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A group of college accreditors is backing the use of artificial intelligence to reduce credit loss during transfer, which is a major barrier to completion for many of the 43\u00a0million people across the nation with some college credit but no degree. <\/p>\n
\u201cCurrent approaches frequently result in delays in students receiving necessary information about how their credits will transfer, the need to retake courses, and other negative consequences for students,\u201d said a statement the Council of Regional Accrediting Commissions released Monday. \u201cTechnological advances, such as AI, can help institutions improve this process.\u201d<\/p>\n
Although the statement from CRAC, which represents seven accrediting agencies that oversee 3,000 total degree-granting institutions, isn\u2019t a mandate, the council hopes it will send a message to colleges and universities that leveraging AI to expand course equivalencies doesn\u2019t conflict with accreditation standards. <\/p>\n
\u201cThere\u2019s a myth that accreditors don\u2019t allow for certain kinds of considerations during credit evaluation,\u201d said Heather Perfetti, president of the Middle States Commission on Higher Education and chair of CRAC. \u201cAccreditors do actually promote innovation and encourage institutions to reconsider the ways they are managing transfer.\u201d<\/p>\n
While the majority of community college students say their goal is to transfer to a four-year university, fewer than half of those who transfer earn a degree within six years. That\u2019s in part because transferring credits to a new college is an inconsistent, time-consuming and decentralized process that often increases the cost of and time to degree completion. <\/p>\n
In 2024, four in ten adult Americans reported trying to transfer credit, and 58 percent of them reported some credit loss, according to a survey from the nonprofit research group Public Agenda; 20 percent of those who tried to transfer credit reported having to repeat a class they already took because their credits didn\u2019t transfer. Having to repeat a course caused 13 percent of respondents to run out of financial aid. For 16 percent, the process of transferring credits was so difficult that they gave up entirely on pursuing a degree or credential.<\/p>\n
Faculty and administrators say manually evaluating courses for transfer is too labor-intensive. At the same time, research shows that decisions about which transfer credits to accept are often guided by inconsistent, arbitrary guidelines and bias; more white respondents (76\u00a0percent) than Black (66\u00a0percent) and Latino respondents (64\u00a0percent) said their new college accepted all or most of the credits they tried to transfer, according to the Public Agenda survey. <\/p>\n
But AI\u2014which can analyze large data sets related to course descriptions, enrollment and learning outcomes\u2014can help streamline the learning-evaluation process and reduce credit loss. <\/p>\n
\u201cFaculty want to know if what is taught on a student\u2019s transcript is comparable to what they are teaching in terms of the learning objectives,\u201d said Zachary Pardos, an associate professor of education at the University of California, Berkeley, who has studied AI and course evaluation. \u201cIn my research and other reports that have come out, most faculty are looking for about a 70\u00a0percent overlap, and AI is well positioned to find out if the courses have that overlap.\u201d <\/p>\n
In 2024, Pardos partnered with the American Association of Community Colleges, the Association of Public and Land-grant Universities and the consulting firm Sova to launch the AI Transfer and Articulation Infrastructure Network. This year, a pilot cohort of 59 institutions is using the platform CourseWise, which engages AI to analyze course equivalencies between participating colleges and identify new or expanded matches that count toward degree completion. <\/p>\n
AI, he added, has the potential to usher in a \u201ccultural shift\u201d away from faculty erring on the side of not accepting a course for transfer. <\/p>\n
\u201cCRAC\u2019s statement gives leadership at institutions the right moment to make a decision to do something innovative, whether that\u2019s exploring the approach we\u2019re experimenting with right now or other innovative approaches,” Pardos said. \u201cThe accreditors aren\u2019t saying it\u2019s time for a free-for-all, where we give credit for everything. It\u2019s that AI is at a sufficient level of accuracy that you can use a trust-but-verify approach.\u201d<\/p>\n
Pardos\u2019s work also helped to inform CRAC\u2019s statement, which implores institutions to \u201ccommit to a default in learning evaluation that credits are applied to program completion unless there is evidence that the required learning outcomes are not met,\u201d combined with considering AI for learning evaluation. \u201cDecision-making should not be based upon anecdotes, assumptions about quality, locations where earned, or an unexamined history of \u2018how things have always been done.\u2019\u201d<\/p>\n
