1. The output is only as good as the input (prompt): High-quality research questions and precise prompts are fundamental for obtaining relevant analytical results.
2. Assessing the truthfulness of the output requires subject knowledge: Critical thinking and fact-checking are essential for scientific integrity.
3. The author is always responsible for their work: Whether the output is generated by AI or not, the final responsibility for its content lies with the author or submitter. Citing AI errors is not a valid excuse.
4. Respect copyright and citation ethics: When using AI, it is necessary to follow academic integrity principles and correctly cite all sources.
5. Do not misuse AI for data fabrication or plagiarism: Academic work demands honesty and transparency.
6. Proper use of AI can enrich research and accelerate learning: AI can be a powerful tool for data analysis, hypothesis generation, and fostering creative thinking.
7. Protect data confidentiality and respect privacy: When working with personal data, it is necessary to comply with relevant laws and ethical standards.
8. Be cautious of echo chambers: AI tools aim to satisfy users, often generating or offering content that aligns with their preferences. This is most evident on e-shops or social media in the form of “for you” content. It is important to be aware of this and intentionally break out of such bubbles.
9. Be alert to biased data and algorithmic prejudice: AI results can be affected by the quality and representativeness of input data.
10. Subscribe for limited periods (max 3 months): Regular evaluation of tools is key to effective and cost-efficient research.

The Ten Guidelines build on the university's Position on the Use of Artificial Intelligence and Recommendations for the Use of AI Tools.
_{* Text of the Ten Principles by Lucie Racyn, M.A., M.A. (Central Library, Faculty of Arts, Masaryk University).}