@article {861,
	title = {Automated Test Generation and Marking Using LLMs},
	journal = {Electronics},
	volume = {14},
	year = {2025},
	pages = {2835},
	abstract = {<p>This paper presents an innovative exam-creation and grading system powered by advanced natural language processing and local large language models. The system automatically generates clear, grammatically accurate questions from both short passages and longer documents across different languages, supports multiple formats and difficulty levels, and ensures semantic diversity while minimizing redundancy, thus maximizing the percentage of the material that is covered in the generated exam paper. For grading, it employs a semantic-similarity model to evaluate essays and open-ended responses, awards partial credit, and mitigates bias from phrasing or syntax via named entity recognition. A major advantage of the proposed approach is its ability to run entirely on standard personal computers, without specialized artificial intelligence hardware, promoting privacy and exam security while maintaining low operational and maintenance costs. Moreover, its modular architecture allows the seamless swapping of models with minimal intervention, ensuring adaptability and the easy integration of future improvements. A requirements{\textendash}compliance evaluation, combined with established performance metrics, was used to review and compare two popular multilingual LLMs and monolingual alternatives, demonstrating the system{\textquoteright}s effectiveness and flexibility. The experimental results show that the system achieves a grading accuracy within a 17\% normalized error margin compared to that of human experts, with generated questions reaching up to 89.5\% semantic similarity to source content. The full exam generation and grading pipeline runs efficiently on consumer-grade hardware, with average inference times under 30 s.</p>
},
	keywords = {artificial intelligence, DeepSeek, exams, large language models, Llama, named entity recognition, Natural language processing, question{\textendash}answer generation, retrieval-augmented generation, Semantic Similarity, test generation, test marking},
	issn = {2079-9292},
	doi = {https://doi.org/10.3390/electronics14142835},
	author = {Papachristou, Ioannis and Dimitroulakos, Grigoris and Vassilakis, Costas}
}
@article {857,
	title = {Stitching History into Semantics: LLM-Supported Knowledge Graph Engineering for 19th-Century Greek Bookbinding},
	journal = {Machine Learning and Knowledge Extraction},
	volume = {7},
	year = {2025},
	pages = {59},
	abstract = {<p>Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conservation practices. However, existing bibliographic records often lack the depth needed to analyze bookbinding techniques, provenance, and preservation status. This paper presents a proof-of-concept system that explores how Large Language Models (LLMs) can support knowledge graph engineering within the context of 19th-century Greek bookbinding (1830{\textendash}1900), and as a result, generate a domain-specific ontology and a knowledge graph. Our ontology encapsulates materials, binding techniques, artistic styles, and conservation history, integrating metadata standards like MARC and Dublin Core to ensure interoperability with existing library and archival systems. To validate its effectiveness, we construct a Neo4j knowledge graph, based on the generated ontology and utilize Cypher Queries{\textemdash}including LLM-generated queries{\textemdash}to extract insights about bookbinding practices and trends. This study also explores how semantic reasoning over the knowledge graph can identify historical binding patterns, assess book conservation needs, and infer relationships between bookbinding workshops. Unlike previous bibliographic ontologies, our approach provides a comprehensive, semantically rich representation of bookbinding history, methods and techniques, supporting scholars, conservators, and cultural heritage institutions. By demonstrating how LLMs can assist in ontology/KG creation and query generation, we introduce and evaluate a semi-automated pipeline as a methodological demonstration for studying historical bookbinding, contributing to digital humanities, book conservation, and cultural informatics. Finally, the proposed approach can be used in other domains, thus, being generally applicable in knowledge engineering.</p>
},
	keywords = {bookbinding history, Cultural heritage, knowledge graphs, large language models, ontology engineering},
	issn = {2504-4990},
	doi = {https://doi.org/10.3390/make7030059},
	author = {Doumanas, Dimitrios and Ntalouka, Efthalia and Vassilakis, Costas and Wallace, Manolis and Kotis, Konstantinos}
}
