Nearly 38 million working-age adults in the United States have earned college credits but have not earned a postsecondary credential. One barrier to credential completion is the difficulty of transferring and applying credits earned at one institution toward a credential at another, or toward a different credential at the same institution. 

Despite growing attention to this problem, many existing solutions have struggled to scale. Students are asked to self-report transcript details, compare requirements, and navigate a system that was never designed for that complexity. DataKind is taking a different approach: building AI-powered technology designed for the people already doing this work – advisors, registrars, and credential evaluators – so they can do it more efficiently and at scale.

In partnership with Axim Collaborative, DataKind is developing and testing AI-assisted technology to match courses across institutions, map completed coursework to credential requirements, and identify where students may already be close to completing a credential. This includes identifying opportunities for reverse transfer or an alternative credential at their current institution. Early testing shows that AI-based matching can outperform traditional keyword-based approaches and that manual review time can be reduced from hours to minutes, freeing advisors to spend more time directly supporting students. 

By mapping these opportunities at scale, DataKind can also give institutions and state systems a clearer picture of where credits get lost and potential credentials not realized – helping inform broader strategies for credit mobility and credential completion.

Through this partnership, DataKind is exploring how AI can make credit mobility more scalable and reduce barriers to credential completion, while keeping advisors and student success teams at the center of the process.