Maastricht University

II-A. Intended learning objectives reliability, validity, and transparency with the use The effect of GenAI on the intended learning of GenAI in mind. objectives (ILOs) is to be evaluated on a programme-by-programme basis. Where possible, 11. Study programmes need to consider how evaluations are carried out jointly, focusing on: examination boards will be able to determine that students have personally attained the ILOs, 6. Complementing the current ILOs on traditional given permitted use of GenAI. academic skills with relevant ILOs on the development of skills related to GenAI. 12. Definitions of fraud and plagiarism are revised in the context of GenAI, and rules developed with 7. Ensuring the relevance of the current ILOs to regard to permitted uses. the changing professional field (e.g. altered or obsolescent work processes). 13. The rules and guidelines of the relevant examination boards are followed at all times.

Recopilado: 2026-10-09 · 215–228
SHA-256: de6a8bf74e2cbbe284f699ee4dd7b8c34529d5a576387f18ca16049142964847

Condiciones y excepciones

Relevant EER and University policy prevail; activities hosted elsewhere follow the host faculty unless deans agree otherwise.

Default. Generative AI may be used by default; restrictions are applied where needed to protect assessment validity, academic integrity, or legal/ethical requirements. Assessment instructions. For each assessment, the assessment instructions (assignment sheet/LMS/rubric/syllabus) state permitted uses, any conditions (incl. disclosure), and any restrictions. Responsibilities. Students and staff uphold academic integrity and comply with the assessment instructions. Support. The faculty ensures training and student support for responsible use of generative AI. 3. Requirements Teaching & Learning. Students may use generative AI by default, provided academic integrity is maintained and their own achievement remains assessable. Teaching/learning are designed assuming generative AI is available; where its use would undermine learning or assessment validity/integrity, restrictions or conditions are set in the assessment instructions. Assessment & QA. Generative AI is allowed by default; restrictions are introduced when needed to protect assessment validity, academic integrity, or legal/ethical requirements. Where generative AI use is not allowed for a specific task, the module’s assessment design, taken together, includes reasonable safeguards that make unauthorised generative AI use unlikely and ineffective for achieving a pass in the module without demonstrable personal achievement. The module assessment plans and the programme assessment plan (toetsprogramma) must demonstrate measurable attainment of module/programme ILOs and be robust to unauthorised generative- AI use. Within the PDCA cycle, programmes review changed summative assessments for generative-AI-related risks and record mitigations. Before awarding credits or the degree, the BoE must be satisfied that the assessed work credibly evidences the student’s own achievement; the BoE is not required to investigate the precise means by which the work was produced and may, on reasonable grounds to doubt authorship, require a verification assessment (e.g., oral defence or supervised reproduction). Staff retain professional autonomy within this policy; structural or substantial deviations are justified with programme leadership. Students. Students must submit work that credibly evidences their own achievement; generative AI may be used only as permitted in the assessment instructions and must be disclosed as required. Misrepresentation may constitute academic misconduct handled under the applicable programme EER and faculty/university regulations.

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SHA-256: e652b4510ff1e1b6b7c59e69f29af14d90f31a0d14692dc7ec041b22b489d3e4

This framework supplements the programme Education and Examination Regulations (EERs) and applicable university policy. In the event of any inconsistency, the relevant EER and university policy prevail. 2. Scope & applicability The policy framework applies to all activities related to teaching, learning, and assessment under the responsibility of FSE. It is intended for all teaching staff, students, support staff, and guest lecturers involved in FSE education, regardless of their primary faculty affiliation or employment contract. All FSE students and staff are expected to be familiar with this policy framework and to act in accordance with its recommendations. Where educational activities involve collaboration with other faculties or external partners, the applicable policy framework depends on where the activity takes place. Students and staff coming into FSE to take or teach courses are bound by the FSE policy framework, while FSE students and staff participating in activities hosted by another faculty are bound by that faculty’s framework, unless otherwise agreed by the deans of the respective faculties.

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SHA-256: e652b4510ff1e1b6b7c59e69f29af14d90f31a0d14692dc7ec041b22b489d3e4

5. Policy framework for students - Students are expected to submit their own work. Where genAI is used, students must follow the assessment instructions, disclose such use when required, and uphold academic integrity. - Submitting work that does not represent a student’s own achievement prevents teaching staff and examiners from reliably assessing learning and may be treated as academic misconduct, with consequences under faculty and university regulations. - Where any external assistance is used (including AI tools, services, or people), it must be acknowledged in line with the assignment instructions. - If there is reasonable doubt about the authorship or originality of submitted work, students may be required to provide clarification or evidence that the work is genuinely their own. - Any attempt to circumvent this framework or to misrepresent achievement may be treated as academic

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SHA-256: e652b4510ff1e1b6b7c59e69f29af14d90f31a0d14692dc7ec041b22b489d3e4

our core values as an institution. referenced appropriately by researchers.12 Furthermore, it is important that researchers 1. Researchers hold final accountability for the disclose the use of GenAI during ethical review by ethical use of GenAI tools in their research and act the UM ethical committees (if applicable). in accordance with the Netherlands Code of Conduct for Research Integrity,8 rules on the IV. Operations processing of personal data9 and all UM codes of conduct and policy,10 including the code of Beyond education and research, UM strives to conduct for data management.11 Extra caution is facilitate the responsible use of GenAI in the required when processing non-anonymized day-to-day work of all employees. personal data or privacy-sensitive information, with strict adherence to applicable regulations 1. GenAI tools are never used to process personal (see Appendix A) and, if necessary, approval from or organisation-sensitive data in the absence of the ethics committee. To avoid potential breaches transparency about how these data are used and of confidentiality, researchers should also be without safeguarding that all applicable aware that existing confidentiality agreements conditions for the use of GenAI have been met. may apply to unpublished research when using The existing rules on the handling of personal and GenAI. organisation-sensitive data apply, such as the provisions on confidentiality from the 2. Researchers need to have a solid understanding employment contract. Sensitive data may not be of the capabilities and limitations of GenAI shared with third parties. As free, unsecured AI models/applications. This does not require tools are regarded as unauthorised third parties, advanced technical expertise, but should suffice sharing information with such tools essentially to enable them to use GenAI in their research in means making that information public, which an ethical and responsible manner. To this end, amounts to a data breach.

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SHA-256: de6a8bf74e2cbbe284f699ee4dd7b8c34529d5a576387f18ca16049142964847