Arts educators serve a vital role in ensuring students deeply understand these technologies and can critically evaluate their usage. Rather than viewing AI systems as 'black boxes' that simply output art and media, students should scrutinise how large language models function, probe their limitations, and identify potential harms stemming from biases or lack of transparency.
Constructively integrating AI into coursework guides students to think through necessary ethical precautions and can inspire rich conversations around human vs automated creation. Equipping learners to ethically co-create with AI as a tool, not as a replacement for human imagination and expression, will empower the next generation of writers, designers, and creative professionals to harness its potential while proactively addressing risks.
Here are some best practices for incorporating AI critically into arts curricula:
Tips:
- Have open conversations about promises and perils
- Teach how systems work and their capabilities and limits
- Use AI tools transparently to augment creativity
- Develop acceptable use policies and governance
- Consult experts in AI ethics and include student input
Examples:
- Lectures on how language models work, training data, and capabilities
- Assignments analysing biases/shortcomings of AI-generated art
- Using AI drafts and outlines to promote creativity with human refinement
- Requiring ethical development practices for student AI projects
Useful readings:
Tegmark: Constructive ways forward on AI safety: https://futureoflife.org/ai-policy/written-statement-of-dr-max-tegmark-to-the-ai-insight-forum/
Meese & Pinto: Implications of generative AI for cultural industries: https://hbr.org/2023/04/how-generative-ai-could-disrupt-creative-work
Jiang & Villeneuve: Diversity and accessibility in algorithmic creativity: https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/---publ/documents/publication/wcms_831316.pdfEquipping students to ethically co-create with AI as a tool guides them to harness its potential while addressing risks. Educational activities centred on critical analysis of outputs surface meaningful deficiencies for exploration. This foundation facilitates informed advocacy and leadership surrounding AI’s progress to positively shape its integration across creative industries.