Sep 2026· Frontiers in Communication· 14 references
Abstract
The emergence of historical channels mediated by artificial intelligence (AI) on social media platforms constitutes an emerging communicative phenomenon that the academic literature in visual communication has not yet addressed in a systematic way. Chloe VS History, a YouTube and TikTok channel, offers a paradigmatic case of this new digital narrative. Based on three consolidated theoretical frameworks: the grammar of visual design by Kress and van Leeuwen, the gatekeeping theory by Shoemaker and Vos, and the concept of epistemic injustice by Fricker, the convergence construct of visual hyperrealism is proposed to name the simultaneous intersection of three conditions: the production of images of technical fidelity indistinguishable from a real record, a distribution environment without institutional verification mechanisms, and a good part of audiences without critical tools to evaluate that content. It is proposed that this convergence could generate a form of new epistemic risk that none of the three frameworks, in isolation, manages to fully capture, and that would help explain how a historical inaccuracy could be perceived as true without encountering any institutional or cognitive barrier. The central problem is not the use of AI in itself, nor the story as content, nor entertainment as a format, but its specific articulation in an ecosystem where visual credibility operates independently of factual fidelity. Ethical implications are derived for the individual production of historical content with AI and for the role of distribution platforms, and the analytical limits of the case are discussed in order to open an empirical research agenda in this emerging field.
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
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
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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