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A University Framework for the Responsible Use of Generative AI in Research

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Title: A University Framework for the Responsible Use of Generative AI in Research
Language: English
Authors: Shannon Michelle Smith (ORCID 0000-0002-5976-3294), Melissa Tate (ORCID 0009-0006-0421-5465), Keri Freeman (ORCID 0000-0003-4334-9416), Anne Walsh (ORCID 0000-0002-5147-8640), Brian Ballsun-Stanton (ORCID 0000-0003-4932-7912), Murray Lane (ORCID 0000-0002-1571-030X)
Source: Journal of Higher Education Policy and Management. 2026 48(1):17-36.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 20
Publication Date: 2026
Document Type: Journal Articles
Reports - Evaluative
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Foreign Countries, Models, Research Universities, Research, Ethics, Integrity
Geographic Terms: Australia
DOI: 10.1080/1360080X.2025.2509187
ISSN: 1360-080X
1469-9508
Abstract: AI poses both opportunities and risks for the integrity of research. Universities must guide researchers in using AI responsibly and in navigating a complex regulatory landscape subject to rapid change. Drawing on the consultative experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of AI. We provide guidance to help distil the diverse regulatory environment into a principles-based university stance through the core values of honesty, transparency and accountability. Further, we explain how a written stance can serve as a foundation for initiatives in training, communications, infrastructure and process change. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework to do so.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1505104
Database: ERIC
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  Value: <anid>AN0191103005;0v101feb.26;2026Jan28.00:56;v2.2.500</anid> <title id="AN0191103005-1">A university framework for the responsible use of generative AI in research </title> <p>AI poses both opportunities and risks for the integrity of research. Universities must guide researchers in using AI responsibly and in navigating a complex regulatory landscape subject to rapid change. Drawing on the consultative experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of AI. We provide guidance to help distil the diverse regulatory environment into a principles-based university stance through the core values of honesty, transparency and accountability. Further, we explain how a written stance can serve as a foundation for initiatives in training, communications, infrastructure and process change. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework to do so.</p> <p>Keywords: AI; ethics framework; research integrity; university policy; research institution</p> <hd id="AN0191103005-2">Introduction</hd> <p>Generative Artificial Intelligence (AI) poses both opportunities and risks for the integrity and quality of research. To support researchers in using AI responsibly, institutions must balance risk mitigation, research governance and management practices, while remaining flexible to allow for innovative use in research. This is a significant challenge due to the uncertainty of how AI will ultimately impact the research sector, which is driven by complex research regulatory environments, rapid innovation, and differing norms between research disciplines, universities and countries.</p> <p>Drawing on experiences from Macquarie University (Macquarie) and Queensland University of Technology (QUT), we provide a strategic framework for responsible AI use in research. These cases demonstrate how to develop a principles-based university stance as part of an overall framework to guide communications, infrastructure, processes and training. Our framework, based on current literature and practical implementation plans, serves as a foundational and adaptable model offering clarity and direction for research institutions.</p> <p>Unlike the abundant research on AI in undergraduate teaching (Crompton & Burke, [<reflink idref="bib13" id="ref1">13</reflink>]; Sullivan et al., [<reflink idref="bib51" id="ref2">51</reflink>]), institutional responses to AI in research remain underdeveloped (Popenici, [<reflink idref="bib41" id="ref3">41</reflink>]). Universities must address serious risks including fake or unreproducible research that threaten credibility (Gibney, [<reflink idref="bib18" id="ref4">18</reflink>]; Liverpool, [<reflink idref="bib25" id="ref5">25</reflink>]) and public trust. Therefore, our paper proposes institutional responses that apply integrity principles to AI challenges, providing common ground for training, communication and infrastructure initiatives.</p> <p>This paper provides a framework to help guide institutional decision-making on AI governance and policy implementation in a rapidly evolving landscape. As a case study, it allows readers to apply our framework and lessons learned as a strategy to identify critical issues and practices, and to develop their own institutional policies, and, core to the framework, a university stance to guide the responsible use of AI in research.</p> <p>Our framework is structured in four layers that represent the order in which the elements should generally be considered and implemented. Figure 1 provides a diagrammatic representation of our framework.</p> <p></p> <ulist> <item> <bold> Context </bold> : Examine the external and internal policy environment that governs research integrity, research conduct and research student supervision and assessment.</item> <p></p> <item> <bold> Development </bold> : Articulate a proposed university position in a written document. This section describes worked examples by our two universities on how we consulted across disciplines and engaged AI researchers.</item> <p></p> <item> <bold> Implementation </bold> : Translate the university's stance into practice with appropriate support, processes and infrastructure.</item> <p></p> <item> <bold> Review </bold> : Iteratively evaluate the university's stance to test its effectiveness and undertake revisions or updates to ensure currency.</item> </ulist> <p>Graph: Figure 1. A framework for institutions to support the responsible use of AI in research (available here under a creative commons CC-BY 4.0 licence: https://osf.io/9b5an/). Image by Mark Hooper (mark@trickygoose.training).</p> <p>It is worth noting that Macquarie called its document a Guidance Note for two reasons. First, pragmatically, a Guidance Note did not require full senate approval for updates, allowing for the note to be a responsive living document. Second, as a document which exists only in an advisory capacity, the format of the Macquarie Guidance Note was designed to be pedagogical, containing many footnotes with links to resources and explanations that would have been inappropriate in a more formal Procedure or Policy. Each university governance structure is different, but having the ability to respond to AI models that are frequently updated is a highly desirable component of this framework. In this paper, the term, <emph>framework</emph>, refers to the overarching structure guiding institutional governance, strategic direction, and adaptation to AI-related developments. <emph>Policy</emph>, on the other hand, refers to specific institutional rules and formal guidelines that establish compliance requirements for researchers around their use of AI.