Developmentally Aligned AI Modeling of Mathematics Learning Disability: Behavioral Validation of Neural Learning-Rate Constraints

Developmentally Aligned AI Modeling of Mathematics Learning Disability: Behavioral Validation of Neural Learning-Rate Constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developmentally Aligned AI Modeling of Mathematics Learning Disability: Behavioral Validation of Neural Learning-Rate Constraints John Hite This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8545352/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Developmentally aligned artificial intelligence (AI) emphasizes calibrating AI systems to the distinctive cognitive and neurodevelopmental constraints of children rather than importing assumptions. Biologically grounded "digital twin" models provide another example of this approach. Personalized deep neural network simulations of mathematics learning disability (MLD) indicate that elevated neural gain (hyperexcitability) slows learning while preserving the potential to reach typical accuracy given sufficient training, requiring approximately 2.7 times more training iterations. This model predicts that behavioral dose–response relationships should be conditional: additional instructional hours should matter most for learners at risk for MLD and for outcomes aligned to the practiced skills. These predictions were tested by combining evidence from (a) a reanalysis of an intensive mathematics intervention database ( k = 171 effect sizes, 24 studies), (b) meta-analytic criterion-validity evidence for mathematics curriculum-based measurement ( k = 330), and (c) randomized manipulation of intervention session frequency holding total minutes constant ( N = 101). In Dataset A, dosage–effect size correlations were significant for at-risk samples ( r = .38) but not mixed samples ( r = .05), and were strongest for at-risk samples with skill-aligned outcomes ( r = .52; r = .40 excluding one extreme outlier). Experimental evidence converged: higher session frequency improved a proximal computation measure but not distal standardized outcomes. Together, results support a developmentally aligned learning-rate account of MLD and illustrate how child-calibrated digital twins can generate precise, testable predictions for intervention science. mathematics learning disability developmentally aligned AI neural network learning rate dyscalculia artificial intelligence assessment Figures Figure 1 Introduction Mathematics learning disability (MLD; also referred to as developmental dyscalculia) is a persistent difficulty acquiring foundational numerical and arithmetic skills that can constrain later academic achievement and life outcomes (Geary, 2011; Kosc, 1974). Although MLD has often been framed as a fixed limitation in core numerical representations or "capacity," an alternative account emphasizes a developmental constraint on learning efficiency or "learning rate": children with MLD can in principle reach typical levels of performance, but require more practice to stabilize accurate representations and procedures. MLD is typically identified when a child's mathematics performance is substantially below age or grade expectations and interferes with educational progress despite adequate instruction. In practice, identification often relies on a combination of low achievement, slow growth, and poor response to targeted intervention. This means that MLD is necessarily studied at the interface of neuroscience, cognitive development, and intervention science: a mechanistic account must explain both why some children learn more slowly and why instructional support sometimes succeeds and sometimes fails. A capacity account of MLD posits that children with MLD have a qualitatively impaired numerical representation system (for example, difficulties mapping symbolic numbers to magnitude representations or deficits in core quantity processing). In such accounts, instruction can help children compensate, but the underlying representational ceiling limits achievable performance. A learning-rate account instead proposes that the underlying representations can be acquired, but that the dynamics of acquisition are slower or less stable. From this perspective, persistent underachievement can emerge from a mismatch between the number of learning opportunities provided and the number required to consolidate accurate procedures and concepts. Discriminating between capacity-limited and learning-rate-limited accounts is not merely theoretical. If a large portion of MLD-related underachievement reflects a learning-rate constraint, then intervention effectiveness may depend strongly on how much training is delivered, and on whether outcomes measure the skills that were actually trained. Conversely, if MLD reflects a hard ceiling on attainable performance for certain core skills, then increasing instructional time may yield diminishing returns. Deciding between these accounts therefore has direct implications for the design and evaluation of child-focused interventions. Dose–response research is central to distinguishing these accounts, but it is also methodologically difficult. In educational intervention, "more hours" is rarely randomized: intensity is often selected based on perceived severity, resource constraints, or program structure. Moreover, interventions differ not only in total time but in how time is allocated across sessions (frequency), the spacing of practice, group size, and the adaptivity of instructional feedback. As a result, simple dose–response analyses may obscure conditional patterns that only emerge in specific populations or with specific measurement choices. The emergence of child-centered computational neuroscience offers a new route for distinguishing between these accounts. In the context of this journal, Kurian (2025) introduced the framework of Developmentally Aligned AI, arguing that AI systems intended for or about children should incorporate developmentally specific constraints rather than applying generic adult models. This framework is often discussed in terms of user-facing systems and child–AI interaction, but it also has clear implications for computational models used to explain child learning: an AI model is "developmentally aligned" to the extent that it captures constraints that are specific to children's developing brains and learning systems. A particularly direct application of developmentally aligned AI is the "digital twin" approach: computational models calibrated to the behavior of individual children to infer mechanistic parameters and generate falsifiable predictions. Strock et al. (2025) developed personalized deep neural networks (pDNNs) tuned to each child's behavioral profile on a mathematics task. Their simulations suggested that networks matching children with MLD were characterized by elevated neural gain (hyperexcitability). Critically, these high-gain networks were not incapable of learning. Instead, they required substantially more training iterations—on average 2.7 times more—to reach the same accuracy criterion as networks tuned to typically developing children (Strock et al., 2025). In Strock et al.'s (2025) framework, neural gain modulates the strength of neuronal responses to input, influencing how rapidly a network updates its internal representations during training. Their key result was a dissociation between attainability and efficiency: high-gain networks could reach the same asymptotic accuracy as low-gain networks, but required many more training iterations to do so. This is precisely the pattern expected under a learning-rate constraint and is fundamentally different from a capacity ceiling. The pDNN approach is also