The Prior Learning Recognition (PLR) Terms Proposal
The Credential Transparency Description Language (CTDL) describes credit recommendations and equivalencies between organizations. For example, the American Council on Education (ACE) has published more than 10,000 CTDL Transfer Value Profiles, showing how prior learning and credit recommendations can be transferred and applied across institutions.
But there was a gap; CTDL had not yet captured the rules, policies, and agreements that determine whether and how prior learning is recognized in the first place. That’s why Credential Engine convened a global task group — to make this information transparent, connected, and reusable as linked open data.
The PLR Terms Proposal expands CTDL to support structured, machine-readable descriptions of:
- Transfer agreements and credit equivalencies
- Recognition policies and related support services
- Types of learning recognition (formal, non-formal, informal)
- Evidence of prior learning accepted
- Methods of evaluation
- Evaluation outcomes
The CTDL will describe what is recognized, as well as the conditions, evidence, and processes that govern recognition decisions.
Why This Work Matters
Too often, prior learning, whether from formal education, work, military service, volunteering, or life experience, is lost in translation. Policies, regulations, agreements, and institutional practices shape how or whether a person’s learning continues to be recognized when they move between institutions, systems, or countries.
The problem is that these rules are often hard to find and difficult to compare. The result? Learners lose recognition they deserve, institutions duplicate effort, and pathways remain unclear.
By expanding CTDL to describe these agreements, policies, conditions, and supports, we unlock this information as linked open data. That means it becomes structured, discoverable, comparable, and usable by people and systems alike.
Everyone benefits when recognition of learning is portable, transparent, and connected:
- Learners gain recognition for what they already know.
- Institutions operate more efficiently and serve more learners.
- Policymakers and technologists gain the data needed to improve guidance, planning, and tools.
- Systems become future-ready, supporting interoperability, automation, analytics, and emerging technologies like AI.
Timeline and Participation
- Task Group Completed: September 17, 2025 — Experts contributed 147 use cases, nearly 150 transfer agreements, and 400 policies.
- Public Webinar: October 15, 2025 — Launch of the terms proposal, with examples and the domain model. You can check out the slides and recording from that webinar here!
- Public Comment Period: October 15–31, 2025 — Anyone can provide input to refine and finalize the proposal.
- Implementation: Beginning November 2025, Credential Engine will update the CTDL Handbook, Credential Registry, Publishing API, publishing system, and consuming services to integrate the new terms.
Active participation matters. By reviewing the proposal, sharing feedback, and engaging with this work, you help ensure recognition of prior learning is transparent, connected, and beneficial for learners, institutions, policymakers, and systems worldwide.
Get in Touch
Our team of experts is ready to help you embark your credential transparency journey. Whether you have questions about our technologies, services, or don’t know how to get started, we’re here to assist.