How words shape the debate about AICEFR A2
16 Apr 2026
Adapted from Daria Dergacheva, Global Voices • CC BY 3.0
Photo by Brett Jordan, Unsplash
When OpenAI made ChatGPT available in November 2022, public debate about artificial intelligence changed. By 2026 the topic remained central and people discussed both risks and benefits.
Generative AI had mixed effects: it disrupted education, gave some coders new tools, and was used in war. Many companies still lack clear business models, yet leaders often describe their systems with human words.
Researchers note that errors are common and that calling them "hallucinations" can mislead people. Some experts worry this language gives machines too much moral or legal status.
Difficult words
- debate — a public discussion with different opinions
- generative — making new things like text and images
- disrupt — to change something in a sudden waydisrupted
- model — a system that makes predictions or choicesmodels
- hallucination — an incorrect or false output from a systemhallucinations
- researcher — a person who studies a subject carefullyResearchers
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- Have you used ChatGPT or another AI tool? What happened?
- Do you think calling errors "hallucinations" is confusing? Why or why not?
Related articles
Reducing unsafe responses in large language models
Researchers studied how large language models (LLMs) handle safety and tested training methods to reduce unsafe outputs while keeping performance. They identified key challenges and a technique that preserves safety during fine-tuning.
Chinese electric car makers shift to Africa as Western markets close
After price pressure at home and new US and EU trade barriers, many Chinese electric vehicle firms are moving into Africa for sales and assembly. Governments and companies plan factories, dealerships and incentives.
LLMs change judgments when told who wrote a text
Researchers at the University of Zurich found that large language models change their evaluations of identical texts when given an author identity. The study tested four models and warns about hidden biases and the need for governance.