Leveraging NLP-based tools for constructive alignment

Abstract

Constructive alignment requires that learning outcomes, teaching activities, and assessments be coherently structured. However, verifying this alignment, especially across large curricula, remains difficult at scale. In this paper, we present a Natural Language Processing (NLP)-based approach to automatically assess the alignment of Intended Learning Outcomes (ILOs), typically comparing smaller course-level objectives to broader Graduate Learning Outcomes (GLOs). Based on expert annotations, we fine-tune an NLP classifier to predict alignment with graduate learning outcomes focused on communication skills. Our results show that NLP tools can support alignment review by surfacing ambiguous phrasing and prompting expert judgement, offering a scalable and pedagogically grounded approach to curriculum quality assurance.