MVST GenAI Policy

Due to the changing landscape of AI in education, this policy will be reviewed on an annual basis.

Given the wide variety of subjects and teaching and learning styles within the MedST and VetST, this policy framework outlines the allowance and rationale for the use of GenAI across MedST and VetST, with flexibility for departments to provide local supplementary guidance where required.

The following policy framework is agreed by the Medical and Veterinary Science Triposes Part I Committee, and builds on the University’s AI Policy Framework and should be used together with:

GenAI use in MedST and VetST

Artificial intelligence (AI) encompasses a wide range of sub-specialties with distinct and overlapping research areas, tasks, technologies and applications. Generative AI (GenAI), and specifically a subtype — Large Language Models (LLMs, including ChatGPT, Claude, CoPilot, and DeepSeek amongst others), have been the subject of debate in higher education institutions as the pros and cons of using software of this type are considered.

All students have access to Microsoft CoPilot and Google Gemini via your @cam.ac.uk account.

MedST and VetST students are permitted to make appropriate use of GenAI tools to support their personal study, research and formative work. However, they are strongly discouraged from copying and pasting content produced by GenAI tools into formative work.

The use of GenAI is not permitted for in-person, closed book examinations.

Direct copying and pasting content produced by GenAI into summative assessments, and presenting it as the student’s own work, constitutes academic misconduct, unless explicitly stated otherwise in the assessment brief.

Using GenAI can aid your learning very effectively, but much like other aspects of academic practice, you should think about GenAI and related software as options within a wider tool set. GenAI is not, and never will be, an effective replacement for developing skills or understanding in your subject area or bypassing critical thinking processes.

The subjects within the MedST and VetST have been carefully designed to illustrate the fundamental principles of each subject and lay the foundations for further study: the more you engage with the teaching and other resources available to you, the more you will learn and develop as a scientist.

General Principles

Some important principles to bear in mind when using GenAI tools and websites are as follows:

  1. GenAI tools can commonly produce incorrect or unsubstantiated information and as such should always be verified from trusted and reliable sources.
  2. Consider thoughtful use of GenAI and associated software where possible, making appropriate use to support your own development, using the most effective tool for the task at hand, and using efficient prompt-engineering to reduce the amount of iteration necessary.
  3. Be aware of the limitations, inconsistencies and biases that can exist within GenAI tools and data sets, and exercise caution when deciding to use information provided by software.
  4. Be accountable and take responsibility for how, when, and why you decide to use generated materials or information from GenAI software.  Remember, if you submit something that is GenAI generated and it is wrong, this is not the software’s fault. Using GenAI is easy; checking everything is hard.
  5. Be aware that there are restrictions on sharing material with restricted copyright (such as lecture handouts, pre-publication research information and other confidential or personal information) with any GenAI tool. In particular, do not use patient or client data, or any dissection prosections – this would be a severe breach of patient confidentiality and violate the University’s obligations under the Human Tissue Act.
  6. Be aware of the environmental impact of your use of GenAI tools and behave accordingly.

Examples of how GenAI could be used to support your learning

Opportunities for the use of GenAI are outlined below but always be mindful of points 1-6 above.

Lectures

Potential Benefits:

Understanding Key Concepts – GenAI could be used to explain concepts in a simpler language, illustrate relationships between ideas, or offer alternative perspectives. This can supplement lectures, tutorials, or reference materials by providing immediate clarification and supporting deeper engagement with content.

Some associated risks are that generated explanations may oversimplify or be inaccurate, and relying on them without verification against authoritative sources can create misunderstandings. There is also a risk of reducing engagement with primary materials, which may limit critical analysis.

Best Practice:

  • Always verify explanations against trusted sources.
  • Explore multiple perspectives, and do not use GenAI as the sole source of information.
  • Follow up with reflection or discussion with peers or supervisors to consolidate understanding.