</p> <hd id="AN0191103005-3">Context</hd> <p>Policy development by research institutions is shaped by both external and internal environments. AI introduces new research techniques, methods and datasets while raising questions about authorship, integrity and judgement. These developments challenge established practices, requiring institutions to update policies and infrastructure in areas including research integrity, ethics, degree requirements, data management, sector policies and terms of service.</p> <hd id="AN0191103005-4">Research integrity</hd> <p>The <emph>Australian Code for the Responsible Conduct of Research</emph> establishes principles that also guide responsible AI use in research, notably rigour, transparency and accountability (National Health and Medical Research Council, Australian Research Council, & Universities Australia, [<reflink idref="bib32" id="ref6">32</reflink>]). Researchers must document and transparently disclose AI integration into their research, detailing AI tools, methodologies, and potential biases (National Health and Medical Research Council, Australian Research Council, & Universities Australia, [<reflink idref="bib32" id="ref7">32</reflink>], Principles 2, 3). Comprehensive records, including logs of AI interactions, prompts, and post-processing methods, should be securely maintained and made accessible to promote reproducibility.</p> <p>Disclosure practices for AI use vary widely. Many journals currently require minimal disclosure following COPE guidelines (Committee on Publication Ethics, [<reflink idref="bib12" id="ref8">12</reflink>]). However, some have started mandating detailed disclosures. For instance, the American Psychological Association requires authors to explicitly document AI use in their methods sections and to provide full AI outputs as supplemental materials ([<reflink idref="bib2" id="ref9">2</reflink>]). Practical disciplinary trends include attaching AI-generated logs and prompts as supplemental materials (e.g., Butson & Spronken-Smith, [<reflink idref="bib10" id="ref10">10</reflink>]; Shiferaw, [<reflink idref="bib49" id="ref11">49</reflink>]).</p> <hd id="AN0191103005-5">Acknowledgement, disclosure, and documentation</hd> <p>A dedicated section for AI disclosure, similar to funding statements or conflicts of interest, helps clarify AI contributions. Rigorous disclosure should be proportional to AI involvement, including specific prompts, model versions, and outputs (Ballsun-Stanton, [<reflink idref="bib6" id="ref12">6</reflink>]). Although no consistent standard exists yet across journals or disciplines, Macquarie and QUT have initiated comprehensive requirements for postgraduate students. Macquarie requires documentation of AI use in each student's thesis, including interactions and supervisory approval, while QUT requires detailed written disclosure of AI use within a thesis (QUT Digital Workplace "AI in Research: responsible use", personal communication, [<reflink idref="bib43" id="ref13">43</reflink>]).</p> <p>However, pragmatic concerns about the practicality of these standards highlight implementation challenges. The required 'active supervision and written consent' may be burdensome; thus, institutions must balance rigorous expectations with realistic supervisory practices. Both Macquarie and QUT acknowledge this tension, placing accountability largely on supervisors while also recognising a need for clearer norms and ongoing education.</p> <p>Responsible AI integration requires institutional guardrails and ongoing training. Applying FAIR principles (Findable, Accessible, Interoperable, and Reusable) to AI use enhances research transparency and reproducibility (see Wilkinson et al., [<reflink idref="bib58" id="ref14">58</reflink>]). Rather than prohibitions that might drive shadow IT use, institutions should support researchers to evaluate and disclose AI contributions ethically (Ben Jabeur et al., [<reflink idref="bib9" id="ref15">9</reflink>]; Sadasivan et al., [<reflink idref="bib45" id="ref16">45</reflink>]). Appendix 1 provides an adaptable disclosure checklist for diverse research contexts (see Online Supplementary Materials).</p> <hd id="AN0191103005-6">Human research ethics</hd> <p>Research involving human participants is governed by region-specific laws, regulations and guidelines. Human research ethics is guided by the National Statement on Ethical Conduct in Human Research (the National Statement), which outlines the ethical aspects of human research design, review and conduct (National Health and Medical Research Council, Australian Research Council, & Universities Australia, [<reflink idref="bib34" id="ref17">34</reflink>]). When AI is used in research with human participation or data, ethical implications require close consideration by Human Research Ethics Committees, also known as Institutional Review Boards (Herington et al., [<reflink idref="bib20" id="ref18">20</reflink>]; Zhou & Chen, [<reflink idref="bib62" id="ref19">62</reflink>]). Australian universities must have frameworks and training to help researchers apply the principles of the National Statement to AI use and equip ethics committees with the necessary knowledge for project review.</p> <p>Relevant ethical issues that fall under the scope of the National Statement relate to bias and committee membership. While AI may not be inherently biased, the data upon which they are trained and the interpretation of their results can lead to bias. For example, Obermeyer et al. ([<reflink idref="bib37" id="ref20">37</reflink>]) revealed bias in an AI tool used to identify patients for follow-up care that reduced the number of black patients identified for additional care by more than half. The images generated by AI could also promote bias due to their training data, system prompts, or responses to previous scandals (see Robertson, [<reflink idref="bib44" id="ref21">44</reflink>]; Yue, [<reflink idref="bib61" id="ref22">61</reflink>]). On the other hand, Salah et al. ([<reflink idref="bib46" id="ref23">46</reflink>]) suggest that AI could potentially help avoid human bias such as in coding and categorisation of qualitative studies. Another issue in accord with the National Statement involves technical expertise on research ethics committees. Current committee membership is diverse, but may not necessarily include the expertise to ethically evaluate research with AI. Research institutions may therefore need to reconsider the core membership of their research ethics committees and animal ethics committees to ensure sufficient knowledge and experience is available. Moreover, AI technologies are not like traditional IT services. They require specialised research support around prompting, ethical issues, methodological implications and quality assurance.</p> <hd id="AN0191103005-7">Research degree requirements</hd> <p>Standards for postgraduate degrees are set by regulatory bodies external to the university. In Australia, the standard for doctoral degrees requires researchers' full responsibility and accountability for personal outputs, regardless of source, and sufficient cognitive skills for intellectual independence and critical thinking (Australian Qualifications Framework Council, [<reflink idref="bib3" id="ref24">3</reflink>]). Given that some scholars question the use of AI as infringing independent thought, there are additional questions needed to ensure the reputation of postgraduate work in this instance, as norms shift (Butson & Spronken-Smith, [<reflink idref="bib10" id="ref25">10</reflink>]). Therefore, implementation of postgraduate degree policy at an institutional level requires more detailed guidelines than those offered by external regulatory bodies, and university protocols on the ethical use of AI may be one component of these guidelines. This can be challenging, since the novel position of postgraduate students, acting as researchers in training, can sometimes be unclear. On the one hand, their work must be subject to assessment and degree standards, but as researchers, they can be treated as academics and even equals (Tanggaard & Wegener, [<reflink idref="bib52" id="ref26">52</reflink>]). If research staff are taking advantage of cutting-edge opportunities using AI, then it is natural for an institution to encourage postgraduate students to do the same. This duality of identity presents a unique challenge for internal institutional policies and requires universities to take action to adequately support research students' understanding of their ethical responsibilities when using AI.