was analyzed across five dimensions: constructs, participants, timing, methods, and discussion of results. Findings suggest the need to establish a shared understanding of social validity constructs, include a broader range of stakeholders in social validity data collection, expand data collection to the pre- and mid-intervention phases, incorporate formal assessment instruments, and provide a more comprehensive discussion of social validity results. Implications for future research and practice are discussed.
This study examined the efficacy of a structured misconception-focused mathematics writing (MW) intervention designed to improve decimal knowledge for fifth-grade students with mathematics difficulties (MD). Forty-eight students with MD were randomly assigned to either a MW intervention ( n = 24) or a business-as-usual (BaU) condition ( n = 24). Across 10 sessions, students engaged in structured MW activities to identify, reason about, and correct decimal misconceptions. Results indicated significantly greater gains in overall MW performance for the intervention group compared with the BaU group. Although students demonstrated larger improvements on procedural MW tasks than on conceptual MW tasks, this pattern diverged at the level of decimal knowledge. The intervention significantly improved conceptual knowledge and decimal vocabulary but did not yield significant gains in procedural knowledge. These findings suggest that misconception-focused MW primarily enhances conceptual understanding by engaging students in structured written reasoning that supports the clarification and revision of misconceptions.
The scoping review adopts a multimodal perspective and aims to map existing empirical studies that examine communicative modes in classroom interactions involving learners with dyslexia. Although research on dyslexia has increasingly emphasized multimodal approaches to support learners’ engagement and cognitive development, limited attention has been paid to how communicative modes are configured within classroom interactions. Drawing on Norris’s Multimodal Mediated Theory, this review conceptualizes classroom practices as mediated actions and identifies learning-related interactions within selected studies on dyslexic learners. Communicative modes were analyzed based on authors’ descriptions of interactional processes. The review includes seven empirical studies published in peer-reviewed resources. The findings offer a lens for future classroom research involving learners with dyslexia and provide important implications for classroom practice.
Accurately measuring high-leverage practices (e.g., HLP 16: explicit instruction) within multitiered system of support (MTSS) instruction is imperative for assessing the efficacy of teacher efforts designed to improve it, yet intricacies associated with assessing such instruction make it difficult. In response to the need for assessments for this purpose, we modified a researcher-developed observation protocol to measure general and special educators’ implementation of HLPs in tiered instruction and explored how teachers implement HLPs across Tiers 1, 2, and 3 using generalizability theory and multifaceted Rasch model analyses. Our results indicate that there was some variability in teaching quality between teachers across tier settings. These findings illustrate how the observation tool and procedures can inform future professional development (PD) research by identifying (a) which HLPs require greater emphasis in PD sessions and (b) how the observation protocol can be improved to better achieve its intended purpose.
Large Language Models (LLM) have the capacity to quickly produce passages of varying lengths, complexity, genres, and topics, which could be useful for teachers of mono- and multilingual students with reading-specific learning disabilities, specifically relating to monitoring oral reading fluency (ORF). This study addressed one primary research question with two sub-questions. Research Question 1: What is the quality of third-grade ORF passages generated by LLMs as compared to validated third-grade ORF in English and Spanish? Research Question 1a: What is the quantitative readability of LLM-generated passages? Research Question 1b: What is the conceptual diversity and co-occurrence of themes in said passages? Analysis was conducted using natural language processing tools, including Coh-Metrix, MultiAzterTest, and Leximancer. Results indicate that readability metrics vary greatly across texts generated by LLMs (ChatGPT, Claude), even with consistent prompting; the use of LLMs to produce ORF passages for progress monitoring and high-stakes decision-making is not recommended at this time.
Despite its importance, research on geometry learning among students with specific learning disabilities remains limited. This study interviewed three students with specific learning disabilities to examine their errors in learning the Pythagorean theorem and to analyze their geometric thinking levels based on the van Hiele model. Errors were categorized as conceptual, procedural, representational, computational, and nonsystematic. Conceptual errors, involving misunderstandings of key terminology, and representational errors, reflecting difficulties in translating verbal descriptions into diagrams, were mainly linked to Level 1 (visualization). Procedural errors, particularly missing steps such as extracting square roots, were associated with Level 2 (analysis). Computational errors involved incorrect calculations, whereas nonsystematic errors included guessing solutions and misreading questions. Despite these challenges, students demonstrated flexible reasoning. The findings identify specific breakdowns in geometric thinking and suggest implications for targeted instructional support.