developmentally aligned because it is calibrated to children's behavioral profiles rather than imposing an adult reference model. Crucially, digital twin models do not only "explain after the fact." Because gain is a mechanistic parameter that governs learning dynamics, the model yields specific behavioral predictions. If a subset of children require more learning iterations to consolidate skill, then interventions should show stronger returns to increased training opportunities for that subset. Conversely, if a sample includes many children without the constraint, dose–response may weaken as gains become dominated by other factors (e.g., instruction quality, baseline knowledge, or measurement error). This mechanistic account aligns with converging neurobiological evidence that atypical E/I balance and circuit organization may contribute to variability in mathematical learning. For example, children with mathematical disabilities show altered functional connectivity during arithmetic and problem solving, including patterns of hyper-connectivity interpreted as inefficient or compensatory circuitry (Jolles et al., 2016; Rosenberg-Lee et al., 2015). Magnetic resonance spectroscopy work further suggests that glutamate and GABA concentrations in parietal regions implicated in numerical cognition relate to learning and achievement during development (Zacharopoulos et al., 2021). Importantly, these findings do not uniquely determine whether learning limitations are capacity-based or rate-based, but they motivate the possibility that neurodevelopmental constraints operate by changing the efficiency of learning dynamics. The digital twin framework also makes a second prediction about measurement. A learning-rate constraint implies incremental, skill-specific gains: with more practice, trained skills improve gradually. Detecting such learning requires outcomes that are sufficiently aligned to trained content. If an outcome measure aggregates across many skills, requires far transfer, or is otherwise insensitive to incremental learning in the targeted domain, then even genuine learning-rate effects may not be observable. In other words, outcome alignment is not merely a methodological preference; it is part of the mechanistic prediction. If MLD reflects a learning-rate constraint, digital twin simulations yield a clear behavioral pattern for intervention research: the relationship between intervention dose (e.g., hours of instruction) and measured gains should be conditional rather than universal. Specifically, additional training should show the clearest returns in samples enriched for learners who possess the rate constraint (i.e., at-risk or identified MLD samples), and on outcomes that are sufficiently proximal to the trained skills to be sensitive to incremental learning. Recent intervention meta-analytic work underscores both the importance and the ambiguity of dosage effects. In a large synthesis of intensive mathematics interventions for early elementary students with MLD, Miller et al. (2025) reported that outcome proximity moderated observed effects and posed a key unanswered question for the field: "whether intervention response differs depending on whether intervention participants were deemed at-risk for MD at the outset of the intervention" (p. 31). This open question maps directly onto the digital twin prediction that dose–response should be stronger in samples restricted to at-risk learners. The present study tests developmentally aligned digital twin predictions by combining evidence across three levels: (a) a reanalysis of an intensive mathematics intervention database to evaluate conditional dose–response patterns, (b) criterion-validity evidence for curriculum-based measurement (CBM) indicators to evaluate whether the most proximal outcomes used in intervention studies behave as valid indicators of the targeted skill domain, and (c) randomized experimental evidence that manipulates dose structure (session frequency) while holding total instructional time constant. Primary hypotheses were as follows: (1) Dose–response associations will be stronger in samples restricted to at-risk learners than in mixed or inclusive samples; (2) Dose–response associations will be stronger for skill-aligned (proximal) outcomes than for broad achievement (distal) outcomes; and (3) Experimental manipulations of dose structure will show greater effects on proximal outcomes than distal outcomes, consistent with a learning-rate interpretation. Methods Secondary analyses and synthesis were conducted to test whether dose–response patterns in intensive mathematics interventions align with predictions derived from a developmentally aligned digital twin model of MLD. The analytic strategy combined evidence from (a) a meta-analytic intervention database (Dataset A), (b) a meta-analytic criterion validity database for mathematics CBM indicators (Dataset B), and (c) a randomized controlled trial that experimentally manipulated intervention frequency (Dataset C). Dataset A was drawn from the study-level and outcome-level database compiled by Miller et al. (2025) for intensive mathematics interventions in Grades 1–3. The dataset comprised k = 171 effect sizes from 24 studies. Following Miller et al. (2025), effect sizes were classified as derived from (a) at-risk samples, defined as studies in which inclusion criteria required low mathematics performance or risk for mathematics disability (e.g., screening below a percentile threshold), versus (b) mixed/inclusive samples that included both at-risk and typically achieving participants. Outcomes were coded as skill-aligned (proximal) when measures directly sampled the content or skills explicitly taught during intervention (e.g., curriculum-based computation fluency measures, researcher-developed aligned probes). Outcomes were coded as broad achievement (distal) when measures assessed more general mathematics achievement or problem solving that extended beyond the trained skill set (e.g., standardized achievement composites). For each outcome, standardized mean change difference effect sizes were computed using the pretest–posttest–control group design (dPPCI), a form of standardized mean change that contrasts treatment and comparison group gains and standardizes by pretest variability (Becker, 1988). To evaluate whether the most proximal outcomes used in intervention studies function as valid indicators of mathematics achievement, a meta-analytic database examining relations between mathematics CBM indicators and external criterion measures was incorporated ( k = 330; Codding et al., 2025). This synthesis provides evidence on whether commonly used CBM indicators correlate with broader criterion outcomes, supporting their use as developmentally sensitive outcome measures. Findings were drawn from Codding et al. (2016), a randomized controlled trial ( N = 101) that manipulated intervention session frequency (four, two, or one sessions per week) while holding total intervention minutes constant. For Dataset A, dose–response was quantified as the Pearson correlation ( r ) between total intervention hours and dPPCI within strata. Confidence intervals (95%) were computed using Fisher's z transformation, and differences between correlations were evaluated using Fisher's z tests. This study involved secondary analysis of published data; no new data extraction was performed. Results The intensive intervention database comprised k = 171 effect sizes from 24 studies. Median total intervention dose was 16.7 hours (interquartile range: 10.0–22.5; range: 3.1–42.0). Samples were approximately evenly split between at-risk-only ( k = 90) and mixed/inclusive ( k = 81) designs. Consistent with a learning-rate