</p> <p>Moreover, postgraduate student use represents two novel failure modes which should be accounted for. The first is over-reliance on AI without the student engaging in <emph>any</emph> critical thinking, where 'the AI did my thesis for me' is the worst outcome. The second is the opposite problem: extremely problematic first drafts and under-reliance on AI for editing and polishing. In recognition of these challenges, both universities placed the responsibility for monitoring appropriate AI use onto supervisors, as this aligns with existing research training responsibilities. The Macquarie Guidance note (Section 2.3) splits this Gordian knot by requiring the 'active supervision and written consent' of the supervisor, allowing the supervisor to actively and narrowly govern the AI use of their student. QUT requires that postgraduate students 'discuss the use of any AI in writing your thesis with your principal supervisor prior to use' (QUT Digital Workplace "AI in Research: responsible use", personal communication, [<reflink idref="bib43" id="ref27">43</reflink>]).</p> <p>Changes to postgraduate student research processes and assessment are a likely consequence of increasing use of AI across universities. Students may use AI for planning research design, conducting research and assisting in the preparation of research outputs and presentations, all of which could require revisions to existing training and a clearer understanding of the responsibilities. Likewise, university processes around thesis assessment must also undergo review. Any effective policy for assessing postgraduate students' work must be enforceable, and therefore universities must address the feasibility of detection (Lodge et al., [<reflink idref="bib26" id="ref28">26</reflink>]). However, quantitative measures of AI use seem currently unreliable (see Liang et al., [<reflink idref="bib24" id="ref29">24</reflink>]; OpenAI, [<reflink idref="bib39" id="ref30">39</reflink>]; Sadasivan et al., [<reflink idref="bib45" id="ref31">45</reflink>]). One possible response to this challenge should be for a greater reliance on in-person, verbal defences of student and research work, which are currently uncommon in Australia (Bending et al., [<reflink idref="bib8" id="ref32">8</reflink>]; Pearce & Chiavaroli, [<reflink idref="bib40" id="ref33">40</reflink>]). As Vinge ([<reflink idref="bib56" id="ref34">56</reflink>]) predicted, '[T]he beginning of trust has to be an in-person contact'. (p. 129) For universities to acknowledge student capabilities, in-person milestones are needed to help validate a student's intrinsic knowledge and subject mastery outside the context of any AI tool-assisted capabilities.</p> <hd id="AN0191103005-8">Research data management</hd> <p>AI platforms process user data offshore, including potentially sensitive research data, which serves as another risk in the use of AI. Depending on the security supporting the AI platform, data may be at risk of privacy or copyright breaches due to user error or cyber threats. Furthermore, a user's prompts may compromise ownership of subsequent innovations, as the legal and moral aspects of copyright related to AI are presently unclear (WIPO IP and Frontier Technologies Division, [<reflink idref="bib59" id="ref35">59</reflink>]). In Australia, institutions must provide supporting infrastructure for research data, including guidance and facilities, to protect data ownership, security and confidentiality (National Health and Medical Research Council, Australian Research Council, & Universities Australia, [<reflink idref="bib33" id="ref36">33</reflink>], Section 2). Institutions must therefore exercise prudence when employing AI to support research data, particularly where the AI platform may not apply adequate data privacy, security and Intellectual Property (IP) standards. Universities choosing what AI to use must trade off between model capability, speed of response, building versus buying and internal capabilities when trying to thread the IP needle.</p> <p>Copyright is another area of concern in the context of research data management. The legal aspects of copyright related to the outputs of AI in Australia have yet to be updated to address the use of AI and are inadequately regulated (see Gaffar & Albarashdi, [<reflink idref="bib17" id="ref37">17</reflink>]; Samuelson, [<reflink idref="bib47" id="ref38">47</reflink>]). It is not currently known whether works created by AI platforms will be protected given that the nature of Australian copyright relies on human authorship. Changing copyright standards for AI involvement may cause a published work to not meet the human author threshold, and thus may not be eligible for protection under the current law. Research data management is another example of the need for researchers to be supported while specific regulations addressing the use of AI are resolved.</p> <hd id="AN0191103005-9">Sector policies</hd> <p>Ensuring accountability in research practices necessitates robust sector policies for academic publishers, research funding and institutional strategies to manage the impact of AI. Academic publishers, particularly those adhering to the Committee on Publication Ethics (COPE) guidelines, emphasise the importance of ethical standards in publication 2024. They generally agree that while AI cannot be held responsible as authors, it can be used for subsidiary research publishing tasks (Nature Portfolio, [<reflink idref="bib36" id="ref39">36</reflink>]), provided acknowledgement is included. Given the rapidly evolving landscape of AI, publishers and institutions must be careful not to harm authors with false claims of inappropriate use of AI (see Wolkovich, [<reflink idref="bib60" id="ref40">60</reflink>]). This acknowledgement-permissive mode ensures that content responsibility always remains with human authors.</p> <p>Large research funding bodies external to universities may also influence institutional research policies and practices, and may override university policies for responsible use of research funds. Mitigating risks is a primary priority, exemplified by the European Union's strict legislation on transparency, bias, privacy and copyright principles (Gibney, [<reflink idref="bib19" id="ref41">19</reflink>]). In Australia, several funding bodies have implemented policies on the use of AI in the peer review of grant applications (Australian Research Council Research Policy Branch, [<reflink idref="bib4" id="ref42">4</reflink>], National Institute for Health, [<reflink idref="bib35" id="ref43">35</reflink>]). The Australian Research Council acknowledges that while AI presents opportunities for research and grant proposal writing, the responsibility for errors falls on researchers and their institutions. This stance, although not new, emphasises the need for thorough due diligence, and appropriate training and guidance, due to the complexity and uncertainty introduced by AI.