Guidance on AI-assisted Marking and Feedback at York

The University’s position is neutral on whether AI tools should actually be used, nor does it suggest that AI should be used to replace the expertise and judgement of humans. But it does emphasise that care is needed to ensure that any AI use is responsible. The overarching principle is that the allocation of marks is a human activity that cannot be delegated to AI or other technology.

Background

The purpose of this guidance is to look at ways that AI can be used in the process of marking and giving feedback to students, specifically at how human oversight can be enhanced in these workflows.

The guidance is based on previous work by King’s, LSE and Southampton. It is deliberately neutral on whether GenAI should be used, but its adoption is intended to ensure that, if a decision to use GenAI in assessment and marking is made, then such usage is responsible and transparent to students. It is not suggested that AI should be used to replace the expertise and judgement of humans, and it is emphasised that student work should not be uploaded to any AI system without explicit consent and forethought.

There are many different ways in which teaching staff might consider using different forms of AI assistance in marking and feedback, including:

1. Enhancing and clarifying feedback initially authored by human markers (for example to change the tone or reduce jargon).
2. Using AI tools to generate draft rubrics that are aligned with module and programme-level outcomes.
3. Aligning human-generated feedback notes to specific rubric criteria for structured and personalised responses.
4. Digitising hand-written notes or converting text to spoken feedback to enhance readability, clarity and engagement.
5. Capturing, transcribing, and summarising verbal feedback discussions or meetings (for example during supervisory meetings or oral examinations).
6. Summarising and synthesising individual marker feedback into coherent collective reports.

Of course, another choice open to markers is not to use AI at all.

Principles

The overarching principle is that the allocation of marks is a human activity that must not be delegated to AI or other technology. The only exception to this is where the allocation of marks involves no academic judgement, such as for MCQs, or quantitative right/wrong questions.

To develop trust between staff and students the following principles for AI-assisted marking and feedback must be followed:

1. Teaching staff must explain their approach to, and use of, AI-assisted marking and feedback.
2. If a decision is made to use AI to assist in marking and feedback, teaching staff must only use institutionally-approved tools.[Generative AI tools] These tools comply with GDPR and relevant privacy regulations and provide enterprise-grade assurances of security, data privacy, and training restrictions for Large Language Models (LLMs). If unsupported GenAI tools are used, staff should explain their rationale and obtain appropriate approval from IT Services.
3. Where AI-assisted feedback has been appropriately used, challenges to academic judgement will not be accepted, in line with standard university regulations.
4. GenAI tools may inform the development of marking criteria and assessment rubrics.
5. Where several members of teaching staff are involved in the assessment of a single module, the approach to AI assistance must be uniform across the marking and feedback team.
6. Where GenAI tools are used, they must be used with due regard for safety and security, and considerations such as data governance, copyright and environmental sustainability.
7. Staff must not include any student details or upload work into any GenAI tools without explicit student permission. This prevents personal data from being collected, stored, accessed, and shared without consent.
8. Teaching staff must follow institutional and local (faculty/department) guidelines to ensure their approach to AI-assisted feedback and marking processes aligns with agreed practices. Transparency should start with, and extend from academics to students.
9. Teaching staff must reflect critically on how AI-assisted feedback and marking practices influence student perceptions of authenticity, trust, and fairness. Staff must actively seek student feedback on these practices.
10. Teaching staff must provide human oversight at every stage of the approach they are adopting. GenAI can generate incorrect responses based on probabilities and biases. While GenAI can produce summaries of given information and solve known problems, it can also produce simplified or incorrect outputs and combine existing knowledge in new ways that are not valid.

Disciplinary norms

There is a variety of attitudes to AI among teaching staff across the institution, which has led to confusion for students, both in what they are permitted to use themselves and in what usage they might see among teaching staff. In order to provide some consistency on the latter point, it is recommended that all departments / schools should debate and then endorse a Departmental / School Statement on AI use in Marking and Feedback, possibly as part of a wider Statement on AI in Education. Further details to follow.

Policy

AI assistance in the marking process

AI-based tools can be used to assist in the marking of student work, but only as an assistant: the allocation of marks is a human activity and cannot be delegated to AI or any other technology. In addition, any usage must conform to the UEC-agreed principles (above) for such tools.

AI assistance in the feedback process

AI-based tools can be used to assist in the process of providing feedback on student work, but any usage must conform to the UEC-agreed principles (above) for such tools.

Examples

The following scenarios comply with the above principles and offer insights into ways that academic staff can use AI transparently and in an assistive capacity, always ensuring human oversight and judgment remain central.

Scenario A – Scaling feedback while maintaining quality

Lecturer A is responsible for marking over 100 essays within a two-week window. Conscious of the limitations this workload places on the depth of individual feedback, they adopt a hybrid approach using the university’s approved LLM tool, Gemini.

Without ever uploading student work directly, Lecturer A composes an anonymised summary for each student, noting which marking criteria were met and the approximate percentage achieved for each. They input this summary alongside the official rubric into Gemini, prompting it to generate supportive, criterion-referenced feedback. This feedback is then carefully reviewed, adapted, and personalised before being uploaded to the marking platform.

Students are made aware of this process in advance and shown a demonstration, reinforcing transparency and trust.

Scenario B – Harnessing accessibility features

Lecturer B experiences recurring pain from repetitive strain injury, making traditional typing-intensive marking methods challenging.

To reduce physical strain, they have begun using the voice chat functionality of an AI tool while reviewing assignments. They verbally articulate their comments during the review, ensuring all input remains anonymised and free of identifying details. The AI transcribes the spoken reflections in real time and is then prompted to produce a concise summary, isolating one key strength and one or two developmental areas to support student progression.

Students are made aware of this process in advance and shown a demonstration, reinforcing transparency and trust.

Scenario C – Bridging traditional and digital practices

Lecturer C prefers the immediacy and freedom of handwritten annotation when reviewing student work.

Historically, these handwritten notes were then typed into the feedback platform - a time-consuming duplication of effort. More recently, Lecturer C has started photographing their feedback notes and using an approved LLM to transcribe the content. The LLM is prompted to reframe the handwritten comments into a clear, structured feedback format organised into bullet points under three headings: strengths, areas for development and points for action. This preserves the authenticity of the lecturer’s voice while enhancing clarity and readability for students.

Students are made aware of this process in advance and shown a demonstration, reinforcing transparency and trust.

Scenario D – Calibrating grades to align with feedback comments

In a previous module, a new teaching assistant had been enthusiastic about giving students a positive and encouraging experience in formative feedback. This had backfired when students noted a mismatch between the superlatives in the comments and the low indicative grades given, leading to discontent among students who felt their indicative grade should have been higher given the highly positive comments, and this led to concern that grades and feedback on the summative assessment might be affected in the same way.

In a formative assessment on a current module, the teaching assistant deploys a GenAI tool to input the official marking criteria and rubric along with their anonymised feedback comments to validate alignment between the indicative grade and the feedback language. This helps nudge the teaching assistant to moderate their language to match the marking criteria more closely.

Students are made aware of this process in advance and shown a demonstration, reinforcing transparency and trust.

In each of these scenarios, GenAI has been used in a responsible and transparent manner that demonstrates the benefits of AI-assisted feedback to students. Taking this approach does not compromise trust, academic integrity or fairness. Augmenting the clarity, depth and consistency of feedback and streamlining grading processes will enhance the quality of the education provided to our students.

Acknowledgements

This guidance was initially based on the work of colleagues at King’s, LSE and Southampton.

University Education Committee
July 2026