Multilingual Mentoring: Our approach to generative AI (genAI)

This is an easy-read version of our project stance document on the use of genAI, the full version of which can be found here.

What is generative AI?

Artificial intelligence (AI) is technology that can carry out tasks that we normally associate with human intelligence.

Generative AI, or genAI, refers to tools such as ChatGPT, Microsoft Copilot, Gemini, and Claude. These tools can produce new content, including text, images and ideas, in response to instructions from a user. 

These tools can be useful, but they also have important limitations and risks.

At Multilingual Mentoring, we believe the most important thing is to think critically about genAI. This means understanding what these tools can and cannot do, questioning their answers, and making informed choices about whether and how to use them. It is also important to understand the impact that these tools have.

We do not tell mentors, teachers, or other people involved in the project that they must or must not use genAI. However, the project team takes a cautious approach and does not rely on genAI for its work. If we do use genAI, we will be open about this (see the transparency statement at the bottom of this page).

How can generative AI be useful?

Used carefully, genAI can help with some tasks.

Developing ideas

GenAI can suggest ideas or different ways of approaching a task. This might be useful when planning an activity or creating a resource.

However, genAI suggestions should always be checked, questioned, and edited by a person.

Saving time

GenAI may help with simple, repetitive, or administrative tasks. This can leave more time for work that needs human judgement, creativity, or decision-making.

Making information more accessible

GenAI can sometimes simplify difficult texts, adapt information, for different reading levels, or present information in another format.

Working across languages

GenAI can help draft or adapt content in different languages. However, the quality is not the same for every language. Languages with less representation in AI training data, including Welsh, can produce less reliable results.

Translations should therefore be checked by someone who knows the language well.

What are the risks?

Using genAI also comes with risks. Some can be reduced by using AI carefully, but others are connected to the way the technology itself works.

AI can get things wrong

GenAI can produce answers that sound confident and convincing but are incorrect, incomplete, out of date, or even made up.

What should you do?
Check important information using trustworthy sources rather than assuming an genAI answer is correct.

Becoming too reliant on AI

If we automatically ask AI to think or create for us, we may have fewer opportunities to develop our own ideas, judgement, and problem-solving skills. GenAI systems can also give us answers that agree with us rather than challenging our assumptions.

What should you do?
Think for yourself first. Use genAI as a possible support, not as a replacement for your own thinking and creativity.

Bias and stereotypes

GenAI learns from large amounts of human-created information. That information contains biases and stereotypes, and AI can repeat or strengthen them.

What should you do?
Think critically about genAI-generated content. Ask whose voices or experiences might be missing and whether the output treats people and communities fairly.

Privacy and personal information

Information entered into some genAI tools may be stored or processed in ways that are not always clear.

This is especially important when working with information about young people, schools, or other individuals.

What should you do?
Never put personal, confidential, or sensitive information into a genAI tool unless you are certain that you are allowed to do so and that the tool is approved for that purpose.

Ownership and plagiarism

There are important questions about where AI-generated content comes from and who owns it. GenAI systems have been trained on huge amounts of existing material, including material created by writers, artists, and other people.

You should also be honest when genAI has contributed to your own work.

What should you do?
Follow the relevant rules about copyright and academic integrity, and clearly state when genAI has been used to help produce work.

Environmental impact

Large AI systems require significant computing power. Running them uses energy and water, and the data centres that support AI can also affect local communities.

What should you do?
Think about whether genAI is actually needed for a particular task rather than using it automatically.

Our key message

Generative AI can be useful, but useful does not always mean accurate, fair, safe, or necessary.

When deciding whether to use it, we encourage everyone involved in Multilingual Mentoring to:

  1. Think first: do you actually need AI for this task?
  2. Protect privacy: don’t share sensitive or personal information.
  3. Check the output: AI can make mistakes.
  4. Look for bias: consider whose perspectives might be missing.
  5. Keep thinking for yourself: AI should support rather than replace human judgement and creativity.
  6. Be transparent: if AI has contributed to your work, say so.

The most important skill is knowing when, why, and whether to use AI in the first place.

Bibliography and useful resources

Artificial intelligence (AI) | Definition, Examples, Types, Applications, Companies, & Facts | Britannica. 2026. Available at: https://www.britannica.com/technology/artificial-intelligence.

Beckingham, S., Lawrence, J., Powell, S., Hartley, P. and Francis, T.&. 2024. Using generative AI effectively in higher education: Sustainable and ethical practices for learning, teaching and assessment. London: Routledge.

Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D. and Jurafsky, D. 2026. Sycophantic AI decreases prosocial intentions and promotes dependence. Science 391(6792).

Chidlow, D. 2026. AI terminology is poorly defined and oft misused. Available at: https://vale.rocks/posts/ai-terminology.

Generative AI & education guidance hub. [no date]. Available at: https://www.imperial.ac.uk/about/leadership-and-strategy/provost/vice-provost-education/education/resources/ai-education-hub/.

Hendrycks, D. et al. 2025. A definition of AGI. [Working paper].

Hosseini, M., Gao, P. and Vivas-Valencia, C. 2025. A social-environmental impact perspective of generative artificial intelligence. Environmental Science and Ecotechnology 23, p. 100520.

King’s guidance on generative AI for teaching, assessment and feedback. [no date]. Available at: https://www.kcl.ac.uk/about/strategy/learning-and-teaching/ai-guidance.

Moravec, V., Gavurova, B. and Kovac, V. 2025. Environmental footprint of GenAI – Changing technological future or planet climate? Journal of Innovation & Knowledge 10(3), p. 100691.

Shaw, S.D. and Nave, G. 2026. Thinking—Fast, slow, and artificial: How AI is reshaping human reasoning and the rise of cognitive surrender. [Working paper].

Tully, S.M., Longoni, C. and Appel, G. 2025. Lower artificial intelligence literacy predicts greater AI receptivity. Journal of Marketing 89(5), pp. 1–20.

Zewe, A. 2025. Explained: Generative AI’s environmental impact. Available at: https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117.

Transparency statement

This document was produced with limited assistance from Microsoft Copilot, a genAI tool licensed under Cardiff University’s tenancy. Its use formed part of the project’s commitment to engaging critically with generative AI technologies. AI-generated suggestions were evaluated alongside other sources and professional judgement, and responsibility for the final content rests with the human author.

For queries or further information, please contact mflmentoring@cardiff.ac.uk.