</p> <p>Grant-makers' concerns include the ability of AI to make effective judgements, the potential sharing of confidential information and protected intellectual property if documents are uploaded to publicly available AI services. Although these issues might be resolved, human oversight remains crucial to maintaining confidence in the peer review process and the responsible allocation of research funds. Thereby underscoring the principle of accountability in institutional AI stances.</p> <p>Another aspect of sector policy impacting AI research frameworks involves institutional strategies and operational plans. A 2024 Australian government review of higher education quality, accessibility and sustainability highlighted the potential of AI to boost research productivity, urging universities to lead in building new research infrastructure (Department of Education, [<reflink idref="bib14" id="ref44">14</reflink>]). AI may contribute to digital transformation efforts, similar to the push for paperless offices in the 1990s (Vrana & Singh, [<reflink idref="bib57" id="ref45">57</reflink>]), and universities might deploy AI to reduce staff loads, an efficiency expected to extend to research integrity and ethics committees. However, institutions must carefully assess the importance of human judgement in all tasks, especially in research support and governance roles. The rapid evolution of AI also complicates the enterprise software purchasing process, as products may change significantly before they are fully implemented. Therefore, proactive strategies can ensure that institutional frameworks incorporate responsible research conduct principles and provide adequate support for researchers amidst the evolving AI landscape.</p> <hd id="AN0191103005-10">Terms of service</hd> <p>Institutions should regularly monitor the terms of service of AI platforms to maintain control over research data and comply with applicable privacy legislation. Typically, an institution can negotiate specific terms of service when purchasing enterprise research software and can reasonably expect that it will remain the same throughout the course of the agreement. However, the current rate of change means that AI platforms release frequent terms of service updates, which presents a significant legal risk if subsequent updates change intellectual property or privacy provisions that compromise security and ownership.</p> <p>AI platforms also mandate service use disclosure that puts the onus of responsibility for any AI generated text onto the human author (OpenAI, [<reflink idref="bib38" id="ref46">38</reflink>]) and specific agreements must inform university policies. Therefore, a university's stance should clearly allocate the responsibilities of monitoring AI company terms and to maintain a list of services, models and modes of interaction that are risk-acceptable and privacy-acceptable to the university. While an institution cannot forbid an entire technology category, it can point to specific ways of using services and guide researchers to more effective and less risky options. The continually changing technology makes maintaining a consistent and aligned university stance an ongoing task.</p> <hd id="AN0191103005-11">Developing a university position</hd> <p>Considering the diverse contextual factors relevant to the use of AI in research, an institutional stance can serve the important function of distilling and applying that context in one place. In general, an articulated university stance provides a foundation for the development of institutional policies, guidelines and training in a whole of enterprise strategy. Applied to AI, this promulgation enables institutions to take a proactive stance to promote the responsible use of technology to foster research quality and innovation, while prohibiting practices inconsistent with research integrity.</p> <p>Developing a written university stance (position statement as preparatory document, or guidance note as advisory document) – as institutional governance and knowledge dictate – informs research managers and leaders, empowering them to contribute to institutional initiatives. By providing a common ground within the institution, a clear university stance can reduce confusion caused by the saturation of literature and opinions, the proliferation of new tools and the rapid pace of disruption. Promulgating a stance can then lead to informed and tested feedback leading to a more specific and useful policy.</p> <p>The format of a position statement can be simple, with clear declarative statements about responsible use of AI, forming the basis for more complex considerations and scholarly debates on nuanced issues. The format of a guidance note requires more explanation and justification of decisions, in an attempt to persuade and inform, with enforcement power coming from extant policy. A guidance note can serve as a first introduction, allowing conversations to occur in the absence of formal and enforced policy.</p> <p>This section of the paper describes the processes involved in the development of a university stance on AI. Each university followed its own internal protocols when naming and framing their document. QUT developed a Position Statement, while Macquarie produced a Guidance Note. Both articulate a university-level stance on the responsible use of generative AI in research.</p> <hd id="AN0191103005-12">Consult across disciplines and research services</hd> <p>Developing a university stance requires broad consultation across research disciplines and research support services to capture the diverse ways of using AI. Development requires targeted consultation with academics who have expertise in research ethics and responsible research practices. This consultation could take multiple forms. We describe the process here for two universities: QUT and Macquarie.</p> <p> <bold>QUT</bold> began consultation in January 2023 by drafting a discussion paper: <emph>Emerging AI tools and challenges for research integrity</emph>, which was then circulated broadly to provoke discussion and feedback. This discussion paper recognised the potential opportunities of emerging AI in research. It outlined the inherent risks AI could pose for responsible research practices, such as authorship, originality of publications and research outputs. It provided a balanced overview of the use of AI in research to help focus the consultation on the key issue of how AI intersects with research integrity. We solicited feedback from across the university, including from Research Integrity and Ethics Advisers.</p> <p> <bold>Macquarie</bold> established a committee of stakeholders and consulted experts from each faculty and professional services. They considered the risks and opportunities posed by AI to research integrity, processes and operations. The committee was tasked with considering whether a new policy or targeted policy updates were required or whether changes to processes were deemed appropriate. Anticipating the fast pace of change and the forecast need for regular updates, a Guidance Note outlining the University's position and recommendations regarding the use of AI in research was drafted. To create the Guidance Note, the committee evaluated the current literature and engaged in cross-faculty discussions. Key aspects of the Guidance Note included identification of key resources to provide an initial foundation for researchers, postgraduates and support staff and a summary of potential risks and justifications for the intended approach. The Guidance Note was submitted to the relevant committees for comment or endorsement. This step was considered critical to allow diverse areas of the University an opportunity to build understanding and consider implications for their areas or alignment with other projects or initiatives.