Earlier this month, higher education policy leaders from all 50 states gathered at the 2025 Higher Education Executive Higher Education Policy Conference in Minneapolis. Aneesh Raman, Chief Economic Opportunity Officer at LinkedIn, reflected the growing need for people to easily build and demonstrate their skills in a plenary session on the future of learning and work and its impact on higher education.
To cope with this need, learning pathways have expanded, and now a large number of Americans have completed career-related training and skills building through MOOC, micro-records and short-term certificates, as well as an increasing number of students completing high school courses through dual enrollment in high school.
Time to focus on the impact on higher education has passed. What is needed now is the pragmatic research we have to ask for for a long time. How should we develop it? We find it cautious and compelling from the start, that is, with the learning assessment process (also known as the credit assessment process) because it can help incorporate more Americans into higher education, or help delay them.
For example, a survey conducted by Sova on Adult Americans and the Advisory Committee on Transfer Policy found that, for example, one in 10 respondents attempted to transfer certain types of credit to a university certificate. This includes credits earned through traditional college admissions and non-traditional pathways, such as from trade/vocational schools, from industry certifications, and work or military experience. Among those who attempt to transfer credit, 65% reported one or more negative experiences, including the negligence of having to repeat previous lessons, based on how their previous learning was calculated, and where they could register were restricted when their previous learning was not calculated. Worse, 16% of people have completely won a college certificate because the process of transferring credit is too difficult.
What if the process improves greatly? A study by the Adult and Experiential Learning Committee on Adult Learners found that 84% of possible enrollment and 55% agreed that their ability to accept credit for their work and life experiences would have a strong impact on their college admissions program. Recognizing the untapped potential of learners and institutions, we are working with outstanding university and university leaders, accreditors, policy researchers and advocates who constitute the Next Generation Learning Assessment and Recognition (Learn) Committee to identify ways to improve learning ability and facilitate voucher completion.
Supported by U.S. University Registration Officers and Admissions and Sova, the Learning Committee has been analyzing available research to better understand the limitations and challenges in current learning assessment methods, finding the following aspects:
- Learning assessment decisions are a highly manual and time-consuming process involving many campus professionals, including backstage staff such as principals and transcript evaluators, as well as academic staff such as deans and faculty.
- There is a high variation in the people who perform comments throughout the organization. What information and standards are used in decision making; how decisions are communicated, recorded and analyzed; and how long does the process take.
- Apart from this difference, most evaluation decisions are opaque, with little use of data, determined criteria or transparency fused together to help campus stakeholders understand how these decisions work for learners.
- Although great efforts have been made to determine curriculum equivalence, establish pronunciation protocols and create frameworks to learn in advance to make learning assessments more transparent and consistent, the data and technical infrastructures have underdeveloped work to support this work. Previous learning credit is often ignored during the transfer process if there is insufficient data record of assessment dates and consistency of learning outcomes; for example, AACRAO’s 2024 survey found that 54% of its member institutions do not accept credits for previous learning granted by previous institutions.
Qualitative studies examined the credit assessment process for public two- and four-year institutions in California and found that these factors cause a lot of pain to learners. First, students can experience unacceptable waiting times (24 weeks in some cases) before accepting the assessment decision. If a decision is not made before the registration deadline, students may take the wrong course, attend class hours or eventually extend the time of graduation.
In addition to adverse effects on students, the MDRC study also sheds light on the challenges experienced by faculty and staff due to the highly manual nature of the current process. As universities face reduced dollar and real staffing constraints, the status quo becomes unsustainable and untenable. However, we hope that the thoughtful application of technology, including AI, can help the slingshot mechanism move forward.
For example, institutions such as Arizona State University and City University of New York are integrating technology to improve the path of student experience. The ASU Transfer Guide and CUNY’s Transfer Explorer democratizes course equivalent information, “makes you easy to understand how course credits and previous learning experiences will be transferred and counted.” Additionally, UC Berkeley researchers are looking at how to leverage the massive amounts of data available (including course catalog descriptions, course expression protocols, and student admission data) to analyze existing course equivalents and provide advice for other courses that can be considered equivalent. Such advancements will reduce the burden on the institution’s staff while retaining academic quality.
Although such solutions have not been widely implemented, they have attracted great interest due to their high value propositions. AACRAO’s recent survey on AI credit liquidity found that while only 15% of respondents reported currently using AI for credit liquidity, 94% acknowledge the potential of the technology to actively change the credit assessment process. Just this year, under the AI transfer and pronunciation infrastructure network, a series of institutions across the country have brought together new AI-enabled credit flow technologies.
As the Learning Committee continues to evaluate how institutions, higher education and policy makers’ institutions improve learning assessments, we believe there is a need to focus on improving curriculum data and technical infrastructure, and a set of principles can guide new credit assessment methods. Based on our emerging awareness of needs and opportunities in the field, we provide some of the following guiding principles:
- Transfer from interrogating course details to central learning outcomes in learning assessments. We must focus on learning outcomes, rather than being fixed on factors such as teaching models or scoring foundations. To do this, we must improve course data in a number of ways, including adding learning outcomes to course courses and catalog descriptions and capturing existing equivalents in databases where they can be easily referenced and applied.
- Provide students with reliable, timely information on the applicability of their courses and prior study, including reasons for learning before they are not accepted or applied. Institutions can use available technologies to automate existing pronunciation rules, recommend new equivalences and generate timely assessment reports for students. This can create a more effective consulting workflow that enables learners to provide reliable information and refocus faculty and staff on other basic work (see No. 3).
- Use student outcome data to improve the learning assessment process. Currently, by default, all previous studies are reviewed for existing courses. But what if we shifted our focus to analyzing data on student outcomes to see if students can successfully learn in subsequent learning, what should we do? In addition, institutions should regularly review departmental and institution-level curriculum transfer, applicability and student success data to identify areas of improvement – including curriculum pathways, student support and design of classroom pedagogy.
- How to transcribe learning and how to share transcripts in the overhaul. We can reduce the time involved in the front end of the credit evaluation process by moving from manual transcript review to machine-readable transcript and electronic transcript transmission. Study as a course (or, as a competency-based program) in transcripts when receiving and applying previous studies (IT IT IT High School Dual Credit, Previous Study Credit or Course transferred from another institution) to promote its future transferability.
- Utilize available technologies to help learners and workers make informed decisions to achieve their ultimate goals. In the field of learning assessment, the amount of learning remaining on the table can be minimized by integrating course data and equivalent systems with degree model software to enable learners and consultants to determine the best way to credentials.
In these ways, we can redesign the learning assessment process to accelerate students’ pathways and generate meaningful value in the ever-changing learning and work environment. Through the Study Committee, we will continue to refine this vision and identify clear and feasible steps. Stay tuned for the full set of suggestions released this fall and have a conversation on #BeyondTransfer.
