AI technology is evolving rapidly and will pose challenges that are difficult to predict. There are, however, principles that should underpin any use or development of AI, particularly generative AI. The University has adopted the following principles, building on existing frameworks for academic integrity and emerging guidance on AI.

All staff and students using or developing AI are personally responsible for adhering to these. 

Transparency

Always make clear when and how you have used AI in a process or in producing an output, citing relevant details. Specifically:

• describe clearly how you have used AI tools and resources, explaining your original contribution;
• cite the AI tools or resources you used and provide links to their documentation;
• keep detailed records to provide an audit trail for your use of AI tools and resources.

Accountability

Take responsibility for any outputs or outcomes resulting from your use of AI. Specifically:

• investigate the reliability of sources from which information has been drawn;
• apply critical thinking to verify the accuracy and reliability of outputs or outcomes;
• acknowledge primary sources appropriately.

Competence

If you use or develop AI, update your knowledge and skills regularly to ensure you are aware of its capabilities, limitations and risks, and can use it effectively. Specifically:

• follow the general and, where appropriate, academic literature on AI;
• take advantage of training opportunities, particularly those recommended by the University;
• seek opportunities to collaborate and share best practice.

Responsible Use

Ensure your use of AI tools and resources is ethical, legal and fair. Specifically:

• avoid malicious, dishonest or harmful uses of AI and adhere to recognised ethical frameworks;
• understand and mitigate the risks of using AI, including embedded ethnic, social and cultural bias;
• do not disclose or inappropriately repurpose personal or sensitive data;
• respect the rights of copyright and intellectual property owners.

Respect

Ensure your use of AI tools and resources demonstrates respect for individuals, society and the environment. Specifically:

• respect the privacy, protect the confidentiality, and ensure the agency of individuals;
• consider and mitigate possible negative impacts on individuals or society;
• be aware of and mitigate negative impacts on the environment.

There is an increasing trend to include AI functionality in common software tools, in ways that may not always be obvious. This can create ambiguity regarding the need to declare the use of AI tools and runs the risk of inappropriate disclosure. In such cases it is your responsibility to take all reasonable steps to apply the core principles as rigorously as possible, noting the following: 

• using AI simply to boost personal productivity does not constitute ‘significant use’; 

• you must make others aware if an AI tool will be used to process their input (e.g. to a meeting); 

• you should use University-approved tools wherever possible to avoid inappropriate disclosure; 

• if you use other tools you are responsible for ensuring there is no inappropriate disclosure. 

Avoiding Bias

Users of AI tools and models, and individuals or teams developing AI-based systems, should be aware of and, as far as possible, mitigate the potential for introducing bias. This requires an understanding of the data on which AI models have been trained – which may not be fully representative of the context in which they used. Where bias could have significant consequences, it is essential to undertake a bias audit and, where possible, take corrective action e.g. by model refinement or the or use of more representative datasets.

Teaching and Learning

University position

The University position is that, when used appropriately, AI tools have the potential to enhance teaching and learning and can support inclusivity and accessibility. Output from AI systems must be treated in the same manner by staff and students as work created by another person or persons, used critically and with permitted license, and cited and acknowledged appropriately.

Assessment

The University has produced a dedicated AI in Teaching and Learning Policy which sets out a shared framework for the use of AI in assessment at the University, defining clear and transparent expectations for students and staff when using AI across undergraduate and postgraduate teaching, learning, assessment, and research or dissertation activities. 

Plagiarism

There is an expectation that work submitted by a student for assessment should contain their own original work and provide an honest representation of their understanding of the subject. Students should declare any use of generative AI in preparing work for assessment, and explain its role.   Submitting work created by Generative AI as their own, or to misrepresent their understanding of the subject, is plagiarism and will be dealt with in accordance with the University’s Academic Malpractice Procedure.

Proofreading

Using an AI tool to correct grammar or spelling is acceptable, but where a student uses an AI tool for proofreading work submitted for assessment, they should ensure that use of the tool does not result in substantive changes to the content or meaning of their work or go beyond what is allowed for the assessment.

Access and choice

Where the use of AI is encouraged or required, the University must ensure students have equitable access to tools at no additional cost to themselves. Where the use of AI requires students to provide personal data to a third party, an alternative mechanism must be available that does not disadvantage any student who declines to provide such data. Students must be made aware of how third-party systems use their data, including their authored prompts and other uploaded content. 

Detecting malpractice

Tools to detect AI-generated content are unreliable and biased and cannot be relied on to identify academic malpractice in summative assessment. Output from such tools cannot currently be used as evidence of malpractice.

