Learning disabilities (LD) are among the most common types of disabilities in juvenile offenders that have been linked to delinquency. Nationwide, children and youth with special education needs are overrepresented in the US justice systems. Reports estimate delinquent juveniles with a disability to comprise about 30% to 60% of the entire delinquent population. A national survey in the US states an average prevalence rate of 33.4% of incarcerated juveniles with disabilities in correctional facilities. Moreover, concerns have long been raised on the recidivism rates of youth with disabilities and special education backgrounds. In general, regarding educational performance, academic deficits such as a lack of basic skills in reading, writing, and mathematics have been associated with recidivism. However, these studies largely neglect the dynamic nature of delinquent and criminal behavior that has been documented within the field of developmental criminology with a focus on the onset, continuity, and extinction of deviant behavior. In light of this research, the identification and remediation of LD as a risk factor for repeat offending has been a persistent challenge given the accumulation of and overlap with other risk factors such as poverty, familial patterns of criminality, influence of delinquent peers, and the differential impact of risk factors across an individual's developmental trajectory. Altogether, there is a challenge around the implementation of sophisticated methodology to model the complex longitudinal and reciprocal links between juvenile delinquency and educational problems such as LD, and how they relate to other risk factors over time. This challenge is intensified by the required large samples to detect robust and interpretable patterns and predictive relationships for groups of youth with severe LDs that, by definition, are small in size and censored with regard to various educational outcomes (e.g., academic performance). This research is conceived to contribute to the field's understanding of the connection between LD and delinquent behavior. We hope to generate unique findings capitalizing on the availability of the relevant big data, the clinical strengths of its members and their capacity to develop and administer educational therapy to juvenile offenders, and its embedment within communities empowering the creation and processing of multi-level longitudinal datasets, merging sociological (i.e., criminological), behavioral, neurophysiological, and genetic/genomic data.