Carol Fletcher
University of Texas at Austin, TX·Education Research
Computing and artificial intelligence now shape nearly every job and civic decision, but which K-12 students get access to computer science courses still depends heavily on which state and which school they attend. Changing that is less a classroom problem than a policy and capacity problem: states differ in their course requirements, teacher certification routes, funding and data systems, and most lack the information needed to see who is and is not being reached.
- Fields
Education Research
Computer Science
Social Science
- Topics
computer science education
K-12 education
artificial intelligence education
- Grants
- 1 known grant, through Dec 2029
What they work on
- Increase the pathways available to K-12 students leading into computing- and AI-intensive college degrees, through state-initiated course and policy changes coordinated across the 30-state ECEP Alliance.
- Strengthen state capacity to implement computer science and artificial intelligence education strategies, using targeted professional coaching of state leadership teams, national convenings and monthly Alliance calls.
- Establish consistent, data-driven measurement of student access to and participation in computing courses through the Common Metrics Project, which builds state capacity to collect and visualize comparable course data.
- Improve student outcomes in computer science and AI by delivering professional development, including the Scaling Innovative Pedagogy (ScIP) course, ECEP Connect and ECEP Deep Dives, and by enabling peer collaboration between state teams.
More about the research
Work here studies and supports how states build the capacity to offer computing and AI education to all students.
Why it matters
Access to computer science in K-12 schools varies sharply by state and by student group, which shapes who can later enter computing- and AI-intensive degrees and careers. Giving state education leaders common metrics, coaching and tested strategies could make participation data visible and comparable, informing policy decisions that widen access for students across 30 states and territories.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Carla Brodley
Northeastern University, MA·Education Research
Test whether a placement assessment plus one-credit supplemental courses can reduce credit loss for community college transfer students entering computer science majors, in a pilot at three universities.
Narges Norouzi
University of California-Berkeley, CA·Education Research
Model students' problem-solving strategies and algorithmic thinking in computer science courses by integrating fine-grained process data to trace competencies across levels of abstraction.
Fred Martin
University of Texas at San Antonio, TX·Education Research
Document current understandings, policies and practices for using and teaching AI literacy in partner middle school districts in Texas and New York, working with teachers and technology coaches.
The school
University of Texas at Austin: 49 labs on PI Pages, $1.1B a year on research, 871 research doctorates a year.
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