Learning Differences

Significantly improving opportunities for children with diverse learning needs to participate and learn in school and life.


Through interdisciplinary collaboration, we will become a global leader in advancing discovery, designing inclusive solutions, preparing educators, cultivating leaders, and mobilizing knowledge to create lives of welcome engagement.

When we create solutions for people with learning differences, the outcomes can benefit all learners. Solutions that enable students with learning differences to be incorporated into classrooms enrich their peer’s conceptions of difference, acceptance, and respect. Understanding and solving for the edge cases benefits all the cases.

Projects underway connect scholars from medicine, computer science, and education to create solutions for learners with disabilities and the first round of seed grants were awarded to five in collaboration with mediaX at Stanford University the Industry Affiliate Partner inside the Stanford Graduate School of Education.

Automated Online Assessment of Reading Ability to Enable Personalized Education

Through our research into the mechanisms of brain plasticity and learning, our group has developed a rapid, automated, online assessment of reading ability that can be administered to children in their own homes through the web-browser by simply sending a URL. The goal of this pilot project is to take this tool and apply it in a large-scale collaborative study with school districts and clinics to characterize learning differences.

Project lead

Bio photo of Jason Yeatman
Jason Yeatman

Assistant Professor

Using AI Analysis of Mobile Games to Both Track and Treat a Continuum of Early Childhood Learning

The focus of this research project will be to define these learning paths as cognitive taxonomic models to characterize a child’s learning potential and to design a series of mobile game solutions with embedded AI that foster personalized learning and dynamic movement along or switching among learning paths.

Project lead

Bio photo of Dennis Wall
Dennis Wall

Associate Professor

Personalizing Support for Individuals’ Development of Self-regulation Using Boolean Network-based Methods and Metrics

The objective of this project is to develop proof-of-concept demonstrations that illustrate the value of a new Boolean network-based method for describing the dynamic interplay of individuals’ cognitive, emotional, and physical states, and designing personalized interventions that would effectively guide individuals toward desired goals.

Project lead

Bio photo of Nilam Ram
Nilam Ram

Professor

Toward New Taxonomies of Student by Treatment Interactions in Special Education: Validating Neural Metrics of Engagement with Naturalistic Stimuli using Mobile EEG

Our long-term goal is to develop individualized neural indicators that are reflective of the quality of the dynamic interaction between a special-needs learner and a specific educational intervention. Such metrics may provide novel insights into distinct sources of learner needs, and better pathways to learner supports.

Project lead

Bio photo of Bruce McCandliss
Bruce McCandliss

Professor

Developing a Computationally Precise Description of Early Interactive Learning and Its Impact on Later Development

We propose to create a taxonomy of learning differences through a mechanistic understanding of how children might differ in their early (first 2 years) learning behaviors, and how these lead to later differences. We hope this will be used as a new lens with which to understand autism and related developmental differences.

Project lead

Bio photo of Nick Haber
Nick Haber

Assistant Professor