These days, Learner Employment Records (LERs) are everywhere, if you know where to look, showing how reimagining resumes is already beginning. From new state-level legislation in California to radical workforce partnerships like the Alabama Talent Triad and the work being done in Wyoming and Indiana and Idaho, there’s a great deal of promise, but it can be a bit difficult to describe and even harder to imagine. What do you mean I’d have a digital wallet to capture a lifetime’s worth of experiences and skills? What’s this about self-sovereign data and reimagining resumes? When do people actually put something in their wallet? And, importantly, how do we verify this information?

AI plays (and will only continue to play) a major role in reimagining resumes, HR, admissions, documentation and data collection. Even before AI, our best approaches to resumes were well short of ideal: the average resume gets 10 seconds of attention, and, according to ResumeLab, 70% of people lie when populating their resumes. Founder of Gobekli, Danny Done, said it succinctly, “We’re using a 15th century document to run a 21st century system.” Validating and verifying these credentials, experiences, and documents builds trust, builds confidence and has the potential to strengthen ties within networks, demonstrating the impact of reimagining resumes.

At the same time, LERs have been described as “a religion looking for a messiah,” —not a ringing endorsement, showing that there is still a way to go in effectively reimagining resumes. Essentially, funders around the world are leaning back slowly in their chairs and saying “prove it”. Proving the technology works is one thing, but showing what an LER-enabled world looks like is another. Without good evidence of growth, learning, and demonstrated competency, LER ecosystems have little heft to them. We believe that future learner wallets will be populated in the following ways, reimagining resumes:

  • Self-attested stories – Something narrated and claimed by the wallet holder.
  • Surveyed stories – Collected evidence from assessments and other data sources.
  • Credentialed stories – This thing has a badge that’s authorized by a third party.
  • Calculated stories – AI-inferred skills based on provided evidence or anecdotes.

Read the full article about the future of learner records by Mason Pashia and Beth Ardner at Getting Smart.