For students it is not allowed to use AI for generating ready-made content (see rules Examining Board), and for PhD candidates there are rules and regulations as well. But there are many ways in which AI-tools can be a helpful addition to your toolkit, while adhering to the rules. For example, you might use AI: 

• as brainstorming tool. You could use AI to provide inspiration when coming up with creative ideas. 
• to improve your own writing. White AI tools may not be used to write full texts or paragraphs, you can use them to improve your own writing by asking for personalized feedback on it. Rewriting of texts to improve readability or to bridge language barriers is a useful and promoted practice.
• for practicing for debates/discussions. You can prepare for discussions or debates by having an AI tool act as your opponent. 

• in literature research. You can use literature-focused AI tools to orientate yourself on relevant academic literature on your chosen topic.  
• to improve or debug your code. To language models, code is just another language, and as such they can help write and improve code, and to fix errors.
• to generate images. You can create images as supporting images for presentations. Do not use these images to replace photographs you could have taken yourself, however. 
• … and many more.

When using GenAI there are a few terms that are commonly used. Knowing these may help you better understand the technology and the rules applied to them better. 

Prompt: A question or instruction given to a generative AI model, typically written in natural language (human-spoken languages such as English, Dutch or Chinese). 

Prompt engineering: The practice of precisely formulating your prompt to most effectively provide a task or question to an AI model. 

Token: A single word or punctuation mark.  

Context length: The maximum ‘memory’ of a model, expressed in the number of tokens. This memory includes both the input and output of the model. 

LLMs: Large Language Models, a collective term for modern textual generative AI models. 

Inference: The process where a trained AI model applies its learned knowledge to new situations to make predictions, decisions, or to generate content. 

If you have any AI-related questions, concerns, or comments after reading this information, there are several people you can approach. For questions on AI in literature research, you can contact the library ([email protected]). For general questions, concerns or suggestions about the use of GenAI within the WUR, feel free to email us at [email protected]. We would like to hear what’s on your mind!