Sep 2026· Journalism and Media· 0 citations· 29 references
Abstract
Artificial intelligence (AI), particularly generative artificial intelligence (GenAI), is influencing journalism and education by transforming professional practices, information production, and media literacy requirements. While generative AI improves efficiency in content creation and educational preparation, it also raises concerns regarding accuracy, credibility, verification, and ethical responsibility. This study examines how generative AI is experienced by journalists and teachers in primary schools in Croatia within the contemporary information ecosystem. Empirical data were collected through semi-structured interviews with ten participants, including five professional journalists and five teachers, and analysed using reflexive thematic analysis. The analysis identified a shared efficiency–verification paradox across both professional groups. Although generative AI reduces the time required for routine cognitive tasks, it simultaneously increases the need for verification, critical evaluation, and ethical judgement. Participants also emphasised the growing importance of AI literacy, critical thinking, and ethical competencies for navigating AI-generated content in professional and educational contexts. The findings suggest that generative AI does not necessarily replace professional expertise but instead redistributes professional responsibilities towards activities requiring human judgement and accountability. This study contributes to a broader understanding of how AI influences interconnected information professions and highlights the need to strengthen competencies required for responsible engagement with AI-generated information.
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
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