constraint account, dose–response associations were concentrated in at-risk samples. In at-risk-only samples, total intervention hours correlated positively with effect size ( r = .38, 95% CI [0.18, 0.54], p < .001; k = 90). In mixed/inclusive samples, the association was near zero ( r = .05, 95% CI [-0.17, 0.27], p = .658; k = 81). The difference between correlations was significant ( z = 2.22, p = .026). A cluster-robust linear model with outcome alignment indicated a significant hours × alignment interaction ( b = .043, 95% CI [.016, .070], p < .001). Stratifying Dataset A by both risk status and outcome alignment revealed a coherent pattern. The strongest dose–response association emerged for at-risk samples assessed with skill-aligned outcomes ( r = .52, 95% CI [0.29, 0.69], p < .001; k = 53). In the corresponding mixed-sample/skill-aligned stratum, the association was weaker ( r = .13, p = .296; k = 67). The difference between these correlations was significant ( z = 2.37, p = .018). Excluding one extreme outlier in the at-risk/skill-aligned stratum reduced the correlation but did not eliminate it ( r = 0.40, 95% CI [0.14, 0.60], p = .004; k = 52). Across k = 330 effects, mathematics CBM indicators demonstrated substantial correlations with external criterion outcomes (overall r = .57; Codding et al., 2025). In the randomized trial by Codding et al. (2016), increasing intervention frequency (4× weekly) while holding total minutes constant improved performance on the most proximal outcome (M-CBM) relative to lower-frequency conditions, whereas frequency effects were weaker or absent on more distal standardized outcomes. Discussion This study tested whether behavioral intervention patterns align with predictions derived from a developmentally aligned AI "digital twin" model of mathematics learning disability. Across meta-analytic reanalysis, criterion-validity evidence, and experimental manipulation of practice frequency, findings converged on a common conclusion: dose–response is not a uniform property of mathematics intervention, but a conditional signal that is clearest when analyses focus on at-risk learners and outcomes aligned to trained skills. From a developmentally aligned AI perspective, the value of the present analysis is that it treats a mechanistic model as a generator of testable predictions rather than as a post hoc explanation. Conditional dose–response is not an intuitive prediction under a simplistic "more is better" view of intervention. Instead, it follows from a model in which a subset of children require more learning opportunities to reach the same criterion and in which measurement must be aligned to observe those opportunities translating into observable gains. Strock et al.'s (2025) pDNN simulations identified elevated neural gain as a mechanistic parameter that can slow learning while preserving eventual attainability, such that children with MLD may need substantially more training iterations to reach typical performance. The present behavioral analyses are consistent with this mechanism: increased intervention hours were associated with larger gains primarily in at-risk samples, and primarily on measures sensitive to incremental learning in the targeted domain. This pattern is difficult to reconcile with a strict capacity model in which additional training would be expected to yield limited benefit once a ceiling is reached. Instead, results support the interpretation that at least part of MLD-related underachievement reflects slower learning dynamics. The present findings also help reinterpret the common observation that intensive interventions sometimes yield strong proximal effects but weak distal transfer. Under a learning-rate account, proximal measures track the stabilization of trained procedures, whereas distal achievement measures require broader generalization and integration with other skills. The learning-rate account receives independent support from evidence that children with MLD do not exhibit qualitatively distinctive cognitive profiles. Latent profile analyses of intelligence test data consistently find that children with learning disabilities cluster at lower overall levels of performance without showing unique patterns of cognitive strengths and weaknesses that differentiate them from typically developing peers (Watkins & Canivez, 2022). This absence of distinctive profiles is inconsistent with capacity accounts that predict qualitatively different representational systems. Furthermore, decades of research on cognitive profile analysis have failed to demonstrate differential response to profile-matched interventions; children do not benefit more when instruction is tailored to their presumed cognitive "type" (Miciak & Fletcher, 2020; Watkins, 2000). The absence of profile-by-treatment interactions, described by Watkins (2000) as a "shared professional myth," undermines capacity-based arguments for individualized instruction based on cognitive assessment and instead supports a simpler learning-rate model in which all children benefit from more intensive, skill-aligned practice, with at-risk children requiring proportionally more. A dose–response relationship can only be detected when the outcome is sensitive to the specific learning produced by the intervention. Skill-aligned measures are designed to sample the exact skills that the intervention targets (e.g., computation fluency or specific number combinations), making them well suited to detect incremental gains. In contrast, broad achievement composites often require transfer across multiple subskills and may be less sensitive to short-term dose differences. Importantly, the presence of strong criterion validity for CBM indicators (Dataset B) suggests that using proximal outcomes does not inherently sacrifice construct validity; rather, it may represent a developmentally appropriate measurement strategy for capturing learning dynamics. Beyond total time, how practice is distributed may matter for children with rate constraints. The Codding et al. (2016) randomized manipulation of frequency, holding total minutes constant, provides experimental evidence that increasing frequency can improve proximal outcomes, consistent with a distributed practice advantage (Donovan & Radosevich, 1999). Kurian's (2025) developmentally aligned AI framework highlights the importance of embedding child-specific constraints into AI systems. The present work illustrates how this principle can be applied in computational neuroscience and intervention science: a child-calibrated digital twin model generated a testable prediction—conditional dose–response—that was supported across independent evidence streams. Limitations and Future Directions Several limitations should be noted. Dataset A analyses are correlational and are vulnerable to study-level confounding. The proximal–distal coding is necessarily imperfect. Finally, while the convergence with digital twin predictions is suggestive, the present study does not directly measure neural gain or E/I balance, and thus cannot establish a mechanistic chain from neurobiology to behavioral response. Future research should pursue direct tests of the neural mechanisms proposed here. Longitudinal studies that track both neural parameters (e.g., E/I balance via magnetic resonance spectroscopy) and intervention response in the same children would provide stronger evidence for the learning-rate account. Additionally, AI and computational neuroscience approaches appear to offer a promising path forward for developing research-informed understandings of mathematics learning disability—specifically, whether MLD constitutes a distinct neurodevelopmental disorder or is better explained