Research

University position

The University recognises the potential of AI to power research and innovation and encourages applications that adhere to the principles for appropriate use. It will seek to support appropriate use by providing equitable access to AI technology and training.

Synthesising data

Any use of AI to generate data should be completely transparent. Legitimate uses may include synthesising datasets for use in research on scarce or sensitive data, imputing missing data in real datasets or conducting research on generative AI. In such cases  
the distinction between real and synthesised data should be made clear, and the methods used should be detailed. Using AI to fabricate or manipulate data such as experimental measurements, interview texts or research images, without clear declaration, constitutes research misconduct. 

Publication

The corresponding author of an academic publication carries ultimate responsibility for its accuracy, balance and transparency. It may be legitimate to use AI in preparing a manuscript for publication, for example, to review the literature or improve writing style, but the corresponding author remains responsible for all content, whether AI-generated material is used verbatim or paraphrased. Specifically, the author should ensure: 

• publishers’ guidelines on the use of AI-generated content are adhered to; 

• any significant use of AI in preparing the manuscript is declared and properly referenced;  

• all claims in the text are accurate and sources properly referenced; 

• the selection of material is unbiased (e.g. in reviewing the literature); 

• references used to support a claim or observation actually do so;" 

• the source of all content is properly acknowledged and referenced. 

Reviewing

When reviewing papers for publication or applications for funding, AI should be used with extreme caution, and any use must be declared to the publisher or funder. Inappropriate use of AI can be considered research misconduct. Reviewers should avoid:

• breaching a duty of confidentiality by uploading parts of a document under review to an AI service, or writing prompts that contain confidential information;
• relying on AI tools when making reviewing decisions, rather than using their own judgment;
• using AI when it is specifically prohibited by the publisher or funder seeking the review.

Chatbots

There is growing use of AI-powered chatbots in research, for example to screen research participants or collect qualitative data. In such cases the use of AI should be made clear to participants. Where there is a need to elicit personal or sensitive information, it is not acceptable to use a public generative AI service because that risks disclosing it to third parties. 

Students undertaking research

Students undertaking research as part of their studies are responsible for maintaining a high standard of academic integrity. They should apply the principles of appropriate use and follow the specific guidance for both teaching and learning and research. As they are relatively new to research, they may be at risk of using AI inappropriately. As part of their research training, they should discuss any use of AI with their academic supervisor on a regular basis, and supervisors should mentor them on appropriate use.

When used appropriately, there are many potential applications of AI across the full spectrum of academic activity.  
Many of these will be discipline-specific, but the following is a non-exhaustive list of generic examples. 

Personal assistant: Intelligent support for personal organisation including generating task reminders, summarising documents, meetings and email conversations, and searching for information. 

Literature review: Finding relevant literature, identifying key publications, and providing summaries of the state-of-the-art in a field  
or sub-field. 

Writing and editing: Real-time spelling and grammar correction, style suggestions, and text revision to improve clarity and coherence. 

Accessibility and inclusion: Captioning and audio description, voice-to-text, text-to-voice, language translation and other assistive technologies. 

Data analysis: Analysing data, finding patterns, generating insights, and creating graphs and charts to enhance the presentation  
and interpretation of research results. 

Personalised learning: Creating personalised learning plans based on learners’ specific needs and providing real-time tutoring. 

Personalised feedback: Providing personalised feedback on work submitted for formative assessment to improve student experience. 

Critical thinking: Encouraging critical thinking by challenging key points in a document, identifying gaps, and suggesting ways  
in which arguments could be strengthened. 

Generating ideas: Generating initial drafts of academic papers, research proposals, business plans or policy documents as  
a foundation for further refinement and development. 

Understanding complex ideas: Summarising key points, clarifying important concepts, and answering specific questions. 

Planning T&L activities: Generating learning activity ideas, creating or enhancing lesson plans, and support the development  
of resources to support T&L. 

Personal research assistant: Suggesting relevant articles, suggesting new avenues to explore, and summarizing research papers. 

Experiments and simulation: Optimising the design of experiments, automating experiments, and simulating complex systems. 

Automating business processes: Providing tailored information or advice, automating decisions, and providing a concierge service for complex processes. 

There are inherent risks in using or developing AI systems, particularly generative AI. The following is a non-exhaustive list of some  
of the common challenges faced by users and developers. 

Confidential information: Submitting confidential or sensitive information to an AI system may result in that information being unintentionally revealed to other users. Similarly, the output of generative AI systems may contain confidential or sensitive information that should not be shared. 

Export control: When information about a research project is shared with a public AI system, there is a specific risk that this may violate export control regulations. 

