Data literacy, algorithmic literacy, and computational thinking are inseparable concepts. Together, these skills form an essential foundation for critically and ethically understanding and using artificial intelligence in journalism.

They are not trusted, but they are used. They are not entirely reliable and accurate, but journalists experiment daily with large language models (LLMs), which are likely to be used at each stage of the news process. What are the key risks and how can they be mitigated to ensure safe and meaningful use?

AI-based technologies are both a tool to combat disinformation and a means to amplify it. However, it would be misleading to reduce the phenomenon to a purely technical problem. Where are we? Where are we going?

One of the misconceptions about AI is the concept of ‘adoption’. In Science and Technology Studies (STS), adoption is about negotiating, appropriating, and domesticating tech into routines and identities. Use is just interaction; practice is integration into professional routines (it is not adoption but can lead to it).

Journalists use AI technologies daily without necessarily understanding what’s going on behind the screen. From research to translation, transcription, and even automated content creation, AI is woven into the fabric of modern journalism. The question, then, is: What does it mean to train journalists on AI?

On social media, the attention economy exploits digital behaviours to maximise engagement. Resisting this constant pressure by adopting a critical approach and thoughtful usage is possible but requires more digital media literacy.

To ask what makes a reporter human is to ask what are the core skills of initial journalism training. This article attempts to identify the skills that are becoming obsolete and to highlight the human qualities that really make a difference.