by insufficient instruction, instructional mismatch, or other environmental factors. Developmentally aligned digital twin models that generate falsifiable predictions represent a methodological advance that can help resolve longstanding debates in the field. Conclusion Across meta-analytic reanalysis, criterion-validity synthesis, and experimental evidence, results support the idea that MLD often reflects a learning-rate constraint rather than an immutable capacity ceiling. This work illustrates a bridge linking computational neuroscience to intervention design and evaluation for children with neurodevelopmental learning differences. Declarations Competing interests The author declares no competing interests. Ethics approval This study involved secondary analysis of published/archival data and did not involve new data collection from human participants. Funding No external funding was received for this work. Author Contribution JH was sole author and designed and wrote the study Data Availability Data analyzed in this study were drawn from published sources (Miller et al., 2025; Codding et al., 2016; Codding et al., 2025). References Becker, B. J. (1988). Synthesizing standardized mean-change measures. British Journal of Mathematical and Statistical Psychology, 41 (2), 257–278. https://doi.org/10.1111/j.2044-8317.1988.tb00901.x Codding, R. S., VanDerHeyden, A. M., Martin, R. J., Desai, S., Allard, N., & Perrault, L. (2016). Manipulating treatment dose: Evaluating the frequency of a small group intervention targeting whole number operations. Learning Disabilities Research & Practice, 31 (4), 208–220. https://doi.org/10.1111/ldrp.12120 Codding, R. S., Nelson, G., Kiss, A. J., Shin, J., Goodridge, A., & Hwang, J. (2025). A meta-analysis of the relations between curriculum-based measures in mathematics and criterion measures. School Psychology Review, 54 (3), 275–290. https://doi.org/10.1080/2372966X.2023.2224055 Donovan, J. J., & Radosevich, D. J. (1999). A meta-analytic review of the distribution of practice effect: Now you see it, now you don't. Journal of Applied Psychology, 84 (5), 795–805. https://doi.org/10.1037/0021-9010.84.5.795 Geary, D. C. (2011). Consequences, characteristics, and causes of mathematical learning disabilities and persistent low achievement in mathematics. Journal of Developmental & Behavioral Pediatrics, 32 (3), 250–263. https://doi.org/10.1097/DBP.0b013e318209edef Jolles, D., Ashkenazi, S., Richardson, J., Degnan, A., & Menon, V. (2016). Parietal hyper-connectivity, aberrant brain organization, and circuit-based biomarkers in children with mathematical disabilities. Developmental Science, 19 (4), 613–631. https://doi.org/10.1111/desc.12399 Kosc, L. (1974). Developmental dyscalculia. Journal of Learning Disabilities, 7 (3), 164–177. https://doi.org/10.1177/002221947400700309 Kurian, N. (2025). Developmentally aligned AI: A framework for translating the science of child development into AI design. AI, Brain and Child, 1 , Article 9. https://doi.org/10.1007/s44436-025-00009-z Miciak, J., & Fletcher, J. M. (2020). The critical role of instructional response for identifying dyslexia and other learning disabilities. Journal of Learning Disabilities, 53 (5), 343–353. https://doi.org/10.1177/0022219420906801 Miller, A. H., Espinas, D. R., McNeish, D., & Barnes, M. A. (2025). Dose response in intensive mathematics interventions for early elementary students with mathematics learning disability. Educational Psychology Review, 37 , Article 91. https://doi.org/10.1007/s10648-025-10070-y Rosenberg-Lee, M., Ashkenazi, S., Chen, T., Young, C. B., Geary, D. C., & Menon, V. (2015). Brain hyper-connectivity and operation-specific deficits during arithmetic problem solving in children with developmental dyscalculia. Developmental Science, 18 (3), 351–372. https://doi.org/10.1111/desc.12216 Strock, A., Mistry, P. K., & Menon, V. (2025). Personalized deep neural networks reveal neural mechanisms of learning deficits in math learning disability. Science Advances, 11 , eadq9990. https://doi.org/10.1126/sciadv.adq9990 Watkins, M. W. (2000). Cognitive profile analysis: A shared professional myth. School Psychology Quarterly, 15 (4), 465–479. https://doi.org/10.1037/h0088798 Watkins, M. W., & Canivez, G. L. (2022). Are there cognitive profiles unique to students with learning disabilities? A latent profile analysis of Wechsler Intelligence Scale for Children–Fourth Edition scores. School Psychology Review, 51 (5), 634–646. https://doi.org/10.1080/2372966X.2021.1919923 Zacharopoulos, G., Sella, F., Hartwright, C. E., Emir, U. E., & Cohen Kadosh, R. (2021). Predicting learning and achievement using GABA and glutamate concentrations in human development. PLOS Biology, 19 (7), e3001325. https://doi.org/10.1371/journal.pbio.3001325 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:43:37","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46463,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8545352/v1/b9ba26e1d5874764b51ff81a.html"},{"id":100599556,"identity":"e0dba16e-d50a-4c83-bd85-91aa9b753736","added_by":"auto","created_at":"2026-01-19 14:43:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":280154,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDose–Response Relationship by Risk Status and Outcome Alignment (Dataset A)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eBlue circles = at-risk + skill-aligned (\u003cem\u003ek\u003c/em\u003e = 53, \u003cem\u003er\u003c/em\u003e = .52); light blue squares = at-risk + broad (\u003cem\u003ek\u003c/em\u003e = 37); red triangles = mixed + skill-aligned (\u003cem\u003ek\u003c/em\u003e = 67, \u003cem\u003er\u003c/em\u003e= .13); orange diamonds = mixed + broad (\u003cem\u003ek\u003c/em\u003e = 14). Regression lines shown for skill-aligned outcomes only. Data from Miller et al. (2025).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8545352/v1/de048baa915b653032f88de4.png"},{"id":100746902,"identity":"3c46b092-88b2-4d86-87a1-8bcd270bc3d1","added_by":"auto","created_at":"2026-01-21 03:45:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":596608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8545352/v1/db83260b-f862-4c68-a24a-d6fc0fcbec2e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developmentally Aligned AI Modeling of Mathematics Learning Disability: Behavioral Validation of Neural Learning-Rate Constraints","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMathematics learning disability (MLD; also referred to as developmental dyscalculia) is a persistent difficulty acquiring foundational numerical and arithmetic skills that can constrain later academic achievement and life outcomes (Geary, 2011; Kosc, 1974). Although MLD has often been framed as a fixed limitation in core numerical representations or \"capacity,\" an alternative account emphasizes a developmental constraint on learning efficiency or \"learning rate\": children with MLD can in principle reach typical levels of performance, but require more practice to stabilize accurate representations and procedures.\u003c/p\u003e \u003cp\u003eMLD is typically identified when a child's mathematics performance is substantially below age or grade expectations and interferes with educational progress despite adequate instruction. In practice, identification often relies on a combination of low achievement, slow growth, and poor response to targeted intervention. This means that MLD is necessarily studied at the interface of neuroscience, cognitive development, and intervention science: a mechanistic account must explain both why some children learn more slowly and why instructional support sometimes succeeds and sometimes fails.