Dublin City University

Academic misconduct includes, but is not limited to the following: 6 ● Submitting work as your own for assessment, which has been done in whole or in part by someone else: a peer; a family member; friend or commercially or otherwise by a third party for an agreed fee; ● Submitting unauthorised Artificial Intelligence generated work; ● Plagiarism: Presenting work / ideas taken from other sources without proper acknowledgement; ● Multi-submission/Self plagiarism: i.e. resubmission of parts of a piece of your own work that was previously submitted for credit without prior consultation with the module co- ordinator; ● Selling or providing previously completed coursework to other students; ● Falsifying references and documentation; ● Breaches of Examination Regulation (please see Examination Regulations)

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SHA-256: b61c60dd2296e59517cb9de0d58fca0400be59dd90b7d7846e418145faa0f2a3

Policy Name Academic Integrity Policy Unit Owner Office of the Vice President for Academic Affairs/ Registrar Version Original Version – 5.0 Reviewed Version – 5.3 Reference Approved by Academic Council Academic Council Effective Date 6 June 2023 2 June 2026

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Starting with honesty and accountability, both call for adherence to the principles of research integrity and transparency. DCU’s mandatory training on research integrity now includes a component on AI use. All first-year candidates must complete this training and it is recommended that supervisory panels incorporate discussion on the topic into their regular meetings with candidates. Doctoral candidates should be encouraged, from the very start of their doctoral journeys, to be open and honest about their use of Gen AI tools so that guidance can be provided by the supervisory panel. From September 2025, DCU’s regulations for academic awards by research degrees will mandate declarations on which (if any) Gen AI tools have been used. The declarations will be required in specific postgraduate forms upon submission of the thesis for examination and in the thesis itself. Candidates should be made aware by the principal supervisor(s) of this requirement from the outset and instructed to keep a detailed record of the tools used, their purpose and the outcomes. The section entitled “Transparent Reporting” below discusses in more detail what is involved in transparent reporting. Reliability hinges on methodological rigour, which would form part of discussions that normally take place between supervisory panels and candidates. If the use of Gen AI tools is proposed, then the impact of their use on methodological rigour requires additional discussion. Ultimately, the candidate needs to deeply understand the methodology, the data collated and perform analysis of that data to derive findings. Respect for participants and other stakeholders demands, among other aspects, ensuring that data privacy is a foremost concern (see below under “Data Security” for further discussion). DCU’s Research Ethics Committee provides guidance on data protection and data privacy in general but also for when Gen AI tools are being used and these ought to be discussed in detail prior to any application to a Research

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SHA-256: 83c626f8ddac4fffe063624136905276694ee26090a16e926cafd8acdba2b97a

An interview will be arranged between the student, member of staff taking the case, and the School Academic Misconduct representative to allow the student concerned to respond to the allegation of academic misconduct. This interview may be conducted electronically or face to face. The interview panel will involve, at a minimum, the School representative and another academic member of staff (for example, the staff member who identified the alleged misconduct). As part of the interview process, the student will be asked if they have ever had any allegation of academic misconduct upheld before, either in DCU or elsewhere. The allegation of academic misconduct will be explained to the student with the evidence explained. The student will then be given the opportunity to refute the allegation or outline any mitigating circumstances involved in their case. The student is entitled to have an observer present during the interview. The interview panel must be notified, at least 48 hours in advance of such planned observer attendance, including the name of the observer and their relationship to the student. This observer may not be a legal representative. The role of the observer is as a support and an observer for the student, not an advocate. As such, they will not speak or otherwise intervene in the proceedings. Communications with the student and interview records must be retained initially by the relevant academic staff member. If the allegation of academic misconduct is not upheld, all records of the alleged instance must be destroyed/deleted. If the allegation is upheld, the records will not be destroyed and will be retained by the relevant Faculty Office according to the University’s data retention policy. An allegation that is upheld should be notified to the Associate Dean for Teaching and Learning for noting at Faculty Teaching and Learning Committee. The student can waive the interview with the School panel and have the matter referred to the University Disciplinary Committee. The matter must be referred to the University Disciplinary Committee if the student retains legal representation, for example a solicitor.

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SHA-256: 72aa5016624ad9fca5e9215a4ae3f1affa40adb969803baa495d62907b60a8c9

Procedure Name Procedure for Academic Misconduct Investigation and Sanction Unit Owner Office of the Vice-President for Academic Affairs / Registrar Version Original Version - Reviewed version – Reference 1.0 2.0 Approved by Academic Council Academic Council Effective Date 6 June 2023 4 June 2024

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SHA-256: 72aa5016624ad9fca5e9215a4ae3f1affa40adb969803baa495d62907b60a8c9