</p> <p>For both cases, consultation was coordinated by each university's Office of Research Ethics and Integrity (OREI). The consultation process provided the opportunity for OREI at both universities to engage with their research communities to understand the varied uses of AI in research and the potential impacts of AI on responsible research practices. Our examples illustrate that while the approach to developing a university position can vary according to each institution's needs and internal structure, coordinated consultation, discussion and feedback across university research communities is crucial to ensure all stakeholders are provided the opportunity to participate in the process.</p> <p>In 2023, when both universities were first writing their documents, no informed student voice was available for formal consultation. During development at Macquarie, in the three months of development of our Guidance Note, the research working group was instructed to consult with their Higher Degree by Research (HDR) students and appropriate staff in their respective faculties. QUT also asked staff to reach out to well-informed students.</p> <p>One crucial aspect of the development of our frameworks was involving researchers and practitioners with actual, thorough, use of the tools when developing our advice. In 2023, this pool was quite narrow, leading to most consultation through faculty experts and those people tasked with investigating these services. In 2025, a broader and more consultative approach is warranted.</p> <hd id="AN0191103005-13">Engage AI researchers</hd> <p>Apart from the formal consultation process across research disciplines and services, it is vital to recognise that some researchers have long been experts in this space and already engage both internally and publicly with emerging opportunities and challenges. Where possible, these researchers should be invited to participate in he consultation process to incorporate their input and gain their endorsement. According to the Technology Acceptance Model (Shahzad et al., [<reflink idref="bib48" id="ref47">48</reflink>]), the advocacy for institutional policy by trusted academics has generally been shown to be a successful strategy for the adoption of technology by staff and students (Faden et al., [<reflink idref="bib16" id="ref48">16</reflink>]). Engaging AI researchers within the university forms a vital part of an institution's strategic framework in shaping policies related to research integrity and providing essential AI literacy training. As Butson and Spronken-Smith ([<reflink idref="bib10" id="ref49">10</reflink>]) have highlighted, it is also essential that institutions consider contrasting and diverse perspectives related to the integration of AI in research tasks, because 'this is not merely a technological transition but also a significant ethical and methodological turning point' (p. 567). Combined broad and specific consultation ensures that institutions not only address the challenges but also explore the opportunities AI provides. Consequently, we consulted innovators and early adopters in our universities who had already published their research (e.Doherty et al., [<reflink idref="bib15" id="ref50">15</reflink>]; Bell et al., [<reflink idref="bib7" id="ref51">7</reflink>]; Kowalkiewicz, [<reflink idref="bib23" id="ref52">23</reflink>]; McDonald et al., [<reflink idref="bib29" id="ref53">29</reflink>]).</p> <hd id="AN0191103005-14">University stance</hd> <p>A strong written statement by the university should address key research integrity principles, especially honesty, transparency and accountability, and reinforce the importance of these principles in the responsible use of AI in research practices. Our university stances have been developed based on broad stakeholder feedback. They provide a consolidated institutional position and clear guidance for diverse faculties and research groups. We provide copies of our position statements as Attachments 1 and 2, and make them available to adapt under a Creative Commons 4.0 CC-BY Licence (see Online Supplementary Material).</p> <p>Broadly, our two university stances have much in common:</p> <p></p> <ulist> <item> They <emph>support</emph> other existing responsible conduct of research policies.</item> <p></p> <item> They <emph>encourage</emph> the use of AI where it promotes responsible research quality and impact.</item> <p></p> <item> They are primarily <emph>principles-based</emph> so that the readers can interpret their implementation within their own specific context.</item> <p></p> <item> They have minimum prescribed rules, such as:</item> <p></p> <item> An AI tool must not be listed as an author, as this violates the principle of accountability for research outputs.</item> <p></p> <item> Explanations on how AI was used should be included in the Methods and/or Acknowledgements sections of a research output to be consistent with the policies of publishers and the principle of transparency.</item> </ulist> <p>There are some notable differences between the two statements in terms of style and delivery. While QUT named its document a 'position statement' to emphasise that it provides definitive university-level advice, Macquarie named its statement a 'Guidance Note', emphasising its role in highlighting extant policies and indicating how they apply to the responsible conduct of research. Both the guidance note and the position statement proved strategically useful for the subsequent development of AI policy in 2025 and to align university thinking around consistent norms for the responsible use of AI in research.</p> <hd id="AN0191103005-15">Implementation</hd> <p>A written stance serves as a foundational guide, articulating the university's</p> <p>principles and position on the responsible use of AI in research. However, it is not a policy itself, but rather informs the development of specific institutional policies and procedures that establish enforceable guidelines. To ensure effective implementation, these policies must be embedded within a broader strategic framework. This strategic framework then supports the development of a governance structure that guides</p> <p>institutional responses and adaptations over time. The framework should encompass a communication and engagement strategy, training and education initiatives, and infrastructure and procedural considerations.</p> <p>This section of the paper outlines each of these key areas and reflects the third layer of our framework after Context and Development.</p> <hd id="AN0191103005-16">Communication and engagement strategy</hd> <p>Rapid AI developments and new AI services can overwhelm researchers tasked with staying informed. To support informed decision-making, any university stance must include a well-designed communication and engagement strategy.</p> <p>Introducing a new policy naturally requires clear communication. However, policy alone rarely changes culture; thus, it must be accompanied by active <emph>engagement</emph> initiatives. QUT's position statement 'recognises that the use of, and implications of using, AI tools may differ widely by discipline', encouraging ongoing scholarly debate and research. The unique risks and benefits of AI particularly necessitate proactive change management (Kotter, [<reflink idref="bib22" id="ref54">22</reflink>]; Singh & Johnston, [<reflink idref="bib50" id="ref55">50</reflink>]).