February 16, 2024, by Laura Nicholson
Engineering Faculty Takeover: AI in Teaching and Learning
Throughout the 2023–4 academic year, we are running a new feature on the Learning Technology (LT) blog: a faculty takeover month! Each month, we will feature posts from different faculty members at the university. Every Friday, posts will highlight interesting work and ideas related to technology in teaching and learning and showcase unique projects from within the various disciplines across the UoN. So far, we have promoted the work of the Faculties of Science, Medicine and Health Sciences, and Arts. This month, we welcome posts from the Faculty of Engineering.
This week, Rob Shipman, Associate Professor in Engineering, contributes to the discussion surrounding AI’s role in academia, with staff and student user cases offering insights into the application of AI within Engineering.
Author: Rob Shipman
Although foundational thinking occurred in the early 20th century through the work of Alan Turing and others, the Dartmouth Workshop of 1956 is widely considered to be the birthplace of Artificial Intelligence (AI) as a discipline. This workshop brought together many of the leading figures of the time with the oft-quoted proposal that “every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it”.
The field has shown great promise, stagnated, spurted into life, and then stalled again ever since. It has shown the ability for extraordinary performance in games such as chess or go, can help us decide what to watch on Netflix, and now drive our cars. However, it was the launch of ChatGPT in 2022 that recently catapulted AI into the public consciousness, reaching 100 million users within 2 months! An example of so-called Generative AI, such models no longer simply learn from historical data to help predict future outcomes but can generate entirely new content in response to appropriate prompts.
“Any sufficiently advanced technology is indistinguishable from magic”, Science fiction writer Arthur C. Clarke’s 3rd Law.
Those early interactions with Generative AI felt a little magical to many. However, a technology that can pass exams, produce essays, create novel art and designs etc. was always going to turn Higher Education on its head. So, is this new technology friend or foe for academia?
The initial response from academia was predictably, and understandably, defensive. It was necessary to protect academic integrity and update rules, procedures, and plagiarism checks accordingly. The limitations of current models, including the tendency to fabricate content or “hallucinate” were also apparent. However, the genie was well and truly out of the bottle. The potential benefits to both staff and students increasingly evident.
Within the Faculty of Engineering and the University more broadly, we are now increasingly looking at ways in which we can support students to become expert users of a technology that will inevitably be widely used in their future careers. The aim is to enhance their creativity through embracing this technology rather than trying to stifle its use. Staff too could benefit from efficiency gains and scaffolding of their content. These goals are encapsulated in the Russell Group principles on the use of generative AI tools in education, which we are aligned with: “Our universities are committed to the ethical and responsible use of generative AI and to preparing our staff and students to be leaders in an increasingly AI-enabled world.”
Many do and will continue to argue that we should outlaw the use of generative AI. That it can be nothing more than copying and plagiarism of others work. This brings up interesting perspectives on the nature of creativity. Generative AI models do indeed assimilate enormous amounts of data to help them create new output. However, this output is not usually a simple copy of that data but rather new content that follows similar structures and patterns. Is this so different to what the human brain does? What, if anything, is the additional “spark” of creativity that exists in a human brain?
The debate will continue to rage but one thing is for sure, AI is here to stay and its impact on Higher Education and society more broadly will continue to grow. The Faculty and University are committed to help shape this evolution (revolution?) and help ensure these tools are used for the benefit of all.
Student Use Case by Lucas Noble a 3rd year Product Design and Manufacture Student.
“My 60-credit final design project is well on the way. I’m aiming to design a stationary exercise bike for users with neurological impairments, like strokes and Parkinson’s.
To my absolute enjoyment, it was made clear that the use of AI wouldn’t be rejected but embraced and required further exploration. I quickly arrived at an AI Image generating software called Midjourney. Midjourney is a ‘prompt’ operating system that requires the user to input a series of words to generate 4 images.
To no surprise, I immediately generated many recumbent exercise bike designs, trying to incorporate dictating adjectives like ‘sleek’ or ‘bulky’. The results were mind blowing. Midjourney was churning out forms and shapes that I had never seen before, but more importantly, forms and shapes that, personally, I would have never managed to imagine, especially on complex products like a recumbent exercise bike (Figure 1. Image 1). As far as ideation is concerned, to have those images of whacky shapes and bold forms in my mind has been an invaluable source of exploration and fuelled my desire to design an aesthetically interesting design. However, Midjourney has by no means solved all problems. The more I use it, the more I acknowledge its flaws, the images generated are rarely functionally correct and would require considerable refinement. My approach was never to use AI as my saving grace but to analyse these images correctly. Why do I like this specific version? What is the feature that makes me feel the way I’m feeling? Do I want to feel that way when using my product? And so on… I’m sure that the more creative I am with my analysis, the more personal my product will be.
How could I take this further? Ideation of colour? I generated different recumbent bikes in different colour schemes and analysed the impact of those. In my head, a red and black combination is a good route to achieve a gadget, techy feel. When AI helped me visualise that a red and black colour scheme on a recumbent bike provided an intense and daunting presence and felt like the bike could be used to plug into the matrix (Figure 1. Image 2). AI easily enabled me to alter colours to blue and white, which provided something completely different (Figure 1. Image 3).
I’m now exploring the impact of semantics in my design by writing prompts like “image of a bike inspired by a Ferrari” and I’m being provided with an exercise bike that looks like it’s been designed by Ferrari! (Figure 1. Image 4) But once again, the image is only as useful as the analysis that I’m willing to put in. Why does this bike look and feel fast? What features enable it to do that and look like it’s operated in a certain way?
There are endless opportunities with AI in Design, I’ve only provided a few brief examples of how I’ve used it. In my opinion, AI has not restricted my creativity in any way but enhanced it. For a long time, my designs have been restricted by my speed and ability, I feel, incorporating AI into the early design stage will only ever produce a better design.”
Figure 1: Four recumbent exercise bike designs generated using Midjourney
Staff Use Case by Becca Ferrari, Deputy Associate Pro-Vice Chancellor for Education and Student Experience
“I have been incorporating Generative Pre-trained Transformers (GPT) into my curriculum design process. By leveraging AI, I’ve developed trained GPT models that align our learning experiences and assessments with Professional, Statutory and Regulatory Bodies (PSRB) learning outcomes, the Sustainable Development Goals (SDGs), and our University of Nottingham Professional Competencies framework, including Student Digital Capabilities. This ensures our curriculum not only meets academic engineering standards but also equips students for graduate employment. Our innovative approach fosters a purpose-driven learning environment and helps develop projects that prepare students to address global challenges. While AI plays a crucial role in this approach, the irreplaceable insight and judgment of human experts is crucial. I am now extending the approach to include the design of assessments and learning activities tailored to diverse student needs. This includes alternative assessment formats for students with support plans, and learning activities that cater to various prior learning experiences and interests.”
Calling all blog volunteers!
Would you like to promote how technology is being used in your faculty? Maybe you have some students who are also keen to share how technology has enhanced their learning experiences. If you are interested in submitting a blog post about your use of technology for teaching and learning, please do get in touch. Find out how to submit a post, or arrange to have a chat about ideas
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