When thinking about generative AI and its disruptive impact, text generation often comes up as the most representative example of this new chapter in technological advancement. Large language models (LLMs) are rapidly transforming sectors that have at their core text generation tasks such as writing, drafting or summarisation, and the online news industry has been challenged in adapting to these new tools since GPT (generative pre-trained transformer) models became known to the mass public in late 2022.

Imagine you would like to write an essay about quantum computing, but your knowledge about quantum computing comes only from your high school textbooks. In this case, you know how to write good papers, but your knowledge is limited. Now imagine if you could access any library in the world while writing. That would make your work easier, and it’s essentially what retrieval-augmented generation (RAG) does for large language models (LLMs).

As the academic field increasingly implements technology to assist with output, LLMs have moved to centre stage, proving they can serve as powerful co-pilots, assisting with understanding abstract concepts, ideation, language-based prototyping, documentation and communication across disciplines. In educational settings, particularly with design students, LLMs have great potential as Creativity Support Tools (Frich Pedersen et al., 2018) and design material (Yu, 2025).