AI acknowledgement

Acknowledgement and accountability are important parts of the evolving etiquettes for responsible AI use. Humans need to be “in the loop” and able to explain why and how AI was used in decision making or creations produced in collaboration with AI. Documenting the processes undertaken is a related way of visibly demonstrating how humans and AI collaborated in the production of something. In addition, humans need to be able to confirm the accuracy and veracity of outputs and are accountable for the outputs.

Further understanding of evolving etiquettes for acknowledging AI use can be found in the approaches being developed for academic publishing. For example Andy Stapleton’s review of academic publishing approaches.

As AI becomes more integrated within our teaching and learning activities, acknowledging and explaining use of, and collaboration with, AI is an important aspect of responsible use.

Responsible use of AI should be explainable in terms of:

What AI was used
Which AI tools assisted in producing the assessment, how many versions and drafts were undertaken, and what known biases may exist with the data sources/AI tools used.
How AI was used
Which aspects/parts of the assessment did AI assist with, including how the AI contributions have been modified, expanded, incorporated, etc.

The following guidance is targeted to educators but applicable to all staff using AI

Citing/referencing source material and acknowledging AI use View

Consider incorporating open conversations in classes and clear information in Moodle about expected academic practices and conventions relevant for your discipline/profession. This includes presenting citing/referencing and acknowledging the use of AI as two separate academic practices that make visible and transparent how an assessment has been created.

Citing/referencing identifies which sources of information are being used to inspire and/or as evidence in an argument. For example, which authors and their papers/ideas were used to inform or position your work. Citing/referencing identifies who’s ideas one is in dialogue with and where the ideas were drawn from/can be found by a reader.

Since items generated by AI are not a primary source of information that can be found and interpreted by others, AI tools and their output are not really a citable source. For example, a prompt generating a text in Claude or an image in Dalle2 will yield different outputs each time so there is no stable reference but a quote on page 2 of an article in an academic journal will be a stable reference. Following disciplinary conventions, citing/referencing is visible through footnotes/endnotes/inline citations and bibliography/reference lists following the system/style identified for use in a unit/assessment by the CE.

In contrast to citing/referencing, acknowledging the use of AI identifies which tools were used in the collaborative production of the assignment. For example, which tools helped with brainstorming, researching, experimenting, writing, editing, etc. for a submission. Acknowledging the use of AI helps explain the processes of how the assessment was produced. Acknowledging the use of AI can be provided in the acknowledgement sections in assessments per the CEs instructions.

CEs should also consider complementing acknowledging the use of AI within assessments with having students carefully document and share the production process, which may include records of iterative chat logs, evolving drafts of portions of assignments, etc.

Strategies for acknowledging AI View

In order to support students in providing concise information about how they are responding to Chief Examiner guidance on AI in assessments and tasks and how and where they used AI in the production of their assessment we suggest adding an acknowledgement section into assessment submissions.

Acknowledgement sections can provide a consistent overview and acknowledgement of how AI has been responsibly and ethically used as part of the assessment submission.

Acknowledgement sections also complement the completion of the submission statement during the Moodle submission process that confirms that the assessment also complies with all academic integrity requirements.

Acknowledgement sections can also be supplemented by submission of detailed documentation of the production process, where required by the Chief Examiner.

Acknowledgement sections within assessments should include two basic components:

Reinforcing the conditions of AI use specified for the assessment by the CE in Moodle
Explanations of what and how AI has been responsibly and ethically used to produce all or part of the assessment submission

➤ Simple acknowledgement explanations may include:

Identifying which AI tools were used and for what purposes.
Describing the choice of tools used, explaining why they selected particular tools and providing a web link to the AI tools that they used.
Noting the number of iterations used and evaluated for each AI tool.
Explaining what output from the AI tool/service has been included, and where the output has been altered, adopted, integrated or built upon.

➤ Consider using the following formula/syntax to help students summarise AI use:

I used <ADD AI tool> (<ADD link if needed>) to <ADD how used> (<ADD number> iterations/drafts). I modified the outputs in <ADD ways>.

➤ Consider dividing the explanations of the use of AI tools into the following categories:

generate ideas or structure suggestions, for assistance with understanding core concepts, for searching for materials or other substantial foundational and preparatory activity for the assessment.
generate text, rewrite, rephrase and/or paraphrase a portion of this assessment.
generate some other aspect of the submitted assessment.

➤ Consider supplementing the acknowledgement with the inclusion of a formal reflection on what was learned through collaborating with AI tools in the production of the assessment.

➤ Consider providing an example that articulates the level of detail that is expected in the acknowledgement response to help clearly communicate requirements and expectations to students.

Important educator resources

The following documents provide templates for CE to use or modify to create acknowledgment sections appropriate for their assessment.

Acknowledgment section template as free text Word file for editing and incorporating into assessment submissions.
Acknowledgment section template as a Word form that can be modified as needed then completed and added at the front or back of an assessment submission.
Some examples of different versions for consideration (as a PDF).

As we progressively learn together how to think with AI, the acknowledgement templates allow us to record and reflect on the range, uses and potentials of emerging tools and practices.

