Sep 2026· Media Practice and Education· pp. 1-19· 26 references
Abstract
This study explores how journalism students use generative AI in their learning and professional skills development and analyzes their perceptions of the benefits, challenges, and solutions for using AI effectively and responsibly in journalism education. The study employs a qualitative approach through semi-structured in-depth interviews with 21 journalism students from four higher education institutions in Hanoi. Data were analyzed using thematic analysis with the support of Taguette software and interpreted based on the AI Literacy theoretical framework. The findings reveal three major themes. First, students use AI to support information retrieval, idea generation, content editing, and journalistic content production, viewing it as a tool that enhances learning efficiency and professional skills development. Second, participants expressed concerns regarding information reliability, professional ethics, copyright, and over-reliance on AI, while emphasising the importance of critical thinking, fact-checking, creativity, and field reporting. Third, students advocated the responsible integration of AI into journalism education through AI literacy development, ethical guidance, and transparent institutional policies. This study contributes empirical evidence from the Vietnamese context and extends the application of the AI Literacy framework in journalism education by conceptualising AI literacy as a professional competency encompassing functional AI use, critical evaluation, and responsible practice.
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
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026