\u003c/p\u003e \u003cp\u003eA capacity account of MLD posits that children with MLD have a qualitatively impaired numerical representation system (for example, difficulties mapping symbolic numbers to magnitude representations or deficits in core quantity processing). In such accounts, instruction can help children compensate, but the underlying representational ceiling limits achievable performance. A learning-rate account instead proposes that the underlying representations can be acquired, but that the dynamics of acquisition are slower or less stable. From this perspective, persistent underachievement can emerge from a mismatch between the number of learning opportunities provided and the number required to consolidate accurate procedures and concepts.\u003c/p\u003e \u003cp\u003eDiscriminating between capacity-limited and learning-rate-limited accounts is not merely theoretical. If a large portion of MLD-related underachievement reflects a learning-rate constraint, then intervention effectiveness may depend strongly on how much training is delivered, and on whether outcomes measure the skills that were actually trained. Conversely, if MLD reflects a hard ceiling on attainable performance for certain core skills, then increasing instructional time may yield diminishing returns. Deciding between these accounts therefore has direct implications for the design and evaluation of child-focused interventions.\u003c/p\u003e \u003cp\u003eDose\u0026ndash;response research is central to distinguishing these accounts, but it is also methodologically difficult. In educational intervention, \"more hours\" is rarely randomized: intensity is often selected based on perceived severity, resource constraints, or program structure. Moreover, interventions differ not only in total time but in how time is allocated across sessions (frequency), the spacing of practice, group size, and the adaptivity of instructional feedback. As a result, simple dose\u0026ndash;response analyses may obscure conditional patterns that only emerge in specific populations or with specific measurement choices.\u003c/p\u003e \u003cp\u003eThe emergence of child-centered computational neuroscience offers a new route for distinguishing between these accounts. In the context of this journal, Kurian (2025) introduced the framework of Developmentally Aligned AI, arguing that AI systems intended for or about children should incorporate developmentally specific constraints rather than applying generic adult models. This framework is often discussed in terms of user-facing systems and child\u0026ndash;AI interaction, but it also has clear implications for computational models used to explain child learning: an AI model is \"developmentally aligned\" to the extent that it captures constraints that are specific to children's developing brains and learning systems.\u003c/p\u003e \u003cp\u003eA particularly direct application of developmentally aligned AI is the \"digital twin\" approach: computational models calibrated to the behavior of individual children to infer mechanistic parameters and generate falsifiable predictions. Strock et al. (2025) developed personalized deep neural networks (pDNNs) tuned to each child's behavioral profile on a mathematics task. Their simulations suggested that networks matching children with MLD were characterized by elevated neural gain (hyperexcitability). Critically, these high-gain networks were not incapable of learning. Instead, they required substantially more training iterations\u0026mdash;on average 2.7 times more\u0026mdash;to reach the same accuracy criterion as networks tuned to typically developing children (Strock et al., 2025).\u003c/p\u003e \u003cp\u003eIn Strock et al.'s (2025) framework, neural gain modulates the strength of neuronal responses to input, influencing how rapidly a network updates its internal representations during training. Their key result was a dissociation between attainability and efficiency: high-gain networks could reach the same asymptotic accuracy as low-gain networks, but required many more training iterations to do so. This is precisely the pattern expected under a learning-rate constraint and is fundamentally different from a capacity ceiling. The pDNN approach is also developmentally aligned because it is calibrated to children's behavioral profiles rather than imposing an adult reference model.\u003c/p\u003e \u003cp\u003eCrucially, digital twin models do not only \"explain after the fact.\" Because gain is a mechanistic parameter that governs learning dynamics, the model yields specific behavioral predictions. If a subset of children require more learning iterations to consolidate skill, then interventions should show stronger returns to increased training opportunities for that subset. Conversely, if a sample includes many children without the constraint, dose\u0026ndash;response may weaken as gains become dominated by other factors (e.g., instruction quality, baseline knowledge, or measurement error).\u003c/p\u003e \u003cp\u003eThis mechanistic account aligns with converging neurobiological evidence that atypical E/I balance and circuit organization may contribute to variability in mathematical learning. For example, children with mathematical disabilities show altered functional connectivity during arithmetic and problem solving, including patterns of hyper-connectivity interpreted as inefficient or compensatory circuitry (Jolles et al., 2016; Rosenberg-Lee et al., 2015). Magnetic resonance spectroscopy work further suggests that glutamate and GABA concentrations in parietal regions implicated in numerical cognition relate to learning and achievement during development (Zacharopoulos et al., 2021). Importantly, these findings do not uniquely determine whether learning limitations are capacity-based or rate-based, but they motivate the possibility that neurodevelopmental constraints operate by changing the efficiency of learning dynamics.\u003c/p\u003e \u003cp\u003eThe digital twin framework also makes a second prediction about measurement. A learning-rate constraint implies incremental, skill-specific gains: with more practice, trained skills improve gradually. Detecting such learning requires outcomes that are sufficiently aligned to trained content. If an outcome measure aggregates across many skills, requires far transfer, or is otherwise insensitive to incremental learning in the targeted domain, then even genuine learning-rate effects may not be observable. In other words, outcome alignment is not merely a methodological preference; it is part of the mechanistic prediction.\u003c/p\u003e \u003cp\u003eIf MLD reflects a learning-rate constraint, digital twin simulations yield a clear behavioral pattern for intervention research: the relationship between intervention dose (e.g., hours of instruction) and measured gains should be conditional rather than universal. Specifically, additional training should show the clearest returns in samples enriched for learners who possess the rate constraint (i.e., at-risk or identified MLD samples), and on outcomes that are sufficiently proximal to the trained skills to be sensitive to incremental learning.\u003c/p\u003e \u003cp\u003eRecent intervention meta-analytic work underscores both the importance and the ambiguity of dosage effects. In a large synthesis of intensive mathematics interventions for early elementary students with MLD, Miller et al. (2025) reported that outcome proximity moderated observed effects and posed a key unanswered question for the field: \"whether intervention response differs depending on whether intervention participants were deemed at-risk for MD at the outset of the intervention\" (p. 31). This open question maps directly onto the digital twin prediction that dose\u0026ndash;response should be stronger in samples restricted to at-risk learners.