This paper presents a transformer-based framework for Natural Language Processing (NLP) analysis of university reading lists, addressing representational diversity in higher education curricula. We apply sentence embeddings, clustering, topic modelling, and Shannon entropy to examine thematic patterns within academic reading lists, a novel application domain for modern NLP methods. Our approach identifies latent topic structures and quantifies thematic diversity across institutional contexts. Testing on 40 semantically aligned readings from Islamic Studies programmes at UK and Middle Eastern universities, the framework reveals significant differences in curricular emphasis despite surface-level similarity. The methodology demonstrates how transformer-based analysis can detect subtle epistemological patterns that traditional curriculum auditing methods cannot capture. This NLP approach offers scalable tools for curriculum evaluation, supporting evidence-based discussions around academic inclusivity and knowledge representation in higher education.