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Digital twins reveal how math disabilities affect the brain
https://phys.org/news/2025-06-digital-twins-reveal-math-disabilities.htmlby Andrew Myers, Stanford University
Using AI to analyze brain scans of students solving math problems, researchers offer the first-ever glimpse into the neural roots of math learning disabilities.
Combining the powers of artificial intelligence and functional magnetic resonance imaging (fMRI), a team of researchers at Stanford University have created "digital twins" of struggling math students to offer first-ever insights into the neurological underpinnings of math learning disabilities, which vex as many as 1 in 5 students in America.
"Our study grew out of a couple of decades of behavioral, cognitive neuroimaging work on trying to understand the brain bases and the cognitive foundations of learning disabilities in children," said Vinod Menon, professor of psychiatry and behavioral sciences at Stanford University. "Now we have these new AI tools that actually allow us to ask those questions much more deeply, much more mechanistically."
In a paper published in the journal Science Advances, Menon and his co-authors, Stanford postdoctoral scholar Anthony Strock and social science research scholar Percy Mistry, introduce what they refer to as personalized deep neural networks. These are effective digital "twin brains" of real children, models able to mimic how individual students solve math problems and to demonstrate computationally where things go awry in the brains of children with math learning disabilities.
. . .
Renewed hope
The educational implications are considerable. Digital twins will allow researchers to test neurological mechanisms in silico—on the computer—in each child, offering a window into brain-level causes of learning struggles. Menon highlighted that the study shows that AI twins modeling math learning disabilities required nearly twice as much training to reach the same accuracy as typically developing math students. But, Menon emphasized, "They do eventually reach equivalent performance. And that gives us great hope for improved remediation strategies."
For educators, digital twins might lead to personalized learning plans tailored to the learning style of a specific student and predict types of instruction that might work best for each individual learner. Menon and the team are now extending their models in new directions to create even richer neurological simulations of mathematical reasoning.
. . .
Combining the powers of artificial intelligence and functional magnetic resonance imaging (fMRI), a team of researchers at Stanford University have created "digital twins" of struggling math students to offer first-ever insights into the neurological underpinnings of math learning disabilities, which vex as many as 1 in 5 students in America.
"Our study grew out of a couple of decades of behavioral, cognitive neuroimaging work on trying to understand the brain bases and the cognitive foundations of learning disabilities in children," said Vinod Menon, professor of psychiatry and behavioral sciences at Stanford University. "Now we have these new AI tools that actually allow us to ask those questions much more deeply, much more mechanistically."
In a paper published in the journal Science Advances, Menon and his co-authors, Stanford postdoctoral scholar Anthony Strock and social science research scholar Percy Mistry, introduce what they refer to as personalized deep neural networks. These are effective digital "twin brains" of real children, models able to mimic how individual students solve math problems and to demonstrate computationally where things go awry in the brains of children with math learning disabilities.
. . .
Renewed hope
The educational implications are considerable. Digital twins will allow researchers to test neurological mechanisms in silico—on the computer—in each child, offering a window into brain-level causes of learning struggles. Menon highlighted that the study shows that AI twins modeling math learning disabilities required nearly twice as much training to reach the same accuracy as typically developing math students. But, Menon emphasized, "They do eventually reach equivalent performance. And that gives us great hope for improved remediation strategies."
For educators, digital twins might lead to personalized learning plans tailored to the learning style of a specific student and predict types of instruction that might work best for each individual learner. Menon and the team are now extending their models in new directions to create even richer neurological simulations of mathematical reasoning.
. . .