\u003c/p\u003e \u003cp\u003eThe present study tests developmentally aligned digital twin predictions by combining evidence across three levels: (a) a reanalysis of an intensive mathematics intervention database to evaluate conditional dose\u0026ndash;response patterns, (b) criterion-validity evidence for curriculum-based measurement (CBM) indicators to evaluate whether the most proximal outcomes used in intervention studies behave as valid indicators of the targeted skill domain, and (c) randomized experimental evidence that manipulates dose structure (session frequency) while holding total instructional time constant. Primary hypotheses were as follows: (1) Dose\u0026ndash;response associations will be stronger in samples restricted to at-risk learners than in mixed or inclusive samples; (2) Dose\u0026ndash;response associations will be stronger for skill-aligned (proximal) outcomes than for broad achievement (distal) outcomes; and (3) Experimental manipulations of dose structure will show greater effects on proximal outcomes than distal outcomes, consistent with a learning-rate interpretation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eSecondary analyses and synthesis were conducted to test whether dose\u0026ndash;response patterns in intensive mathematics interventions align with predictions derived from a developmentally aligned digital twin model of MLD. The analytic strategy combined evidence from (a) a meta-analytic intervention database (Dataset A), (b) a meta-analytic criterion validity database for mathematics CBM indicators (Dataset B), and (c) a randomized controlled trial that experimentally manipulated intervention frequency (Dataset C).\u003c/p\u003e \u003cp\u003eDataset A was drawn from the study-level and outcome-level database compiled by Miller et al. (2025) for intensive mathematics interventions in Grades 1\u0026ndash;3. The dataset comprised \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;171 effect sizes from 24 studies. Following Miller et al. (2025), effect sizes were classified as derived from (a) at-risk samples, defined as studies in which inclusion criteria required low mathematics performance or risk for mathematics disability (e.g., screening below a percentile threshold), versus (b) mixed/inclusive samples that included both at-risk and typically achieving participants. Outcomes were coded as skill-aligned (proximal) when measures directly sampled the content or skills explicitly taught during intervention (e.g., curriculum-based computation fluency measures, researcher-developed aligned probes). Outcomes were coded as broad achievement (distal) when measures assessed more general mathematics achievement or problem solving that extended beyond the trained skill set (e.g., standardized achievement composites). For each outcome, standardized mean change difference effect sizes were computed using the pretest\u0026ndash;posttest\u0026ndash;control group design (dPPCI), a form of standardized mean change that contrasts treatment and comparison group gains and standardizes by pretest variability (Becker, 1988).\u003c/p\u003e \u003cp\u003eTo evaluate whether the most proximal outcomes used in intervention studies function as valid indicators of mathematics achievement, a meta-analytic database examining relations between mathematics CBM indicators and external criterion measures was incorporated (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;330; Codding et al., 2025). This synthesis provides evidence on whether commonly used CBM indicators correlate with broader criterion outcomes, supporting their use as developmentally sensitive outcome measures.\u003c/p\u003e \u003cp\u003eFindings were drawn from Codding et al. (2016), a randomized controlled trial (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;101) that manipulated intervention session frequency (four, two, or one sessions per week) while holding total intervention minutes constant.\u003c/p\u003e \u003cp\u003eFor Dataset A, dose\u0026ndash;response was quantified as the Pearson correlation (\u003cem\u003er\u003c/em\u003e) between total intervention hours and dPPCI within strata. Confidence intervals (95%) were computed using Fisher's \u003cem\u003ez\u003c/em\u003e transformation, and differences between correlations were evaluated using Fisher's \u003cem\u003ez\u003c/em\u003e tests. This study involved secondary analysis of published data; no new data extraction was performed.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe intensive intervention database comprised \u003cem\u003ek\u003c/em\u003e = 171 effect sizes from 24 studies. Median total intervention dose was 16.7 hours (interquartile range: 10.0\u0026ndash;22.5; range: 3.1\u0026ndash;42.0). Samples were approximately evenly split between at-risk-only (\u003cem\u003ek\u003c/em\u003e = 90) and mixed/inclusive (\u003cem\u003ek\u003c/em\u003e = 81) designs.\u003c/p\u003e\n\u003cp\u003eConsistent with a learning-rate constraint account, dose\u0026ndash;response associations were concentrated in at-risk samples. In at-risk-only samples, total intervention hours correlated positively with effect size (\u003cem\u003er\u003c/em\u003e = .38, 95% CI [0.18, 0.54], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003ek\u003c/em\u003e = 90). In mixed/inclusive samples, the association was near zero (\u003cem\u003er\u003c/em\u003e = .05, 95% CI [-0.17, 0.27], \u003cem\u003ep\u003c/em\u003e = .658; \u003cem\u003ek\u003c/em\u003e = 81). The difference between correlations was significant (\u003cem\u003ez\u003c/em\u003e = 2.22, \u003cem\u003ep\u003c/em\u003e = .026).\u003c/p\u003e\n\u003cp\u003eA cluster-robust linear model with outcome alignment indicated a significant hours \u0026times; alignment interaction (\u003cem\u003eb\u003c/em\u003e = .043, 95% CI [.016, .070], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Stratifying Dataset A by both risk status and outcome alignment revealed a coherent pattern. The strongest dose\u0026ndash;response association emerged for at-risk samples assessed with skill-aligned outcomes (\u003cem\u003er\u003c/em\u003e = .52, 95% CI [0.29, 0.69], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003ek\u003c/em\u003e = 53). In the corresponding mixed-sample/skill-aligned stratum, the association was weaker (\u003cem\u003er\u003c/em\u003e = .13, \u003cem\u003ep\u003c/em\u003e = .296; \u003cem\u003ek\u003c/em\u003e = 67). The difference between these correlations was significant (\u003cem\u003ez\u003c/em\u003e = 2.37, \u003cem\u003ep\u003c/em\u003e = .018). Excluding one extreme outlier in the at-risk/skill-aligned stratum reduced the correlation but did not eliminate it (\u003cem\u003er\u003c/em\u003e = 0.40, 95% CI [0.14, 0.60], \u003cem\u003ep\u003c/em\u003e = .004; \u003cem\u003ek\u003c/em\u003e = 52).\u003c/p\u003e\n\u003cp\u003eAcross \u003cem\u003ek\u003c/em\u003e = 330 effects, mathematics CBM indicators demonstrated substantial correlations with external criterion outcomes (overall \u003cem\u003er\u003c/em\u003e = .57; Codding et al., 2025). In the randomized trial by Codding et al. (2016), increasing intervention frequency (4\u0026times; weekly) while holding total minutes constant improved performance on the most proximal outcome (M-CBM) relative to lower-frequency conditions, whereas frequency effects were weaker or absent on more distal standardized outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study tested whether behavioral intervention patterns align with predictions derived from a developmentally aligned AI \"digital twin\" model of mathematics learning disability. Across meta-analytic reanalysis, criterion-validity evidence, and experimental manipulation of practice frequency, findings converged on a common conclusion: dose\u0026ndash;response is not a uniform property of mathematics intervention, but a conditional signal that is clearest when analyses focus on at-risk learners and outcomes aligned to trained skills.