AI and NLP driven approaches to curriculum–industry alignment in higher education: A systematic literature review

This article presents a systematic literature review of Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches to align higher education curricula with industry skill requirements. Rapid technological change requires universities to continuously adapt curricula to evolving labour-market needs. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 protocol, 411 studies published between 2015 and 2025 were screened, and 36 met the inclusion criteria. The review identifies a methodological shift from rule-based and classical NLP techniques to transformer-based contextual models, reflecting growing sophistication in this research area. Despite these advances, persistent challenges include monolingual bias, lack of standardized datasets, fragmented methodologies, and limited validation. To address these issues, the paper proposes a decision-tree framework linking dataset characteristics, model complexity, and evaluation strategies to suitable AI/NLP methods, supporting consistent curriculum reform aligned with industry demands.