Transferring from a 2-year to a 4-year college is crucial for socioeconomic mobility, yet students often face challenges ensuring their credits are fully recognized, leading to delays in their academic progress and unexpected costs. Determining whether courses at different institutions are equivalent (i.e., articulation) is essential for successful credit transfer, as it minimizes unused credits and increases the likelihood of bachelor's degree completion. However, establishing articulation agreements remains time- and resource-intensive, as all candidate articulations are reviewed manually. Although recent efforts have explored the use of artificial intelligence to support this work, its use in articulation practice remains limited. Given these challenges and the need for scalable support, this study applies artificial intelligence to suggest articulations between institutions in collaboration with the State University of New York system, one of the largest systems of higher education in the US. To develop our methodology, we first surveyed articulation staff and faculty to assess adoption rates of baseline algorithmic recommendations and gather feedback on perceptions and concerns about these recommendations. Building on these insights, we developed a supervised alignment method that addresses superficial matching and institutional biases in catalog descriptions, achieving a 5.5-fold improvement in accuracy over previous methods. Based on articulation predictions of this method and a 61% average surveyed adoption rate among faculty and staff, these findings project a 12-fold increase in valid credit mobility opportunities that would otherwise remain unrealized. This study suggests that stakeholder-informed design of AI in higher education administration can expand student credit mobility and help reshape current institutional decision-making in course articulation.

翻译：从两年制学院转入四年制大学对于社会经济流动至关重要，然而学生常常面临学分无法被完全认可的挑战，导致学业进度延迟和意外成本。确定不同院校课程是否等效（即课程衔接）对于成功的学分转移至关重要，因为这能最大限度地减少未使用学分并提高学士学位完成的可能性。然而，建立课程衔接协议仍然耗时耗力，因为所有候选衔接课程均需人工审核。尽管近期研究探索了利用人工智能支持此项工作，但人工智能在衔接实践中的应用仍然有限。鉴于这些挑战以及对可扩展支持的需求，本研究应用人工智能为纽约州立大学系统（美国最大的高等教育系统之一）内的院校间课程衔接提供建议。为开发我们的方法，我们首先调查了衔接工作人员和教职员工，以评估基线算法建议的采纳率，并收集关于这些建议的认知与担忧的反馈。基于这些见解，我们开发了一种监督对齐方法，解决了课程目录描述中的表面匹配和机构偏见问题，相比先前方法实现了5.5倍的准确率提升。根据该方法的衔接预测以及教职员工平均61%的调查采纳率，这些发现预计可将原本无法实现的有效的学分流动机会提升12倍。本研究表明，高等教育管理中基于利益相关者知情设计的人工智能能够扩展学生的学分流动性，并有助于重塑当前课程衔接中的机构决策。