\u003c/p\u003e \u003cp\u003eFrom a developmentally aligned AI perspective, the value of the present analysis is that it treats a mechanistic model as a generator of testable predictions rather than as a post hoc explanation. Conditional dose\u0026ndash;response is not an intuitive prediction under a simplistic \"more is better\" view of intervention. Instead, it follows from a model in which a subset of children require more learning opportunities to reach the same criterion and in which measurement must be aligned to observe those opportunities translating into observable gains.\u003c/p\u003e \u003cp\u003eStrock et al.'s (2025) pDNN simulations identified elevated neural gain as a mechanistic parameter that can slow learning while preserving eventual attainability, such that children with MLD may need substantially more training iterations to reach typical performance. The present behavioral analyses are consistent with this mechanism: increased intervention hours were associated with larger gains primarily in at-risk samples, and primarily on measures sensitive to incremental learning in the targeted domain. This pattern is difficult to reconcile with a strict capacity model in which additional training would be expected to yield limited benefit once a ceiling is reached. Instead, results support the interpretation that at least part of MLD-related underachievement reflects slower learning dynamics.\u003c/p\u003e \u003cp\u003eThe present findings also help reinterpret the common observation that intensive interventions sometimes yield strong proximal effects but weak distal transfer. Under a learning-rate account, proximal measures track the stabilization of trained procedures, whereas distal achievement measures require broader generalization and integration with other skills.\u003c/p\u003e \u003cp\u003eThe learning-rate account receives independent support from evidence that children with MLD do not exhibit qualitatively distinctive cognitive profiles. Latent profile analyses of intelligence test data consistently find that children with learning disabilities cluster at lower overall levels of performance without showing unique patterns of cognitive strengths and weaknesses that differentiate them from typically developing peers (Watkins \u0026amp; Canivez, 2022). This absence of distinctive profiles is inconsistent with capacity accounts that predict qualitatively different representational systems. Furthermore, decades of research on cognitive profile analysis have failed to demonstrate differential response to profile-matched interventions; children do not benefit more when instruction is tailored to their presumed cognitive \"type\" (Miciak \u0026amp; Fletcher, 2020; Watkins, 2000). The absence of profile-by-treatment interactions, described by Watkins (2000) as a \"shared professional myth,\" undermines capacity-based arguments for individualized instruction based on cognitive assessment and instead supports a simpler learning-rate model in which all children benefit from more intensive, skill-aligned practice, with at-risk children requiring proportionally more.\u003c/p\u003e \u003cp\u003eA dose\u0026ndash;response relationship can only be detected when the outcome is sensitive to the specific learning produced by the intervention. Skill-aligned measures are designed to sample the exact skills that the intervention targets (e.g., computation fluency or specific number combinations), making them well suited to detect incremental gains. In contrast, broad achievement composites often require transfer across multiple subskills and may be less sensitive to short-term dose differences. Importantly, the presence of strong criterion validity for CBM indicators (Dataset B) suggests that using proximal outcomes does not inherently sacrifice construct validity; rather, it may represent a developmentally appropriate measurement strategy for capturing learning dynamics.\u003c/p\u003e \u003cp\u003eBeyond total time, how practice is distributed may matter for children with rate constraints. The Codding et al. (2016) randomized manipulation of frequency, holding total minutes constant, provides experimental evidence that increasing frequency can improve proximal outcomes, consistent with a distributed practice advantage (Donovan \u0026amp; Radosevich, 1999).\u003c/p\u003e \u003cp\u003eKurian's (2025) developmentally aligned AI framework highlights the importance of embedding child-specific constraints into AI systems. The present work illustrates how this principle can be applied in computational neuroscience and intervention science: a child-calibrated digital twin model generated a testable prediction\u0026mdash;conditional dose\u0026ndash;response\u0026mdash;that was supported across independent evidence streams.\u003c/p\u003e\n\u003ch3\u003eLimitations and Future Directions\u003c/h3\u003e\n\u003cp\u003eSeveral limitations should be noted. Dataset A analyses are correlational and are vulnerable to study-level confounding. The proximal\u0026ndash;distal coding is necessarily imperfect. Finally, while the convergence with digital twin predictions is suggestive, the present study does not directly measure neural gain or E/I balance, and thus cannot establish a mechanistic chain from neurobiology to behavioral response.\u003c/p\u003e \u003cp\u003eFuture research should pursue direct tests of the neural mechanisms proposed here. Longitudinal studies that track both neural parameters (e.g., E/I balance via magnetic resonance spectroscopy) and intervention response in the same children would provide stronger evidence for the learning-rate account. Additionally, AI and computational neuroscience approaches appear to offer a promising path forward for developing research-informed understandings of mathematics learning disability\u0026mdash;specifically, whether MLD constitutes a distinct neurodevelopmental disorder or is better explained by insufficient instruction, instructional mismatch, or other environmental factors. Developmentally aligned digital twin models that generate falsifiable predictions represent a methodological advance that can help resolve longstanding debates in the field.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAcross meta-analytic reanalysis, criterion-validity synthesis, and experimental evidence, results support the idea that MLD often reflects a learning-rate constraint rather than an immutable capacity ceiling. This work illustrates a bridge linking computational neuroscience to intervention design and evaluation for children with neurodevelopmental learning differences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eThis study involved secondary analysis of published/archival data and did not involve new data collection from human participants.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo external funding was received for this work.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJH was sole author and designed and wrote the study\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData analyzed in this study were drawn from published sources (Miller et al., 2025; Codding et al., 2016; Codding et al., 2025).