Accuracy and reliability: The accuracy and reliability of AI systems are only as good as the data on which they have been trained, and training sets often contain errors or have uncertain provenance. 

Hallucination: Generative AI systems can make spurious connections between different data sources used in their training, leading them to fabricate ‘facts’ and misattribute sources. 

Bias and stereotypes: Many generative AI systems are trained on data generated by humans –mainly from developed countries and self-selected ethnic and social groups. This means the outputs they generate are prone to ethnic, social and cultural bias and stereotyping. 

Data cut-off: Generative AI systems are typically trained on a snapshot of data taken at a particular point in time, making them ‘blind’ to information generated since then. 

User input: The quality and appropriateness of the outputs of generative AI systems depend on the ability of the user to provide appropriate, clear, concise prompts. 

IP and copyright: Generative AI systems may generate content based on intellectual property or copyright material which the owners or creators have not licenced or consented for use. Similarly, submitting material that is copyright or otherwise embodies intellectual property to a generative AI system is likely to infringe the owners’ rights potentially leading to legal action. 

Impeding the development of knowledge and skills: There is a real risk that students or staff who rely too heavily and/or non-critically on AI systems will fail to acquire key knowledge and skills that are crucial to their development. 

Potential harms: Users and developers of AI systems may fail to foresee potential harms to individuals or society, and this is exacerbated by these systems’ lack of transparency or accountability. 

Generic vs specialised tools: Users can be tempted to use generic generative AI tools where more specialised tools (possibly also using AI) are available, resulting in poor outcomes. 

Academic Integrity at The University of Manchester: In this guide you will learn what is meant by Academic Integrity and what it means for your studies. You will find guidance and resources on how to avoid academic malpractice, and what may happen if it is suspected.  

Acceptable Use Policy: Outlines the principles for acceptable use of the University’s IT facilities and services. 

AI and referencing:  The University of Manchester Library’s AI referencing page explains how students should use AI tools appropriately in academic work, emphasising critical use, transparency, and correct citation of AI outputs to uphold academic integrity. 

AI at The University of Manchester: This course provides an overview of AI and explores how it is expected to develop within the University of Manchester and the wider world. 

AI in Teaching and Learning Policy: This policy covers a range of teaching and learning activities, including assessment, student wellbeing support and the use of lecture recordings 

AI Technical Security Standard at The University of Manchester: The Artificial Intelligence Technical Security Standard (AI TSS) sets out mandatory, risk‑based security, governance and compliance controls for developing and operating AI at the University of Manchester, ensuring safe, lawful, ethical and accountable use of AI across all systems and partnerships.  

Centre for AI Fundamentals: The University of Manchester’s Centre for AI Fundamentals is a cross‑disciplinary hub focused on foundational AI research and collaboration, covering areas such as machine learning theory, explainable AI, and human‑centred deployment. 

Environmentally Responsible Use of Artificial Intelligence (AI): This document outlines the University’s guidelines for the environmentally responsible use of Artificial Intelligence (AI), including Generative AI (GenAI), across research, teaching, operations and procurement. 

Equality, Diversity and Inclusion: The University of Manchester works to promote fairness, dignity and respect by embedding EDI across the institution through strategy, guidance, data, training and support for staff and students. 

EDI Training: The University of Manchester supports staff to build inclusive knowledge, skills and behaviours through mandatory and optional learning on equality law, unconscious bias, accessibility and active bystander practice. 

From Manchester for the world, our strategy to 2035: Manchester 2035 sets out the University of Manchester’s values‑led ambition to be a civic university rooted in social responsibility, inclusion, partnership, and academic excellence, creating knowledge and opportunities that benefit people locally and globally. 

ICO Guidance on AI and data protection: The ICO’s guidance on AI and data protection explains how organisations should apply UK GDPR principles to AI systems, emphasising lawfulness, fairness, transparency, accountability and the protection of individuals’ rights when using personal data in AI. 

Information Governance Policies: The University’s Information Governance Framework protects information and minimises risk. 

New principles on use of AI in education: The Russell Group brought together UK and Dutch research universities to share approaches to using generative AI in education responsibly, covering assessment practice, inclusivity, innovation, and shared AI principles. 

Our PDR Philosophy: The University of Manchester emphasises values‑led, reflective development by encouraging open dialogue, shared responsibility, inclusion, and continuous learning to help colleagues thrive and contribute meaningfully to the University’s goals. 

Principles for the use of AI in FE colleges - Jisc: Jisc sets out a shared, principles-based framework to help use AI safely, ethically and transparently while building learner skills, supporting staff, ensuring equality of access, and maintaining academic integrity.