John Fink, a senior research associate and program lead at the Community College Research Center at Columbia University\u2019s Teachers College, said he\u2019s hopeful that CRAC\u2019s push to get institutions to explore AI may help \u201cflip the system from the default of \u2018credits do not transfer unless there is evidence that they should\u2019 to the default of \u2018courses that meet certain equivalency criteria using predictive algorithms are deemed transferable unless there is evidence to the contrary.\u2019\u201d<\/p>\n
However, \u201cthe devil will be in the details of how AI is being used to identify which courses are equivalent and how reliable this is when implemented on a large scale,\u201d he added. \u201cBut if the field can figure this out, it would certainly speed up transfer credit evaluations and help students while also building in checks from academic departments as needed.\u201d <\/p>\n
CRAC\u2019s statement suggests that AI and other technological innovations may be used to:<\/p>\n
\u201cThis statement is about encouraging institutions to examine deeply their current learning evaluation practices. That could be examining how AI could be leveraged to support some of the challenges with processing transfer,\u201d said Perfetti, the CRAC chair. \u201cCertainly, there is controversy around AI and the ways that it can and should be used. But it\u2019s clear that AI can play a solid role in transfer credit and get at some of those barriers we know exist with transfer credits: the slow nature of processing, the inequity of decisions and the loss of credit without good reason or rationale.\u201d<\/p>\n
The goal of CRAC isn\u2019t to take humans out of the course evaluation loop entirely, but to enhance their decision-making about which transfer credits to accept, said Stephen Pruitt, president of the Southern Association of Colleges and Schools.<\/p>\n
\u201cEvaluating course credit, transfer and content gets so decentralized that it can be interpreted many different ways,\u201d he said. \u201cWhat AI should be able to do is make that a little more seamless and aid in that evaluation so that it doesn\u2019t depend on which professor gets to decide if the course material is appropriate or aligned. There\u2019s actually something that puts that initial stake in the ground that provides humans with more uniformity in how they approach that work.\u201d<\/p>\n
The CRAC statement is also a sign from accreditors that they encourage embracing AI to advance student success, at a time when many higher education institutions have been slow to develop comprehensive AI guidelines. <\/p>\n
\u201cWe\u2019ve been silent on it, and we can\u2019t afford to remain silent. As long as we stay silent, others make assumptions about what they can or can\u2019t do with that technology,\u201d Pruitt said. \u201cCRAC just simply releasing a statement saying, \u2018This is a good thing and you should embrace it\u2019 isn\u2019t going to make anything different or better. It\u2019s going to be institutions stepping up to the plate and deciding how they\u2019re going to use it and [make it] work for them.\u201d<\/p>\n
But the CRAC statement may help put some momentum behind the idea of using AI to improve course evaluation, said George Railey Jr., vice chancellor for academic success for the Alamo Colleges District and a member of the Beyond Transfer Policy Advisory Board.<\/p>\n
\u201cEducational entities are slow to change, but things are moving so rapidly. Enrollments and budgets are declining, and colleges need a way to bring more students to their campuses,\u201d he said. \u201c[CRAC\u2019s statement] puts the force of the accrediting commission behind efforts to move institutions into the present and perhaps the future with AI to advance student completion and success.\u201d<\/p>\n
(This article has been updated to better reflect the results of Public Agenda’s survey.)<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" A group of college accreditors is backing the use of artificial intelligence to reduce credit loss during transfer, which is a major barrier to completion for many of the 43\u00a0million people across the nation with some college credit but no degree. \u201cCurrent approaches frequently result in delays in students receiving necessary information about how their<\/p>\n","protected":false},"author":1,"featured_media":26415,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[57],"tags":[12205,650,2575,6667,3136,3364],"class_list":{"0":"post-26414","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-education","8":"tag-accreditors","9":"tag-boost","10":"tag-credit","11":"tag-encourage","12":"tag-process","13":"tag-transfer"},"_links":{"self":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts\/26414","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=26414"}],"version-history":[{"count":0,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts\/26414\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/media\/26415"}],"wp:attachment":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26414"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=26414"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=26414"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}