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBecker, B. J. (1988). Synthesizing standardized mean-change measures. \u003cem\u003eBritish Journal of Mathematical and Statistical Psychology, 41\u003c/em\u003e(2), 257\u0026ndash;278. https://doi.org/10.1111/j.2044-8317.1988.tb00901.x\u003c/li\u003e\n \u003cli\u003eCodding, R. S., VanDerHeyden, A. M., Martin, R. J., Desai, S., Allard, N., \u0026amp; Perrault, L. (2016). Manipulating treatment dose: Evaluating the frequency of a small group intervention targeting whole number operations. \u003cem\u003eLearning Disabilities Research \u0026amp; Practice, 31\u003c/em\u003e(4), 208\u0026ndash;220. https://doi.org/10.1111/ldrp.12120\u003c/li\u003e\n \u003cli\u003eCodding, R. S., Nelson, G., Kiss, A. J., Shin, J., Goodridge, A., \u0026amp; Hwang, J. (2025). A meta-analysis of the relations between curriculum-based measures in mathematics and criterion measures. \u003cem\u003eSchool Psychology Review, 54\u003c/em\u003e(3), 275\u0026ndash;290. https://doi.org/10.1080/2372966X.2023.2224055\u003c/li\u003e\n \u003cli\u003eDonovan, J. J., \u0026amp; Radosevich, D. J. (1999). A meta-analytic review of the distribution of practice effect: Now you see it, now you don\u0026apos;t. \u003cem\u003eJournal of Applied Psychology, 84\u003c/em\u003e(5), 795\u0026ndash;805. https://doi.org/10.1037/0021-9010.84.5.795\u003c/li\u003e\n \u003cli\u003eGeary, D. C. (2011). Consequences, characteristics, and causes of mathematical learning disabilities and persistent low achievement in mathematics. \u003cem\u003eJournal of Developmental \u0026amp; Behavioral Pediatrics, 32\u003c/em\u003e(3), 250\u0026ndash;263. https://doi.org/10.1097/DBP.0b013e318209edef\u003c/li\u003e\n \u003cli\u003eJolles, D., Ashkenazi, S., Richardson, J., Degnan, A., \u0026amp; Menon, V. (2016). Parietal hyper-connectivity, aberrant brain organization, and circuit-based biomarkers in children with mathematical disabilities. \u003cem\u003eDevelopmental Science, 19\u003c/em\u003e(4), 613\u0026ndash;631. https://doi.org/10.1111/desc.12399\u003c/li\u003e\n \u003cli\u003eKosc, L. (1974). Developmental dyscalculia. \u003cem\u003eJournal of Learning Disabilities, 7\u003c/em\u003e(3), 164\u0026ndash;177. https://doi.org/10.1177/002221947400700309\u003c/li\u003e\n \u003cli\u003eKurian, N. (2025). Developmentally aligned AI: A framework for translating the science of child development into AI design. \u003cem\u003eAI, Brain and Child, 1\u003c/em\u003e, Article 9. https://doi.org/10.1007/s44436-025-00009-z\u003c/li\u003e\n \u003cli\u003eMiciak, J., \u0026amp; Fletcher, J. M. (2020). The critical role of instructional response for identifying dyslexia and other learning disabilities. \u003cem\u003eJournal of Learning Disabilities, 53\u003c/em\u003e(5), 343\u0026ndash;353. https://doi.org/10.1177/0022219420906801\u003c/li\u003e\n \u003cli\u003eMiller, A. H., Espinas, D. R., McNeish, D., \u0026amp; Barnes, M. A. (2025). Dose response in intensive mathematics interventions for early elementary students with mathematics learning disability. \u003cem\u003eEducational Psychology Review, 37\u003c/em\u003e, Article 91. https://doi.org/10.1007/s10648-025-10070-y\u003c/li\u003e\n \u003cli\u003eRosenberg-Lee, M., Ashkenazi, S., Chen, T., Young, C. B., Geary, D. C., \u0026amp; Menon, V. (2015). Brain hyper-connectivity and operation-specific deficits during arithmetic problem solving in children with developmental dyscalculia. \u003cem\u003eDevelopmental Science, 18\u003c/em\u003e(3), 351\u0026ndash;372. https://doi.org/10.1111/desc.12216\u003c/li\u003e\n \u003cli\u003eStrock, A., Mistry, P. K., \u0026amp; Menon, V. (2025). Personalized deep neural networks reveal neural mechanisms of learning deficits in math learning disability. \u003cem\u003eScience Advances, 11\u003c/em\u003e, eadq9990. https://doi.org/10.1126/sciadv.adq9990\u003c/li\u003e\n \u003cli\u003eWatkins, M. W. (2000). Cognitive profile analysis: A shared professional myth. \u003cem\u003eSchool Psychology Quarterly, 15\u003c/em\u003e(4), 465\u0026ndash;479. https://doi.org/10.1037/h0088798\u003c/li\u003e\n \u003cli\u003eWatkins, M. W., \u0026amp; Canivez, G. L. (2022). Are there cognitive profiles unique to students with learning disabilities? A latent profile analysis of Wechsler Intelligence Scale for Children\u0026ndash;Fourth Edition scores. \u003cem\u003eSchool Psychology Review, 51\u003c/em\u003e(5), 634\u0026ndash;646. https://doi.org/10.1080/2372966X.2021.1919923\u003c/li\u003e\n \u003cli\u003eZacharopoulos, G., Sella, F., Hartwright, C. E., Emir, U. E., \u0026amp; Cohen Kadosh, R. (2021). Predicting learning and achievement using GABA and glutamate concentrations in human development. \u003cem\u003ePLOS Biology, 19\u003c/em\u003e(7), e3001325. https://doi.org/10.1371/journal.pbio.3001325\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mathematics learning disability, developmentally aligned AI, neural network, learning rate, dyscalculia, artificial intelligence assessment","lastPublishedDoi":"10.21203/rs.3.rs-8545352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8545352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDevelopmentally aligned artificial intelligence (AI) emphasizes calibrating AI systems to the distinctive cognitive and neurodevelopmental constraints of children rather than importing assumptions. Biologically grounded \"digital twin\" models provide another example of this approach. Personalized deep neural network simulations of mathematics learning disability (MLD) indicate that elevated neural gain (hyperexcitability) slows learning while preserving the potential to reach typical accuracy given sufficient training, requiring approximately 2.7 times more training iterations. This model predicts that behavioral dose\u0026ndash;response relationships should be conditional: additional instructional hours should matter most for learners at risk for MLD and for outcomes aligned to the practiced skills. These predictions were tested by combining evidence from (a) a reanalysis of an intensive mathematics intervention database (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;171 effect sizes, 24 studies), (b) meta-analytic criterion-validity evidence for mathematics curriculum-based measurement (\u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;330), and (c) randomized manipulation of intervention session frequency holding total minutes constant (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;101). In Dataset A, dosage\u0026ndash;effect size correlations were significant for at-risk samples (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38) but not mixed samples (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05), and were strongest for at-risk samples with skill-aligned outcomes (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.52; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.40 excluding one extreme outlier). Experimental evidence converged: higher session frequency improved a proximal computation measure but not distal standardized outcomes. Together, results support a developmentally aligned learning-rate account of MLD and illustrate how child-calibrated digital twins can generate precise, testable predictions for intervention science.\u003c/p\u003e","manuscriptTitle":"Developmentally Aligned AI Modeling of Mathematics Learning Disability: Behavioral Validation of Neural Learning-Rate Constraints","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 13:35:34","doi":"10.21203/rs.3.rs-8545352/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"286f1d44-3e26-4e71-b965-e1be56b7c663","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T03:44:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 13:35:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8545352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8545352","identity":"rs-8545352","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}