Thunkable's 2026 pilot brought AI-assisted, no-code app-building and entrepreneurship lessons to about 120 high-school students across three West Virginia counties; participants built roughly 80 mobile apps and 44% reported greater interest in starting a business.
120 students
Students engaged (2026 pilot)
80 apps
Mobile apps built
44 %
Participants reporting greater interest in starting a business
Details
Maturity
Pilot
Promoter
Thunkable, with the West Virginia Department of Education and Rockefeller Foundation support
Period
2026 pilot, concluded September 2026
Keywords
AI literacy, entrepreneurship education, no-code app development
Context
Thunkable, a no-code mobile-app-development platform, ran a 2026 AI-literacy and entrepreneurship pilot with the State of West Virginia across high schools in Monongalia, Kanawha and Berkeley counties, aimed at students in grades 9-12 considered vulnerable to AI-driven labour-market disruption.
Activities
Students used AI-assisted, no-code tools to design and build their own mobile applications while learning practical AI concepts, problem-solving and entrepreneurship basics.
Results
The pilot, which concluded in September 2026, engaged about 120 students who built an estimated 80 working mobile apps; the programme's own reporting states 44% of participants said the experience increased their interest in starting a business.
Conclusions
These are self-reported, funder-published figures rather than an independently verified evaluation. The pilot sits alongside West Virginia's broader state AI and Digital Sciences Framework, the first such state-level framework in the US.
Implementation
Indicative cost
Low (< €50k)
Time to results
Short (< 1 year)
Staffing & skills
Thunkable (platform provider), West Virginia Department of Education (state partner), Rockefeller Foundation (funding support)
Conditions for success
No-code tooling lowering the technical barrier to app-building
Alignment with the state's AI and Digital Sciences Framework
Targeting counties/grades considered vulnerable to AI-driven labour disruption
Commonly funded by
Philanthropic / foundation fundingNational / regional programmes
Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.
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Data sources
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