This article examines the comprehensiveness of institutions’ articulation agreements and how well they utilize those agreements in granting credit to transfer students. The study introduces the Articulation Coverage Score (ACS) and the Artificial Intelligence-enhanced ACS (AI-ACS) as measures of articulation agreement coverage. Additionally, it proposes measures to identify extra equivalencies, credits awarded beyond formal articulation agreements, and unused equivalencies, credits that have been articulated but not applied for student credit. Utilizing data sets from the The State University of New York (SUNY), the largest intersegmental system of higher education, we investigating articulation agreements from 21 community colleges and 24 universities. We find a high percentage of unused credits, 39%, along with significant variability between community colleges and 4-year institutions, with average AI-ACS of 32% (SD = 15) and 34% (SD = 19), respectively.

Algorithm aversion can be described as the tendency of human decision-makers to discount algorithmic recommendations more heavily than similar recommendations made by humans. It has been a phenomenon observed to be most acutely exhibited by domain experts. In our work, we focus on expert administrators in higher education making course credit equivalency decisions that affect the academic planning and potential degree progress of millions of prospective transfer students. Using human-centered design, we construct an AI-based platform for recommending matches to courses on a student's transcript to courses offered at another institution. We conduct a 2 x 2, between-subject experiment to investigate potential aversion mitigation techniques by manipulating the presence of outliers and allowing users to provide feedback to the algorithm. Our findings indicate that intentional, human-centered design and careful presentation of algorithm-based recommendations can help improve Human-AI interaction and productivity with implications for various domains of expertise.

Planning scenarios involving user pre-specified items present themselves frequently in recommender system domains. Although next-item and next-basket recommendation has been a focus of prior research, multiple consecutive item or basket approaches are needed for planning. No prior work has leveraged pre-specified future reference items to improve this type of challenging consecutive prediction task at inference time. PLAN-BERT is the first to accommodate this general planning scenario. It does so by contributing novel modifications that take inspiration from the masked training and contextual embedding of self-attention models. To test the model, we use the domain of student academic degree planning, in which students’ past course histories and future pre-specified courses of interest are used to fill in the remainder of their curriculum. Our offline analyses consist of 15 million historic course enrollments at 20 institutions and an online evaluation conducted at one of the institutions. Our results show that PLAN-BERT outperforms existing models including BERT, BiLSTM, and a UserKNN baseline, with small numbers of future reference items substantially improving accuracy. Significant results from our online evaluation show PLAN-BERT to be strongest in students' perceptions of personalization.