@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 {862,
	title = {A Machine Learning Framework for Harvesting and Harmonizing Cultural and Touristic Data},
	journal = {Information},
	volume = {16},
	year = {2025},
	pages = {1038},
	abstract = {<p>Cultural and touristic information is increasingly available through a multitude of heterogeneous sources, including official repositories, community platforms, and open data initiatives. While prominent landmarks are typically covered across sources, less-known attractions are also documented with varying degrees of detail, resulting in fragmented, overlapping, or complementary content. To enable integrated access to this wealth of information, harvesting and consolidation mechanisms are required to collect, reconcile, and unify distributed content referring to the same entities. This paper presents a machine learning-driven framework for harvesting, homogenizing, and augmenting cultural and touristic data across multilingual sources. Our approach addresses entity resolution, duplication detection, and content harmonization, laying the foundation for enriched, unified representations of attractions and points of interest. The framework is designed to support scalable integration pipelines and can be deployed in applications aimed at tourism promotion, digital heritage, and smart travel services.</p>
},
	keywords = {Cultural heritage, data augmentation, data homogenization, digital heritage, Machine Learning, named entity recognition, social media analysis, tourism analytics, trajectory extraction, web scraping},
	issn = {2078-2489},
	doi = {10.3390/info16121038},
	url = {https://www.mdpi.com/2078-2489/16/12/1038},
	author = {Deligiannis, Kimon and Tryfonopoulos, Christos and Raftopoulou, Paraskevi and Vassilakis, Costas and Kaffes, Vassilis and Skiadopoulos, Spiros}
}
