Applying language models (LMs) and generative artificial intelligence (GenAI) to the study of Ancient Greek offers promising opportunities. However, it faces substantial challenges due to the language’s morphological complexity and lack of annotated resources. Despite growing interest, no systematic overview of existing research currently exists. To address this gap, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology. Twenty-seven peer-reviewed studies were identified and analyzed, focusing on application areas such as machine translation, morphological analysis, named entity recognition (NER), and emotion detection. The review reveals six key findings, highlighting both the technical advances and persistent limitations, particularly the scarcity of large, domain-specific corpora and the need for better integration into educational contexts. Future developments should focus on building richer resources and tailoring models to the unique features of Ancient Greek, thereby fully realizing the potential of these technologies in both research and teaching.
Tzanoulinou et al. (Thu,) studied this question.