Teaching & Learning
- Students are also told they remain responsible for the final submitted work and must critically review any AI-generated output
- Use of AI in graded coursework is generally not governed by a single blanket rule; it depends on the type of activity and the instructor’s design and instructions
- The university states that AI use should be specified in each assignment, and recommends designing tasks in which AI support is either unnecessary or explicitly integrated under defined conditions
- Assessment rules are left to the specific activity and should be stated explicitly by the instructor
- It also notes practical limits on controlling AI use in non-presential assessment and points instructors toward oral defenses, practical tests, and classroom work
- The university recommends in-class assessment when possible, and if assessment occurs outside class, it recommends designing tasks so that using AI is either not worthwhile or is built into the assignment under defined conditions
- Students are told to verify information, be aware that AI can hallucinate or contain bias, and avoid relying on it without human supervision for academic writing
- The university permits and encourages educational use of AI as a support for learning, including personalized learning, tutoring-style help, idea generation, and feedback, while emphasizing critical use
- Not defined
Research
- AI may be used to support research writing only with human supervision and with transparency about its use
- It also states that AI-generated text cannot be listed as an author because authorship requires human accountability
- The university states that students should not use generative AI to draft or redraft academic texts without human supervision, including scientific articles, final degree projects, master’s theses, and doctoral theses
- It explicitly warns against introducing personal or sensitive data into publicly accessible AI tools and requires users to know the origin, ownership, and privacy conditions of the data used
- The university’s general AI code of good practice requires users to consider data quality, reliability, and legal constraints when using AI systems, but it does not define a specific rule for using AI in research data analysis workflows
- It also frames AI use under broader ethical principles such as transparency, privacy, non-discrimination, fairness, and accountability
- The university sets general research-integrity style expectations for AI use: human users remain responsible for generated content, AI systems cannot be named as authors, and AI use in academic and research work must be explicitly declared
Academic Integrity
- Disclosure of AI use is required in academic and research work
- Students are also responsible for verifying AI-produced information and citations
- The university library provides citation guidance stating that AI-generated content must be identified and referenced, and emphasizes that AI use should be explicitly declared in the work
- It indicates that these tools may assist instructors, but enforcement should not rely only on detector outputs
- The university says there is no reliable way to know whether a student used open-access generative AI in non-presential assessment, and it cautions against basing evaluation decisions solely on AI-detection technologies
Institutional & Administrative
- The sources provided do not set a specific rule on recommendation letters or administrative communications
- More broadly, university personnel using AI must maintain human responsibility for outputs, assess reliability, and follow good-practice rules
- Faculty may use AI to support teaching and course design, but the university stresses critical and effective use and recommends that instructors define clearly what students may or may not do with AI in assessments
- It also advises users to understand the type of data they input and the legal implications, especially regarding privacy and confidentiality
- The sources provided discuss commercial applications of AI and good practice, but do not define a formal list of universally approved AI platforms
- The university requires careful handling of data in AI systems and prohibits entering personal, sensitive, confidential, or IP-protected information into public-access AI tools
- The university has a formal AI governance structure and institutional framework for AI adoption
- Its AI initiative also presents AI-related guidance, training, and domain-specific support across the institution
- It has approved a code of good practice for AI use, created a university AI working group, and frames AI deployment around principles such as transparency, fairness, privacy, safety, responsibility, and accountability
DocuMark: Responsible AI Use for Academic Integrity
Knowing your institution's AI policy is step one. DocuMark helps enforce it fairly by empowering universities to manage AI-generated content, prevent cheating, and support student writing through responsible AI use.
Common Questions About Public University of Navarre's AI Policies
Public University of Navarre has defined AI policies in 12 of 12 categories, with an overall coverage score of 100%.
Disclosure of AI use is required in academic and research work. The university library provides citation guidance stating that AI-generated content must be identified and referenced, and emphasizes that AI use should be explicitly declared in the work. Students are also responsible for verifying AI-produced information and citations.
The university says there is no reliable way to know whether a student used open-access generative AI in non-presential assessment, and it cautions against basing evaluation decisions solely on AI-detection technologies. It indicates that these tools may assist instructors, but enforcement should not rely only on detector outputs.
The university requires careful handling of data in AI systems and prohibits entering personal, sensitive, confidential, or IP-protected information into public-access AI tools. It also advises users to understand the type of data they input and the legal implications, especially regarding privacy and confidentiality. The sources provided discuss commercial applications of AI and good practice, but do not define a formal list of universally approved AI platforms.