This pilot study explored how preservice teachers (PSTs) use generative artificial intelligence (AI) to create disability reference handouts by comparing a ChatGPT group to a traditional search group. Thirty preservice general and special educators enrolled in an introductory special education course at a Midwestern U.S. university were assigned to one of two conditions. Handouts were evaluated with a rubric aligned to a widely used textbook, assessing definitions, characteristics, instructional challenges, and strategies. ChatGPT handouts were at least as accurate as traditional ones. Participants reported positive experiences using AI. Findings suggest that AI can support PSTs' knowledge acquisition, warranting further study of specialized AI in preservice education.
Students with and without disabilities increasingly learn to read within multitiered systems of support that provide universal (Tier 1) instruction and targeted (Tier 2) intervention. Researchers have emphasized the importance of evaluating Tier 2 interventions in relation to the quality and coherence of Tier 1 instruction. Although large-scale studies increasingly report implementation fidelity and alignment across tiers, it is unclear to what extent single-case research design (SCRD) studies include such information. The SCRD encompasses a variety of within-subject experiments that are common in special education and have potential benefits for students with or at-risk for learning disabilities. In this systematic review, we examined how SCRD studies of Tier 2 literacy interventions ( k = 9) reported instructional fidelity and alignment across tiers. While most studies documented fidelity for the implemented intervention, none described the nature or quality of Tier 1 instruction. Findings indicate that SCRD research rarely situates Tier 2 outcomes within the context of Tier 1, limiting interpretability and integration with the larger evidence base on reading interventions.
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.
Important articles published in Learning Disability Quarterly (LDQ) during the years 1988 to 1998 were reviewed. Ten articles were identified with citations still occurring to date (2020-2025). These articles focused on strategy instruction, metacognition, fluency training, social skills, risk factors, and identification of children with learning disabilities across the United States. Also discussed during the 1988 to 1998 time were LDQ articles that focused on the limitations of discrepancy criteria, the narrowing of research focus, limitations of the null hypothesis related to intervention research, and the politics of knowledge that permeated the field at that time. The selected articles reviewed, based upon continuing citations in the last 5 years, have continued to yield an intellectual impact on the field of learning disabilities.
The emergence of generative artificial intelligence (GenAI) has introduced new opportunities to support students with learning disabilities (SWLDs) in educational settings. While interest in AI integration in classrooms continues to grow among educators and researchers, concerns persist regarding the potential to bypass essential cognitive processes and undermine learning when students are overreliant on GenAI. This conceptual review examines these tensions through the lens of cognitive offloading, the delegation of cognitive tasks to external tools. It synthesizes research on the cognitive challenges experienced by SWLDs, the benefits of GenAI as a compensatory aid, the risks of excessive cognitive offloading, and the cognitive and motivational factors that shape students' offloading decisions, culminating in an initial conceptual model. The review concludes with implications for future research and practice to support strategic cognitive offloading, highlighting the need to investigate how SWLDs make offloading decisions and how these decisions influence learning in GenAI-supported contexts.
We examined whether children with comorbid reading and mathematics difficulties (RDMD) experience more school, reading, and mathematics anxiety than children with either reading difficulties (RD) or mathematics difficulties (MD). Furthermore, we examined whether attention differences account for these effects. Thirty-three children with RD (51.5% female; Mage = 10.80 years), 35 with MD (60.0% female; Mage = 10.79 years), 37 with comorbid RDMD (45.9% female; Mage = 10.79 years), and 42 chronological-age (CA) controls (64.3% female; Mage = 10.82 years) were assessed on measures of reading, mathematics, general cognitive ability, attention, and school, reading, and mathematics anxiety. The groups did not differ from each other in general cognitive ability, and the RDMD group did not present more severe RDMD than the RD and MD groups, respectively. Analyses of variance revealed that children with RDMD exhibited significantly higher levels of reading and mathematics anxiety compared to the CA controls. The RDMD group also exhibited higher levels in reading anxiety compared to the MD group and mathematics anxiety compared to the RD group. These differences remained significant after controlling for attention. Findings suggest that children with RDMD are at greater risk for both reading and mathematics anxiety, emphasizing the need for targeted, domain-specific interventions.
