Introduction to AIDED

Generative AI (GenAI) is widely seen as a disruptive force across the creative industries – one that today’s design students will need to navigate in their professional life. But how do they view the use of the technology in design practice? And what role should GenAI play in shaping design education?

A new study from The Open University – the AI Design Education (AIDED) project led by AnnMarie McKenna and Catherine Scott with Sally Caird – has begun to explore these questions. Over 1,000 undergraduates enrolled on OU design modules were invited to participate in the AIDED survey, resulting in 80 responses – a 7.5% response rate.

AIDED survey aims

The survey set out to explore students’ views and experiences with GenAI image tools, compare these tools with conventional visual communication methods, examine the perceived benefits and challenges, and identify appropriate ways such tools might be integrated into design education.

It is important to acknowledge some limitations with the survey, which had a relatively low response rate (7.5%) and relied on self-reported data. So, the findings may not be fully representative or generalisable. Despite this, the results provide useful exploratory insights into students’ views and experiences.

We would like to thank the students who shared their time and perspectives, providing valuable insights for the AIDED research.

AIDED survey findings

Student profile

The OU design students who responded were typically older than the national UK undergraduate age profile, digitally confident, and often career-experienced – studying design to support career development or change. The main motivations for study were a passion for design and professional growth.

Awareness and use

Although most students reported strong digital confidence, their use of GenAI image tools was limited. Just over half were aware of tools, such as DALL-E, ChatGPT, Midjourney, Adobe Photoshop (with Firefly), and Canva. However, fewer than half had actually tried them, and less than a third used them regularly. Most of the experimentation was for personal purposes, although some had trialled tools in professional contexts – finding it particularly valuable for creative exploration and achieving efficiency gains. Overall, this suggests a cautious approach to adoption, with relatively few students engaging with GenAI image tools on a regular basis.

Conventional visual methods are still favourites!

Most students favoured conventional visual communication methods for addressing design problems and solutions – sketching, photography, descriptive text and physical prototyping – over GenAI alternatives. Notably, 80% reported they rarely or never used AI generated images on design modules. Around half assumed such use was not permitted, while a smaller proportion cited a preference for conventional methods or limited knowledge of GenAI as reasons for non-use. Some students also raised concerns that an overreliance on GenAI would erode creativity in design practice – a recurring theme in the data.

Barriers and benefits

Adoption of GenAI was further held back by strong deterrents, including lack of clarity over permissions, high software costs, unclear guidance, and uncertainty about intellectual property rights – all cited by around a half or more respondents. Despite these reservations, around two-fifths also recognised the potential benefits of GenAI for idea generation, communication of design ideas and solutions, image refinement, and professional presentation.

GenAI tools

Many students commented enthusiastically on the tools they considered to be the ‘best’. These included tools like – Adobe Photoshop with Firefly, Canva, Craiyon, DaVinci AI, DreamStudio, Leonardo AI, LightX, Midjourney, DALL-E, PicsArt, PixIr AI, Vizcom, Yodayo, Microsoft Designer and AI-enhanced stock image sites. They also valued general purpose AI platforms, such as ChatGPT, Copilot, Bing AI and Discord AI Bots. They appreciated their ease of use – particularly intuitive user interfaces, collaboration features, controllability, and accessibility – as well as their usefulness for creating, refining, and iterating high quality imagery.

GenAI skills – a boost for employability?

Most strikingly, the majority (85%) believed that developing AI image generation skills would enhance their employability and career prospects. However, comments from students suggested that GenAI skills were only beneficial if the technology is used to augment rather than replace the human element in creative design. Despite this, only about half of students supported the formal integration of GenAI into design modules. This suggests a tension between recognising the value of GenAI for employability and future careers and uncertainty about the role it should have in design education.

Conclusions

Overall, the findings suggest that if GenAI image tools are introduced with clear guidance on permissions, referencing, and good practice, and integrated with conventional visual methods in design education, the technology has the potential to enrich learning, strengthen design skills, enhance employability, and prepare students more effectively for the evolving expectations of industry.

Recommendations include:

• establishing clear polices and guidance on GenAI use
• developing module resources to build student confidence and AI literacy
• piloting the integration of GenAI activities with conventional methods
• monitoring GenAI’s impacts on creativity, learning, visual communication skills, and employability.

It is increasingly clear that future designers will enter industries where GenAI is part of the professional landscape. If so, the question is no longer whether design education should engage with GenAI, but how that engagement should be designed and delivered.

The AIDED project continues research with an in-depth study of how students use GenAI image tools for visual communication to provide further insights.