</p> <p>As AI capabilities, services and models continually emerge, institutions must not only communicate broader policy implications to staff and students, but also provide safe institutional access to mitigate privacy risks and promote equity of use. This communication should be a coordinated change management strategy that allows researchers to understand both the benefits and risks of AI, especially when available models change. For example, policy authors might visit department meetings to</p> <p>explain critical clauses and their technical or practical rationale, ensuring that most researchers are exposed to the new guidelines.</p> <p>Beyond announcements and informational sessions, a detailed communication plan can support further university-wide discussions on the nuances and implementation of AI. Encouraging and acting on feedback allows faculty and researchers to voice concerns, particularly those critical of emerging technologies. It is imperative to foster a collaborative culture that focuses on policies related to responsible AI use and also capitalises on researchers' experiences of the benefits that using AI tools can provide (Balalle & Pannilage, [<reflink idref="bib5" id="ref56">5</reflink>]).Through such collaboration and conversations, universities can refine norms accepted by various faculties and reach consensus on ethical AI use. Often, such norms can help mitigate institutional risk by clarifying which training resources and AI literacy initiatives are needed to support ongoing engagement.</p> <hd id="AN0191103005-17">Training and education strategy</hd> <p>Training and education are fundamental and ongoing elements of any institution's response to ensuring its researchers fully understand their ethical responsibilities around the use of AI in their research practices. In Australia, institutions have a responsibility to '<emph>Provide ongoing training and education that promotes and supports responsible research conduct for all researchers</emph>' (National Health and Medical Research Council, Australian Research Council, & Universities Australia, [<reflink idref="bib32" id="ref57">32</reflink>]). Moreover, AI literacy is fast becoming an essential part of research processes and a valuable industry skill (see Hutson, [<reflink idref="bib21" id="ref58">21</reflink>]; Prillaman, [<reflink idref="bib42" id="ref59">42</reflink>]; Van Noorden & Perkel, [<reflink idref="bib55" id="ref60">55</reflink>]). As reported by Thompson et al. ([<reflink idref="bib53" id="ref61">53</reflink>]), p. 3), 'there is still sensemaking work to be done, as misconceptions about what AI is and how it works remain common'. Therefore, there is an urgency for institutions to provide education that equips research graduates for their careers and empowers academic staff to pursue industry research partnerships.</p> <p>The diversity of opportunities provided by AI heightens the importance of AI literacy training and initiatives which emphasise and integrate research integrity. The framework recognises both opportunities and risks. Although the university's stance should focus on risk mitigation, training strategies should also support researchers in identifying and achieving the appropriate benefits of AI. Some issues to consider, therefore, include data management policies, authorship guidelines, copyright law and research reproducibility. AI literacy training must also focus on the development of skills to understand, identify and address the trustworthiness and reliability of AI. Researchers should be able to assess whether using AI is appropriate for a specific challenge and therefore be able to defend their choice of AI selection or not. Necessary researcher skills include the ability to tackle challenges such as privacy and the risk of disclosing IP, the potential for bias and prejudice in the data that AI uses to formulate responses and the ability to critically evaluate outputs. Training should focus on explicit scaffolding and self-regulated learning that encourages critical engagement and agency in ethical decision-making when using AI (Markauskaite et al., [<reflink idref="bib28" id="ref62">28</reflink>]; McKnight, [<reflink idref="bib30" id="ref63">30</reflink>]; UNESCO, [<reflink idref="bib54" id="ref64">54</reflink>]).</p> <p>At our two universities, initial researcher training on the responsible use of AI has included workshops, online modules and panel discussions allowing postgraduate students and supervisors to ask questions of experts with diverse specialities in AI research, research ethics and student policies. Training has also emphasised the importance of effective and thoughtful prompting. Likewise, online modules challenge participants to make decisions for themselves on the appropriate use of AI, using the key principles of honesty, transparency, accountability and fairness, through scenarios and case studies. In each of these examples, rather than providing purely didactic instruction, participants are prompted to consider their own unique circumstance and make decisions accordingly. As such, training strategies on AI usage must be regularly updated and adaptable to keep pace with the frequent updates and advancements in the field.</p> <hd id="AN0191103005-18">Acknowledgement and documentation of use by postgraduate students</hd> <p>As an example of training and education strategy, we may examine how both universities have set high expectations of their postgraduate students. These requirements have been written into guidance for postgraduate students and communicated by presentation and webpage to supervisors and students.</p> <p>Macquarie has chosen to require documentation of use:</p> <p>Generative AI, LLMs, and tools integrating these models will likely become part of the writing, research, and composition process for many researchers and students. However, Higher Degree Research (HDR) and Honours students must be responsible, from start to finish, for their research. ... At Macquarie University, it is recommended that <emph>all</emph> stages of a students research journey be well documented both for data integrity purposes and to serve as evidence of originality and intellectual input if challenged. Supervisors must closely monitor the use of Generative AI by their research students. Generative AI should not be used without supervisory approval, and where these tools are used in any capacity by research students or supervisors, they must be acknowledged'. (Macquarie University Guidance Note, Section 2.3)</p> <p>At QUT, the position statement focuses on the university's commitment to research integrity. Additionally, research students are instructed to seek approval from their Principal Supervisor prior to AI use. Any use of AI, including text editing for readability and grammar, must be disclosed in the thesis acknowledgement and a detailed written description documenting the extent of AI use and how it has been used is also required (QUT Digital Workplace "AI in Research: responsible use", personal communication, [<reflink idref="bib43" id="ref65">43</reflink>]). We acknowledge that both universities could do more in this regard – as there is a tension between the high standards of our guidance and the lived realities of supervisors. The pragmatic reality of both of these institution's requirements is that of offloading the onus of responsibility of the student's use of AI onto the supervisor. The Macquarie Guidance Note chose to explicitly empower supervisors to make the guidelines specific <emph>per student</emph> as students and supervisors have different needs and expectations when it comes to writing and AI use. This phrasing is an acknowledgement that as researchers in training, the consequences of AI use will fall on the supervisor. The choice of terms of 'active supervision' is to emphasise the supervisor's responsibility of this component of research training. The level of active attention given by the supervisor is up to the supervisor's discretion. At Macquarie, Ballsun-Stanton has mediated supervisor and postgraduate student meetings setting out the allowable scope of AI use – while these meetings are the exception to the norm of benign neglect, some students and supervisors have been open to these discussions when educated about the guidance note. Nevertheless, more work is necessary around norm setting, and the provision of sufficient education of the appropriate uses of these technologies.