Educational laws and current standards emphasize fostering conceptual understanding in problem-solving, as well as cultivating higher-order thinking and reasoning skills, with the ultimate goal of developing independent mathematical thinkers. The purpose of this study was to explore the impact of the Please Go Bring Me-COnceptual Model-based Problem-Solving intelligent tutor on word problem-solving skills and multiplicative concept development of students with learning disabilities or difficulties in mathematics. The multiple-probe across-participants design was used to explore the functional relationship between the intervention program and students' problem-solving skills and the development of conceptual knowledge. Concept development was measured by the levels of independence in solving problems provided by the intelligent tutor. Results showed that the tutoring system was effective in promoting students' critical thinking and word problem-solving performance. It seems that features, such as the guided discovery strategy embedded in the tutoring system may have contributed to students' development as an independent problem solver.
The purpose of this study was to identify the current state of artificial intelligence (AI) policies in U.S. education and propose actionable recommendations through large language model-based topic modeling and Delphi surveys. Out of 12 policy documents released between 2015 and 2025, only two documents (National Center for Learning Disabilities, 2024; W.A. v. Clarksville/Montgomery County School System, 2024) specifically addressed learning disabilities. Policy documents addressing topics such as AI-driven risk assessment, data protection, legal risk management, and ethical guidelines covering other disabilities and general AI in education policy were provided as baselines that could be discussed and validated through the following Delphi surveys involving 17 experts from diverse stakeholder groups. A total of 36 policy items across five thematic categories of inclusive and personalized learning (11 items), ethics, equity, and inclusion (nine items), student empowerment and AI literacy (six items), assessment and research (six items), and educator preparation (four items) were proposed. Based on experts' ranking of the top 10 policy items important for students, the most essential policy suggestions include student empowerment and AI literacy.
Response to intervention (RTI) has received attention for its potential to improve special education eligibility practices for emergent bilinguals, who are often disproportionately identified with disabilities. The purpose of this systematic review was to examine the use of RTI as a means of reducing disproportionality in special education for emergent bilinguals. Seven studies met the eligibility criteria. Two studies used experimental/quasi-experimental group designs and reported that RTI was associated with reduced levels of disproportionality for emergent bilinguals. Two studies examining secondary data reported that emergent bilinguals were not disproportionately represented in special education when provided access to RTI but did not provide outcomes for schools in which RTI was not used. Three studies failed to identify evidence of an effect. Two studies reported that RTI was associated with improvements in reading. Results suggest that, although RTI offers benefits for emergent bilinguals in some contexts, its effect on disproportionality in special education is less clear.
This study reviews the literature on error patterns in mathematics among students with mathematics difficulty. We analyzed and synthesized the findings from 17 studies, focusing on the characteristics of error analysis studies, the mathematics topics examined, and the specific error patterns identified. The results revealed the following: (a) the criteria used to identify mathematics difficulties and the coding processes varied; (b) the mathematics topics investigated encompassed fractions (including fraction computation and representation), problem-solving, and general computation; and (c) a variety of common error types were identified across these mathematical domains. Implications for practitioners and researchers were discussed. Keywords error patterns , error analysis , mathematics difficulty , mathematics
While spelling instruction may enhance reading interventions, there is little experimental evidence to date that examines the benefit of integrating spelling activities above and beyond explicit word reading instruction and practice alone. The study sought to investigate whether a common approach to spelling practice, cover-copy-compare (CCC), uniquely contributed to multisyllabic word reading skills of third to fifth grade students with dyslexia ( n = 32). In this brief experiment, students completed two controlled individual sessions (30-min each). Each session included two instructional components and one practice component. The practice component differed by study condition, with students randomized to either reading practice (Decoding condition) or spelling practice using a modified CCC activity (Decoding+Spelling condition). No between-group differences were observed on researcher-developed or standardized word reading and pseudoword reading measures; however, a small but statistically significant effect favored students in the Decoding condition on a standardized measure of word reading efficiency. Findings suggest students in both conditions improved their reading of multisyllabic words, and there did not appear to be a differential benefit of spelling practice via CCC compared with reading practice. We discuss implications for future research on the contribution of spelling practice to word-level reading interventions for students with dyslexia.
Although students are often taught to look for keywords when solving word problems, this strategy is erroneous. It is especially problematic when students solve inconsistent word problems that include a relational term, such as more but are not solved with the assumed operation (e.g., addition). In this study, we analyzed 112 Grade 3 students’ constructed equations on four word problems that included the word more. We compared students with and without mathematics difficulty and disaggregated based on dual-language status. Most students constructed accurate equations for the two consistent word problems, but fewer constructed accurate equations for the two inconsistent word problems. Students with mathematics difficulty, particularly those who were also dual-language learners, had the lowest rates of accurate equations on the inconsistent word problems. This analysis reinforces previous calls by researchers to avoid the ineffective keywords strategy.
