Objective: This article examines the reliability of generative artificial intelligence in visualizing the early medieval past, using the Sopot stronghold as a case study. It asks whether increasingly specialized prompts improve historical accuracy and how AI-generated historical images are perceived by museum audiences. Method: A qualitative comparative experiment used a four-level prompt matrix across three topics–stronghold architecture, amber craftsmanship, and dress and weaponry–and three tools: ChatGPT (DALL·E 3), Gemini 3 (Nano Banana 2), and Adobe Firefly (Image 4). The design comprised 36 planned combinations. Several images were generated for each combination without changing the prompt, and the result considered the best within that series was selected for analysis. The assessment sheet contains 37 records because two specialist Firefly variants for dress and weaponry were retained. No additional reference materials were supplied to the models. Both authors first assessed the selected images independently using the same criteria, then compared their observations and agreed on the final assessments after jointly re-examining the images. The experiment was complemented by a pilot audience study (N = 32). Results: Increasing prompt specialization did not produce a consistent improvement in historical accuracy. Performance depended on both the tool and the task. Architectural scenes were generally easier to render than technological processes and typological details of dress and weaponry. Visually convincing images could still contain anachronisms and incorrect elements of material culture. In the audience study, photographs of reconstructions were considered more credible than AI-generated visualizations. Conclusions: A specialist prompt should not be treated as a guarantee of archaeological accuracy. In museum practice, generative AI can support prototyping, education, and alternative interpretations, but images that fill gaps in the archaeological record should be clearly labelled, documented, and subject to expert review. In such cases, they are better treated as visual hypotheses than automatically as reconstructions.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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