Learners Deserve a Relevant, Expansive, AI-Enabled Future
Higher education has reached a structural fork in the road that, once passed, separates institutions that shaped the future from those made irrelevant by it. Where the internet democratized access to education, AI can now democratize access to learning by making it deeply personal and relevant.
Two weeks ago, at the ASU+GSV Summit, I delivered a keynote about how WGU is approaching this moment. AI isn't just a tool for improving upon the status quo; it's a catalyst for reimagining every aspect of education from the ground up, from curriculum to instruction, support, assessment, and more. The question for other institutions of learning is whether they’ll seize this opportunity to build the future learners deserve or resist it and gradually become obsolete.
Start With the End
There’s often a tendency to overlay new technologies onto existing models. But something I learned while working at Amazon is the benefit of first envisioning a desired future state. What are the outcomes we owe learners, and working backwards, how might AI enable us to achieve those outcomes, if we were to design from scratch? That reframing allows leaders to think beyond the methods and models that constrain today, opening up entirely new possibilities.
Students deserve a future where learning is personalized, adaptive, competency-based, and skills-architected. Where learning is designed to be continuous, rather than one-and-done; relevant to the world of work, and networked, connecting individuals to opportunity. Above all, they deserve a system anchored around agency and trust — one designed to help them lead self-determined lives, not to preserve institutional advantage. Everything we build should enable these key design features, not constrain them.
Here’s what that will look like at WGU 2.0:
- Education that is relevant for the first and next opportunity. Given the fast-changing nature of work and the rapidly evolving skills needed for workplace success, education is reconceived, not as a vaccine that inoculates learners at the beginning of their careers, but as a vitamin that provides resilience and strength to tackle the next opportunity and the next.
- Personalized pathways at scale. Instead of one prescribed path to a credential, there are thousands, as many as there are unique individuals. Just as we navigate using Google Maps, students see their current skill profile, their desired destination, and can explore the routes, modes, and tradeoffs to get there. Acquired skills are verified through rigorous assessment, then connected to real networks and real opportunity. The journey is theirs.
- Mass customization of curriculum. Historically, students have followed a fixed path to earn their credential, with minimal data on the end-to-end student journey. Now, we can atomize learning from broad degrees to micro-courses, modules, and composite curriculum, designing backwards from the skills students need, not forward from faculty preferences. AI lets us do this at a scale that was previously impossible.
- Continuous feedback and assessment. Through Cognitive Task Analysis and structured domain modeling, we're measuring competency continuously and adaptively, with immediate feedback loops enabled by an agent-first, tutor model. In this way, we’re now positioned to deliver on what research from Benjamin Bloom and others has long promised—increased mastery via one-to-one, mastery-based instruction with corrective feedback, at scale. Early data shows improved first-attempt pass rates, faster progression, and higher confidence.
- Secure, reliable verification. Of all the supply chains that exist, the talent economy is the least digitized, resulting in gaps in transparency, opportunity, and equity. Powered by AI, our Digital Credential Wallet provides students a trusted way to verify and communicate their competency that is interoperable, portable, and designed to live with them. But for universal learning records such as these to live up to their promise, it’s equally incumbent upon employers to design systems that integrate the technology.
- Humans matter, but we have to prove it. Even in an AI-native environment, our disposition should always be to enable human connectivity and ensure humans remain the principal actors. But it’s a mistake to assume certain roles are decidedly human without the data to support it. That requires being honest about where human presence is irreplaceable — where our empathy, judgment, and compassion are vital — and using AI to amplify that capacity everywhere else.
Changing Lives is the Purpose
The accelerated pace of change now requires continuous reskilling, signaling the end of the one-and-done, coming-of-age education model. In the future, education will be continual, timely, and relevant, with work experience embedded within it, not separated from it. This isn’t a threat to higher education; it’s an invitation to think more expansively about how we deliver it.
As that shift accelerates, ownership matters. When learners can control their pathways and carry trusted, verifiable proof of what they know, access improves, mobility increases, and opportunity expands. This is the future AI makes possible — and that is world-changing.