</p> <hd id="AN0191103005-19">Infrastructure and procedural considerations</hd> <p>Universities face several common barriers when implementing AI policies: equity of access, budget constraints, maintaining integrity, and promoting appropriate use amid uneven distribution of expertise. For institutions with limited resources, the rapid evolution of AI creates additional challenges.</p> <p>To address these barriers effectively while maximising limited resources:</p> <p></p> <ulist> <item> Make transparent disclosure mandatory through existing processes. Data management plans and ethics applications should require researchers to declare AI use, creating accountability without new administrative structures.</item> <p></p> <item> Prioritise cost-effective solutions. The price of AI inference is dropping rapidly",about 10× every 12 months" (Altman, [<reflink idref="bib1" id="ref66">1</reflink>]), making thoughtful commercial AI adoption increasingly affordable compared to custom solutions that promise to reduce expertise requirements. Paying per token with a low daily budget allocated per student and researcher is <emph>significantly</emph> cheaper than paying a fixed fee per month per user, regardless of their use. By targeting a cost-efficient frontier model with a pay-per-token approach, universities pay only for what they use, and cap expenses of power-users.</item> <p></p> <item> Leverage established research norms. Existing integrity frameworks provide a foundation for <emph>post hoc</emph> judgement as technology norms evolve, requiring only targeted adaptations rather than complete policy overhauls.</item> <p></p> <item> Ensure auditability through service selection. Choose AI platforms that maintain researcher-accessible logs and comply with institutional research policies, avoiding costly compliance issues later.</item> </ulist> <p>Most universities already have secure research data infrastructure that can be leveraged for AI implementation. By communicating clear trade-offs between hosted solutions and institutional infrastructure, universities can help researchers make appropriate choices for their specific needs while controlling costs and maintaining security standards.</p> <hd id="AN0191103005-20">Review</hd> <p>The evaluation process is an important component in reviewing the design, implementation and refinement of university policy and is therefore integrated into the framework at all levels. At each stage of development, implementation and communication, feedback from stakeholders is needed to highlight mistakes, inform future policy development and respond to the changing AI landscape. The evaluation process, therefore, provides a review mechanism to inform stakeholders of policy effectiveness, ensures the currency of the university's stance and informs new directions and updates to the framework. Essentially, the evaluation phase of a strategic framework involves asking stakeholders for feedback on progress and adjusting the framework and/or implementation strategies accordingly. This is a cyclical process where implementation can have impacts on the institutional context, and therefore, feedback is essential to inform the next stage of policy development.</p> <p>Strategic planning, governance and policy development for the responsible use of AI will continue to evolve. Therefore, continuous adaptation is essential to 'harness its full potential while safeguarding academic integrity and promoting equitable access' (McDonald et al., [<reflink idref="bib29" id="ref67">29</reflink>], p. 79). We suggest reviews should consistently examine gaps in research governance that will inevitably be created by ongoing research innovation and digital disruption. Given the fast pace of AI change, it is inevitable that future changes will necessitate equivalent policy changes to maintain relevance. For example, as AI developers and providers continue to address limitations and challenges related to model capabilities, inquiry and rigour to understand the challenges and benefits of AI updates to models and updates will remain fundamental and ongoing issues that are 'central to academic endeavour and require rigorous scrutiny' by institutions and researchers (Butson & Spronken-Smith, [<reflink idref="bib10" id="ref68">10</reflink>], p. 566). We also propose a more direct form of review: to regularly survey and engage with stakeholders at all levels: understand how new model releases impact student use, academic use, academic concerns, and university successes.</p> <p>To evaluate effectiveness amid rapid AI evolution, institutions should track these trends: awareness and engagement, whether researchers understand policies; compliance trends, whether AI misuse is decreasing; and policy relevance, whether guidelines remain current. As Chen et al. ([<reflink idref="bib11" id="ref69">11</reflink>]) note, effective mechanisms are required to 'detect and prevent unethical behaviours in research, such as data fabrication, plagiarism, and improper use of AI technology. Regular reviews should prioritise maintaining currency rather than reactive overhauls, tracking new model capabilities that impact different university stakeholders in uneven ways.</p> <p>For example, after Mollick ([<reflink idref="bib31" id="ref70">31</reflink>]) described using AI to detect statistical errors in journal articles, Macquarie updated its Peer Review policy to permit reviewers to use AI tools that support their judgement, provided this does not violate confidentiality or commissioning guidelines. Similarly, QUT is implementing additional training based on stakeholder feedback. These cases demonstrate how institutions can adapt policies to reflect emerging scholarly practices through regular review.</p> <p>The Macquarie Guidance note levies an additional onus on supervisors: 'Supervisors should have iterative exposure to their students work across their entire research degree and ensure their research progression (including critical judgement or originality) is observed and well documented. Supervisors must closely monitor the use of Generative AI by their research students' (Macquarie Guidance Note, section 2.3). Macquarie is tracking these conversations through checking compliance at Data Management Plan submission stage, along with <emph>ad hoc</emph> conversations with supervisors during graduate training. QUT is tracking the effectiveness of its training through training evaluation surveys.