Literature curriculum in Greek primary and secondary education is focused on state-assigned anthologies. Texts in those anthologies are organised in thematic groups until level 8. There is little research on how texts semantically align with those thematic groups or how themes are distributed across educational levels or in the whole thematic profile of Greek literature curriculum.

This project addresses this gap by applying computational methods across literature curriculum books. AI based methods such as sentence-embeddings, BERTopic and zero-shot assignment are used. Multiple pretrained multi-language sentence transformers embedding models are used throughout.

Initially, a way of creating stable target thematic groups from the pre-defined themes is constructed. Using sentence embeddings and BERTopic with a range of parameters and models, a set of more than 3000 groupings were formed. The resulting groups are the ones that appear together more often, using co-occurrence.

In the main part, zero-shot similarity-based assignment was used to assign texts to thematic groups centroids using distance metrics. To identify both strong and subtle themes, hard similarity (most similar text to thematic group) as well as soft similarity (distance to each thematic group) were used. Using the above assignments comparison of the assigned theme and the pre-existing theme can be performed, and the full corpus can be analysed.

The study produces results grounded in a systematic, transparent, and reproducible analytical framework, examining thematic patterns and thematic structure across the literature curriculum. The findings reveal patterns of thematic distribution and thematic overlap across all educational levels. The study does not replace human interpretation or qualitative literary analysis; rather, it complements them.

This research contributes to curriculum studies in literature by introducing large-scale AI-based semantic analysis as a neutral, computational perspective on the full corpus of school literature textbooks, offering an unbiased, reproducible approach to examining thematic structure through sentence embeddings.