EPISODE · May 4, 2026 · 38 MIN
The End of Average: AI and Learner Variability, from Classroom to Career
from ASU+GSV Summit Sessions · host ASU+GSV
Recorded live at the 2026 ASU+GSV Summit in San Diego, this session featured Antoinette Banks, CEO and Founder at Expert IEP; Barry Finder, Vice President of Operations at Chegg; Nellie Tayloe Sanders, Former Oklahoma Secretary of Education; Toni Barton, CEO at E.L. Haynes; and Meredith Rosenberg, Partner and Co-Founder at NU Advisory Partners.The speakers explored what it would mean to design education and workforce systems around learner variability instead of retrofitting support only after students fell behind. They examined how, for decades, support for learning differences had been siloed, reactive, and often cut off after high school, and how AI was challenging that model.This conversation brought together leaders across K–12, research, and workforce innovation to discuss whether AI would be used to redesign systems for every learner or reinforce the inequities those systems were originally built on. Panelists examined how personalization and skills development at scale could transform support structures so that learner differences were addressed proactively rather than reactively.At its core, this session focused on what needed to change to ensure support did not end at graduation and that opportunity truly extended from classroom to career. By rethinking learner variability as a lifelong design principle rather than a temporary intervention, the conversation highlighted how AI could help build more inclusive, adaptive, and equitable systems across education and workforce pathways.
What this episode covers
Recorded live at the 2026 ASU+GSV Summit in San Diego, this session featured Antoinette Banks, CEO and Founder at Expert IEP; Barry Finder, Vice President of Operations at Chegg; Nellie Tayloe Sanders, Former Oklahoma Secretary of Education; Toni Barton, CEO at E.L. Haynes; and Meredith Rosenberg, Partner and Co-Founder at NU Advisory Partners.The speakers explored what it would mean to design education and workforce systems around learner variability instead of retrofitting support only after students fell behind. They examined how, for decades, support for learning differences had been siloed, reactive, and often cut off after high school, and how AI was challenging that model.This conversation brought together leaders across K–12, research, and workforce innovation to discuss whether AI would be used to redesign systems for every learner or reinforce the inequities those systems were originally built on. Panelists examined how personalization and skills development at scale could transform support structures so that learner differences were addressed proactively rather than reactively.At its core, this session focused on what needed to change to ensure support did not end at graduation and that opportunity truly extended from classroom to career. By rethinking learner variability as a lifelong design principle rather than a temporary intervention, the conversation highlighted how AI could help build more inclusive, adaptive, and equitable systems across education and workforce pathways.
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The End of Average: AI and Learner Variability, from Classroom to Career
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