</p> <hd id="AN0191103005-21">Conclusion</hd> <p>As universities grapple with the challenges and opportunities presented by AI, our paper introduces a strategic framework which identifies context to investigate, stakeholders to consult in development, key implementation steps and review mechanisms to allow for iterated development of stances and policies. The developed texts can then be used to promote responsible and ethical practices in academic research. The evolving AI landscape necessitates comprehensive, flexible and university-wide policies and strategies to ensure research integrity and support researchers in responsibly using AI.</p> <p>The framework we present provides a structured approach with insights into the elements institutions should consider for practical implementation. Central to this process is the development of an articulated university stance on AI, framed as a document appropriate to that university (position statement, guidance note, etc.), reinforcing key principles of research integrity. This stance guides the operationalisation of training programs, communication strategies and infrastructure initiatives. Our framework outlines an iterative process starting with examining relevant contextual factors, developing a consensus around a stance, implementing it, and conducting ongoing reviews and improvements to the formal written document.</p> <p>While other practitioners have produced frameworks for universities dealing with AI broadly (for undergraduate policy, see Luo, [<reflink idref="bib27" id="ref71">27</reflink>]), this paper specifically addresses the research policy context. Our framework serves as a road map for research</p> <p>institutions to adapt, considering their unique contexts to develop appropriate strategies and plans. Examples from QUT and Macquarie illustrate potential</p> <p>outcomes of a strategic framework process, without presenting them as definitive end-points.</p> <p>Testing various strategies within this framework could inform future research related to identifying the most effective strategies to promote responsible practices and research integrity in AI use across institutions. Each organisation must drive its own AI agenda, but our framework can initiate and guide that process.</p> <hd id="AN0191103005-22">Acknowledgements</hd> <p>The authors would like to acknowledge the significant contributions of Mark Hooper (mark@trickygoose.training) in developing the graphical representation of the framework and in assisting with earlier revisions of this manuscript. At his request, in this version of the manuscript, we have moved him from an author to acknowledged contributor. We warmly thank him for his time and support on this paper.</p> <p>Special thanks to the following early readers for their feedback and encouragement: Jane Thogersen, Stephanie Bradbury, Matt Bower, Ross McLennan, Sylvie Saab and Jacqueline Phillips.</p> <hd id="AN0191103005-23">Disclosure statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <hd id="AN0191103005-24">Authors' contributions</hd> <p>All authors contributed conceptualisation, writing, reviewing and editing.</p> <hd id="AN0191103005-25">AI use disclosure</hd> <p>All prompts and full chat-logs of sessions with large language models used as part of editing this work are available at https://osf.io/8dxj6/ Models used have included Anthropic's Claude 3.5 Sonnet, 3.7 Sonnet, and 3.7 Sonnet Thinking; And OpenAI's models including o1.</p> <hd id="AN0191103005-26">Supplemental material</hd> <p>Supplemental data for this article can be accessed online at https://doi.org/10.1080/1360080X.2025.2509187</p> <ref id="AN0191103005-27"> <title> References </title> <blist> <bibl id="bib1" idref="ref66" type="bt">1</bibl> <bibtext> Altman, S. 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  Data: A University Framework for the Responsible Use of Generative AI in Research
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  Data: English
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Shannon+Michelle+Smith%22">Shannon Michelle Smith</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5976-3294">0000-0002-5976-3294</externalLink>)<br /><searchLink fieldCode="AR" term="%22Melissa+Tate%22">Melissa Tate</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-0421-5465">0009-0006-0421-5465</externalLink>)<br /><searchLink fieldCode="AR" term="%22Keri+Freeman%22">Keri Freeman</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4334-9416">0000-0003-4334-9416</externalLink>)<br /><searchLink fieldCode="AR" term="%22Anne+Walsh%22">Anne Walsh</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5147-8640">0000-0002-5147-8640</externalLink>)<br /><searchLink fieldCode="AR" term="%22Brian+Ballsun-Stanton%22">Brian Ballsun-Stanton</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4932-7912">0000-0003-4932-7912</externalLink>)<br /><searchLink fieldCode="AR" term="%22Murray+Lane%22">Murray Lane</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1571-030X">0000-0002-1571-030X</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Higher+Education+Policy+and+Management%22"><i>Journal of Higher Education Policy and Management</i></searchLink>. 2026 48(1):17-36.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: Y
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  Group: Src
  Data: 20
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  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Evaluative
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Universities%22">Research Universities</searchLink><br /><searchLink fieldCode="DE" term="%22Research%22">Research</searchLink><br /><searchLink fieldCode="DE" term="%22Ethics%22">Ethics</searchLink><br /><searchLink fieldCode="DE" term="%22Integrity%22">Integrity</searchLink>
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  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Australia%22">Australia</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/1360080X.2025.2509187
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  Label: ISSN
  Group: ISSN
  Data: 1360-080X<br />1469-9508
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: AI poses both opportunities and risks for the integrity of research. Universities must guide researchers in using AI responsibly and in navigating a complex regulatory landscape subject to rapid change. Drawing on the consultative experiences of two Australian universities, we propose a framework to help institutions promote and facilitate the responsible use of AI. We provide guidance to help distil the diverse regulatory environment into a principles-based university stance through the core values of honesty, transparency and accountability. Further, we explain how a written stance can serve as a foundation for initiatives in training, communications, infrastructure and process change. This paper underscores the urgency for research institutions to take action in this area and suggests a practical and adaptable framework to do so.
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  Data: 2026
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  Data: EJ1505104
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        Value: 10.1080/1360080X.2025.2509187
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      – Text: English
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        PageCount: 20
        StartPage: 17
    Subjects:
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Models
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      – SubjectFull: Australia
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      – TitleFull: A University Framework for the Responsible Use of Generative AI in Research
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