Supervisions

Potential Benefits:

Generating Synopses – GenAI can summarise notes you have made yourself, condense long texts into concise summaries, highlight key points, or identify connections across sources. Summaries can be tailored based on level of detail or focus area. This can save time and support organisation of information, making it easier to review, compare sources, or integrate multiple readings.

Some associated risks are that summaries may omit important nuances or misrepresent ideas, and over-reliance can reduce engagement with the original text. Critical reading skills may be weakened if summarisation is used without verification or reflection. In addition, you are not permitted to upload lecture notes or any work that is not your own onto AI platforms. It is inappropriate to share intellectual property that you do not own.

Best Practice:

  • Use summaries as a starting point, not a replacement for reading the original text.
  • Cross-check summaries with source materials for accuracy and completeness.
  • Reflect on the implications or connections highlighted to deepen understanding.

Practical Classes

Potential Benefits:

  • Data Analysis – GenAI can assist with organising, summarising, or identifying patterns in quantitative or qualitative data. GenAI can provide insights, highlight trends, or suggest visualisation approaches.
  • Supporting coding tasks – GenAI can assist with writing, debugging, and explaining code snippets, but always verify outputs against authoritative documentation or test cases.

Some associated risks are:

  • GenAI-generated analysis may misinterpret data, overlook context, or produce biased results. Blind trust in outputs could compromise the validity of your results.
  • GenAI-generated coding may be completely erroneous, may contain some errors or fabricate outcomes.

Best Practice:

  • Cross-check GenAI outputs with standard analytical methods.
  • Ensure context, assumptions, and limitations are clearly understood.
  • Combine GenAI insights with critical evaluation.
  • Verify all GenAI generated references by checking the sources carefully wherever possible.
  • Always check to ensure consistency in style/formatting of references.

Assessment

Assessment includes any work that is assessed that is not an examination, including coursework, dissertations, essays, project write-ups, poster presentations and oral presentations.

Potential Benefits:

  • Understanding Key Concepts – GenAI could be used to help you summarise papers in a simpler language, illustrate relationships between ideas, or offer alternative perspectives. This can supplement reference materials by providing immediate clarification and supporting deeper engagement with content.
  • Enhancing clarity and structure in writing - GenAI could be used to improve sentence flow and check for coherence or grammar.

Some associated risks are:

  • GenAI generated text may oversimplify or be inaccurate and relying on this without verification against authoritative sources can create inaccurate or untrue sections in your work: if this happens, it is the responsibility of the user, not the responsibility of ChatGPT (or equivalent) for the errors that have been introduced. There is also a risk of reducing engagement with primary materials, which may limit your ability to produce critical analysis.
  • GenAI-generated references may be completely erroneous, may contain some errors, may be formatted incorrectly and may not attribute authorship correctly. Referencing software should be used instead.

Best Practice:

  • Always verify explanations against trusted sources.
  • Explore multiple perspectives, not using GenAI as the sole source of information.
  • Follow up with reflection or discussion with peers or supervisors to consolidate understanding.
  • Verify all GenAI-generated references by checking the sources carefully wherever possible.
  • Always check to ensure consistency in style/formatting of references.

Cambridge AI Literacy Course

Cambridge University’s Blended Learning Team have put together an AI literacy course intended to equip participants with the knowledge, agency and critical awareness to become informed users of Generative AI (GenAI) in education.

The course is organised into three modules:

  1. Introduction to GenAI and Large Language Models (LLMs): What is GenAI, what are LLMs and how do they work
  2. Artificial Intelligence in Teaching and Learning: practical techniques to mitigate potential risks of using GenAI and promote the responsible integration of AI tools in education.
  3. Artificial Intelligence Ethics, Regulation and Policy: focuses on developing a more complete understanding of the impact of artificial intelligence and how to use it responsibly in an education setting.

Modules 2 and 3 provide some excellent tips on points 1-6 above (General Principles).

We encourage all staff and students to review the course materials and use the content to inform local decision-making and guidance where possible.