How Technology Can Smooth Pain Points in Credit Evaluation

Earlier this month, higher education policy leaders from all 50 states gathered in Minneapolis for the 2025 State Higher Education Executive Officers Higher Education Policy Conference. During a plenary session on the future of learning and work and its implications for higher education, Aneesh Raman, chief economic opportunity officer at LinkedIn, reflected on the growing need for people to be able to easily build and showcase their skills.

In response to this need, the avenues for learning have expanded, with high numbers of Americans now completing career-relevant training and skill-building through MOOCs, microcredentials and short-term certificates, as well as a growing number of students completing postsecondary coursework while in high school through dual enrollment.

The time for pontificating about the implications for higher education is past; what’s needed now is a pragmatic examination of our long-standing practices to ask, how do we evolve to keep up? We find it prudent and compelling to begin at the beginning—that is, with the learning-evaluation process (aka credit-evaluation process), as it stands to either help integrate more Americans into higher education or serve to push them out.

A 2024 survey of adult Americans conducted by Public Agenda for Sova and the Beyond Transfer Policy Advisory Board found, for example, that nearly four in 10 respondents attempted to transfer some type of credit toward a college credential. This included credit earned through traditional college enrollment and from nontraditional avenues, such as from trade/vocational school, from industry certification and from work or military experience. Of those who tried to transfer credit, 65 percent reported one or more negative experiences, including having to repeat prior courses, feeling limited in where they could enroll based on how their prior learning was counted and running out of financial aid when their prior learning was not counted. Worse, 16 percent gave up on earning a college credential altogether because the process of transferring credit was too difficult.

What if that process were drastically improved? The Council for Adult and Experiential Learning’s research on adult learners finds that 84 percent of likely enrollees and 55 percent of those less likely to enroll agree that the ability to receive credit for their work and life experience would have a strong influence on their college enrollment plans. Recognizing the untapped potential for both learners and institutions, we are working with a distinguished group of college and university leaders, accreditors, policy researchers and advocates who form the Learning Evaluation and Recognition for the Next Generation (LEARN) Commission to identify ways to improve learning mobility and promote credential completion.

With support from the American Association of Collegiate Registrars and Admissions Officers and Sova, the LEARN Commission has been analyzing the available research to better understand the limitations of and challenges within current learning evaluation approaches, finding that:

• Learning-evaluation decision-making is a highly manual and time-intensive process that involves many campus professionals, including back-office staff such as registrars and transcript evaluators and academic personnel such as deans and faculty.
• Across institutions, there is high variability in who performs reviews; what information and criteria are used in decision-making; how decisions are communicated, recorded and analyzed; and how long the process takes.
• Along with this variability, most evaluation decisions are opaque, with little data used, criteria established or transparency baked in to help campus stakeholders understand how these decisions are working for learners.
• While there have been substantial efforts to identify course equivalencies, develop articulation agreements and create frameworks for credit for prior learning to make learning evaluation more transparent and consistent, the data and technology infrastructure to support the work remain woefully underdeveloped. Without adequate data documenting date of assessment and aligned learning outcomes, credit for prior learning is often dismissed in the transfer process; for example, a 2024 survey by AACRAO found that 54 percent of its member institutions do not accept credit for prior learning awarded at a prior institution.

Qualitative research examining credit-evaluation processes across public two- and four-year institutions in California found that these factors create many pain points for learners. For one, students can experience unacceptable wait times—in some cases as long as 24 weeks—before receiving evaluation decisions. When decisions are not finalized prior to registration deadlines, students can end up in the wrong classes, take classes out of sequence or end up extending their time to graduation.

In addition to adverse impacts on students, MDRC research illuminates challenges that faculty and staff experience due to the highly manual nature of current processes. As colleges face dwindling dollars and real personnel capacity constraints, the status quo becomes unsustainable and untenable. Yet, we are hopeful that the thoughtful application of technology—including AI—can help slingshot institutions forward.

For example, institutions like Arizona State University and the City University of New York are leading the way in integrating technology to improve the student experience. The ASU Transfer Guide and CUNY’s Transfer Explorer democratize course equivalency information, “making it easy to see how course credits and prior learning experiences will transfer and count.” Further, researchers at UC Berkeley are studying how to leverage the plethora of data available—including course catalog descriptions, course articulation agreements and student enrollment data—to analyze existing course equivalencies and provide recommendations for additional courses that could be deemed equivalent. Such advances stand to reduce the staff burden for institutions while preserving academic quality.

While such solutions are not yet widely implemented, there is strong interest due to their high value proposition. A recent AACRAO survey on AI in credit mobility found that while just 15 percent of respondents report currently using AI for credit mobility, 94 percent of respondents acknowledge the technology’s potential to positively transform credit-evaluation processes. And just this year, a cohort of institutions across the country came together to pioneer new AI-enabled credit mobility technology under the AI Transfer and Articulation Infrastructure Network.

As the LEARN Commission continues to assess how institutions, systems of higher education and policymakers can improve learning evaluation, we believe that increased attention to improving course data and technology infrastructure is warranted and that a set of principles can guide a new approach to credit evaluation. Based on our emerging sense of the needs and opportunities in the field, we offer some guiding principles below:

1. Shift away from interrogating course minutiae to center learning outcomes in learning evaluation. Rather than fixating on factors like mode of instruction or grading basis, we must focus on the learning outcomes. To do so, we must improve course data in a number of ways, including adding learning outcomes to course syllabi and catalog descriptions and capturing existing equivalencies in databases where they can be easily referenced and applied.
2. Provide students with reliable, timely information on the degree applicability of their courses and prior learning, including a rationale when prior learning is not accepted or applied. Institutions can leverage available technology to automate existing articulation rules, recommend new equivalencies and generate timely evaluation reports for students. This can create more efficient advising workflows, empower learners with reliable information and refocus faculty time to other essential work (see No.3).

3. Use student outcomes data to improve the learning evaluation process. Right now, the default is that all prior learning is manually vetted against existing courses. But what if we shifted that focus to analyzing student outcomes data to understand whether students can be successful in subsequent learning if their credits are transferred and applied? In addition, institutions should regularly review course transfer, applicability and student success data at the department and institution level to identify areas for improvement—including in the design of curricular pathways, student supports and classroom pedagogy.
4. Overhaul how learning is transcripted and how transcripts are shared. We can shorten the time involved on the front end of credit-evaluation processes by shifting away from manual transcript review to machine-readable transcripts and electronic transcript transmittal. When accepting and applying prior learning—be it high school dual-enrollment credit, credit for prior learning or a course transferred from another institution—document that learning in the transcript as a course (or, as a competency for competency-based programs) to promote its future transferability.
5. Leverage available technology to help learners and workers make informed decisions to reach their end goals. In the realm of learning evaluation, this can be facilitated by integrating course data and equivalency systems with degree-modeling software to enable learners and advisers to identify the best path to a credential that minimizes the amount of learning that’s left on the table.

In these ways, we can redesign learning evaluation processes to accelerate students’ pathways and generate meaningful value in the changing landscape of learning and work. Through the LEARN Commission, we will continue to refine this vision and identify clear actionable steps. Stay tuned for the release of our full set of recommendations this fall and join the conversation at #BeyondTransfer.