JCQ and Awarding Bodies take the view that AI-generated images are not the candidates’ unaided independent work. Therefore AI images cannot be used as evidence of students’ independent achievement in meeting the examination criteria and will not receive any marks. Students must be informed of this. The obvious implications of this aspect of the regulations governing NEA would suggest that students should not submit AI generated work. If they do so and declare it, then it will be ignored. If they do so and do not declare it they will be accused of cheating.

However, students may use the internet and AI in their research, treating it in the same way as any other reference material – such as Google-resourced images.

Students must always declare the source of their references, including AI-generated imagery. They should never simply copy such images – plagiarism.

See more on the Exams, Rules, and Options page.

In 2023 the then government identified five principles that would inform the evolution of AI in the UK. This included the education sector, schools and Ofsted. Ofsted will begin to take account of a school’s appropriate and safe use of AI. As a consequence, it is assumed that soon, most schools will have an AI policy, and templates will become available on the Internet.

Art teachers will wish to engage in this whole school debate and follow school policies. Art teachers may feel that AI poses particular issues in their subject and will wish to clearly establish subject-specific guidelines and principles for their students. Art departmental guidelines will vary from school to school.

Read more on the ‘Policies‘ page.

Click here for some interesting pragmatic ideas about how to teach responsible AI art education from the ‘Art of Education University‘. These include student agreements to confirm and reinforce everyone’s understanding of responsible AI use.

Possibly, if well written prompts are used. However, it should best be seen as presenting ideas and suggestions rather than answers. It would be good practice to try different prompts and different AI programmes rather than just using a single response. 

We are currently conducting research into prompt engineering to identify and translate abstract concepts into pragmatic teaching programmes. Our current research involves:

Stage 1: Identify concepts (these could be about improving the design content of the curriculum, but could be any aspect, drawn from the Big Landscape for instance);

Stage 2: Upload the abstract concepts (objectives) into an AI programme and ask it to create a prompt which would be most effective in creating a programme of study to achieve this outcome. Specify age range, school context etc. of the students;

Stage 3; Feed this new AI generated prompt into a few different AI programmes.

It is hoped that this would be useful in translating abstract aspirations into pragmatic teaching propositions for teachers to consider as they reflect on their programmes of study.

Our initial findings are promising, but more testing needs to be done. We will publish these when ready – but colleagues are welcome to contribute to this research and share ideas in the comments below.

See more about prompt engineering on the ‘Prompts‘ page.