4. Stage 4: Classification and Sanction There are four factors to be considered when classifying the level of severity of academic misconduct: ● Category 1 ‘Types of Violations’: consideration of whether the case is considered as basic violations, limited plagiarism, extensive plagiarism, collusion, falsification / fabrication, fraud / impersonation, or contract cheating. A description of each of these concerns is included in Appendix IV. ● Category 2 ‘Stage’: the student’s current stage on their programme of study i.e. year 1, year 2, year 3 or 4 (not final), final year, Masters year 1, or Masters year 2 or beyond. ● Category 3 ‘Assessment Weighting’: the weighting of the assessment(s) under consideration with respect to the total module mark. ● Category 4 ‘Prior breaches’: takes into consideration if a student has had a previous case of academic misconduct upheld locally or by University Disciplinary Committee. The Academic Misconduct Calculation Sheet (Appendix V) should be used to provide a score based on the considerations listed above. The score will indicate the level of academic misconduct that has occurred. This level is then mapped to sanctions that can be imposed at a local level, as outlined in the Rubric to map classifications of Academic Misconduct to Sanctions (Appendix VI). There are three levels of academic misconduct, these have been adopted from the NAIN Framework and amended to reflect current DCU practices: ● Level 1: Minor Infringement Sanctions applied at the local level include mandatory academic integrity training, a formal reprimand, and may include a lower mark or a mark of zero being applied to the assessment component. ● Level 2: Moderate Infringement Sanctions applied at the local level include mandatory academic integrity training, a formal reprimand, and may include a lower mark or grade of zero being applied to the complete assessment, and a lower mark or a zero mark for the module. ● Level 3: Major Infringement Sanctions include mandatory academic integrity training and a formal reprimand. Major infringements are referred to the University Disciplinary Committee. In any case where a student does not complete the mandatory academic integrity training their mark will be withheld. The full implication of any penalty must be made clear to the student, including potential impact for progression and/or award. The University Disciplinary Committee may impose higher sanctions, including suspension from the University for a year or permanent exclusion from the University.

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SHA-256: 72aa5016624ad9fca5e9215a4ae3f1affa40adb969803baa495d62907b60a8c9

Students should be informed promptly in writing of the outcome of the local process. A student must be informed that if they do not accept the outcome of the local process, they have the right to have the case heard from the beginning by the University Disciplinary Committee. In such cases Registry should be advised so that appropriate revisions to the centrally held flag on the student record can be made. 6. Processes in respect of postgraduate research students Allegations of unacceptable academic or research practices such as falsification of data are dealt with under DCU’s Policy for Responding to Allegations of Research Misconduct. A judgement as to whether or not academic misconduct has occurred is integral to the examination of research reports submitted for formal assessment (such as at confirmation of a research student on the PhD register, and at award stage). The recommendations of the examiners, in line with Academic Regulations for Postgraduate Degrees by Research and Thesis, reflect the outcome of this evaluation. This procedure applies to Research students undertaking taught modules.

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SHA-256: 72aa5016624ad9fca5e9215a4ae3f1affa40adb969803baa495d62907b60a8c9

Be alert to the particular risk with AI of inadvertently sharing data inappropriately or illegally. Your use of AI must be in compliance with existing University policies, which include: Data Privacy Policy (GDPR) Digital Systems and Cloud Services Policy Intellectual Property Policy Ensure that you do not share any content that compromises an individual’s privacy, confidential university processes, DCU’s commercial, or strategic interests. If you do not know if it is safe to share DCU data with a specific AI tool, DO NOT. DCU data includes content from email accounts and drives, recordings or transcripts of DCU meetings, draft papers, books, book chapters, theses, or any document not officially published via our web pages or on DORAS. The use of Personal Data as an input to a GenAI tool is likely to constitute a data breach under GDPR, unless this action received explicit informed consent. Data can be shared in a range of ways including: Information entered via prompts All uploaded material/data Personal identifying information such as names or email address Other background information you provide to improve personalization AI product providers vary in their approaches to data protection, retention, and usage of your data. Entering information into AI tools is similar to posting that information on a public website. You have to assume that any information you input to, or give access to these tools is no longer private and may be used for training the model, sold on to 3rd parties, accessed by governments or made publicly available. Open source European tools are a safer approach than using non-EU owned tools, which are very likely not GDPR compliant. AI tools do not offer the 'right to forget' which means that once data is uploaded / entered - there is no way to delete it which can lead to loss of data ownership. It is intended to build up a whitelist of tools that have been subject to scrutiny in terms of data privacy and licensing agreement conditions, in order to approve them for potential use with specific categories of DCU data. The approach will include a mechanism for colleagues to propose AI tools for review, and regular re-visiting of approvals with software upgrades or licensing changes. As with all IT investment, central licenses and quality controls will be important. Note that whitelisting will not supersede decisions to deploy an AI tool for a particular purpose, given that many applications of AI will involve important additional considerations. Under the University's existing enterprise licences, MS Co-pilot2, Google Gemini AI and Zoom AI Companion suites are available to all staff. Login with your DCU identity. These systems are configured to be GDPR compliant, all data entered remains within the existing DCU data privacy agreements and no data is shared with any of these systems training models. No DCU data should be entered into any other AI tools. 2 This is distinct from Microsoft 365 Co-Pilot for which we have not entered into a licencing agreement.

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SHA-256: c5dfd37d5140f0ed772b030665d293138ec203c65990384d28d61ead35b7ae9b

4) Protect Your Own and Students Work Protect your own intellectual property. Exposing information to an AI tool could result in lost ownership. Do not use AI detection software to review student’s work for plagiarism. Such tools are currently not particularly effective, but more importantly once submitted to an AI tool, the work can be harvested by the AI platform without student’s permission.

Zebrano: 2026-10-08 · 206–208
SHA-256: c5dfd37d5140f0ed772b030665d293138ec203c65990384d28d61ead35b7ae9b