Further considerations regarding acknowledgement sections of assessment

Version	Can I use the templates provided as is within my assessment design or do I need to modify the instructions and requirements?
Location	Where will I incorporate the acknowledgement information within the submission package so that it can be received and reviewed in a consistent way across submissions? As the first page, within the assessment when using AI tools, as an appendix, etc.
Iterations	How much guidance to give students regarding documenting iterations/drafts/attempts? Understanding of the volume of iterations provides some insights into the learning and engagement with a particular tool and the learning journey. Requiring multiple iterations to be produced, evaluated and synthesised also provides a vehicle for learning.
Process documentation	Is the summary of AI use via an acknowledgement section sufficient or do I also want/need students to submit documentation of the process? If so, how detailed should the process documentation be? What examples can I provide to guide students?
Feedback	With the acknowledgement incorporated into the assessment submission and separated from the academic integrity submission statement, what kinds of feedback will be provided to students regarding their responses to the acknowledgement section(s), and documentation of process where required.
Weighting	Where required as part of the submission, how much weighting/value, if any, will be assigned to the acknowledgment section and process documentation within the marking regime for the assessment. For example, is it part of a criterion about following instructions, a separate criterion (e.g. satisfactory/unsatisfactory), a category for critical engagement with learning/production processes, or is it mandatory but not included in the marking regime?
Reinforcing responsibility	Do I want to include the confirmation to reinforce that the submitting student or team take responsibility for the content?
Trust	What kinds of open conversations do we need to facilitate in order to encourage students to honestly and ethically complete the acknowledgment section(s) and explain their responsible use of AI. For example, we don’t feel compelled to share which AI navigation software we used in our car or smartphone to help us get from point A to point B. If asked we are not likely to be reluctant to share which recipe(s) we found and used to make a tasty dish. There is no shame in using one word processor over another. How do we craft a culture of transparency and comfort explaining AI use?
Reinforcement	Can the acknowledgement statement be used as a confirmation that students followed instructions and did not use AI? In other words, could the additional submission supplement the standard academic integrity tick box within Moodle and thereby reinforce ethical academic practices?

Documenting use of AI View

Documenting AI use is a useful practice to be able to show how an outcome or decision was arrived at with AI inputs if/when an explanation is required. Documentation of the learning and production process, including the iterative feedback and revisions included in the process, also helps shift focus from just the “what” of the final product to also recognise and value the “how” and “why” of the process.

Please note that documenting AI use is separate from acknowledgement of AI use but can support that process.

In pedagogical contexts, documenting AI use can be a useful practice to make the learning journey visible and tangible. Documentation can:

provide a basis for reflection on the knowledge and skill acquisition
support the preparation of AI Acknowledgement statements
provide further evidence to support claims made in the overview AI Acknowledgement statements
Track the contributions of multiple intelligences in the production of an output

Educators should provide students with guidance on the appropriate ways to document an assessment. Below are various approaches to documenting and acknowledging AI processes in assessments. Each method should be adapted or modified to align with the specific learning context.

Expand the accordions to learn more

Shared links are a feature that allows users to generate a unique URL for a ChatGPT conversation. This URL can then be shared with friends, colleagues, and collaborators. Shared links allow users to share their ChatGPT conversations, replacing the old and burdensome method of sharing screenshots.
ChatGPT instructions
Graphical annotations can transform static works into living narratives by pinpointing moments where generative AI intersects with human authorship. Whether in creating poems, prose, or surreal images, these visual annotations provide context, demystifying the process for the reader. In digital formats, these annotations could become interactive layers, inviting deeper exploration.
Scribe, a generative AI tool, streamlines workflow documentation by automating the creation of descriptions, user manuals, and FAQs. It allows users to record working processes across any application, desktop, or web, capturing cross-application processes seamlessly. To capture a Scribe, simply turn on the Scribe recorder via the browser extension or desktop app, walk through your process as usual, and turn it off when finished. Scribe then automatically generates a step-by-step guide with screenshots, text, and cursor clicks. Additionally, Scribe offers a range of editing options, enabling users to edit, reorder, and enhance documentation.
Progressive overview maps serve as succinct summaries that capture current thinking and making. They encourage regularly pulling back to ‘map the big picture’ – the research foci, project aims, and compelling questions as understood at a particular moment. This practice helps maintain perspective ‘in action’ and captures the evolution of a project for later reflection. The form these maps take will be context-dependent and could be produced at set or regular intervals, such as daily, weekly, or monthly.
Tracking changes and document history features in platforms like Google Docs or Microsoft Word can be useful for identifying the potential use of generative AI by students. These features reveal significant shifts in writing quality, inconsistencies in style or tone, and rapid content additions, allowing for comparisons between document versions. However, they are not foolproof and may raise privacy concerns. Students might draft work elsewhere, leaving no trace in the document history, or be savvy enough to bypass these checks. Therefore, promoting academic integrity and educating students on the responsible use of AI is crucial for effective monitoring.
When documenting processes involving generative AI, screenshots or recordings are invaluable. These visual snapshots serve as step-by-step guides, capturing each stage of the workflow. Accompanying captions or brief narratives can clarify the steps taken and provide curated evidence of content generated by AI models. Sharing these visual records alongside the final outcomes ensures transparency and potentially enhances reproducibility.
When documenting processes involving generative AI, screenshots or recordings are invaluable. These visual snapshots serve as step-by-step guides, capturing each stage of the workflow. Accompanying captions or brief narratives can clarify the steps taken and provide curated evidence of content generated by AI models. Sharing these visual records alongside the final outcomes ensures transparency and potentially enhances reproIf an assessment is produced through multiple drafts, then each version could be included with a cover sheet briefly summarising the key differences and/or how the draft responds to feedback provided.

Contributions

This content was produced with contributions from the AI in Education Learning circle: Brendan Boniface, Jeremy Breaden, Michael Burke, Fabio Capitanio, Thu Do, Wendy Ellerton, Andrew Junor, Jennifer Mansfield and Sadia Nawaz.

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