Finally, Part 3 of my conference round up presents some good practice, thoughts on careers, and reflections on AI overall.
Avoiding AI-generated student writing
A joyful presentation from PennState showcased some solutions to students’ engagement with AI (H. McCune Bruhn, Art History, USA). We were presented with fun metaphors on how to get students thinking about the drawbacks of AI, noting how AI is drawing on a poisoned well of terrible writing in the field of AI. The speaker referred to the concept of a “buzzword smoothie” and a “garbage in: garbage” out situation here. To address problems, some preliminary solutions were suggested:
- Eliminate “busy work” or the at least the perception of busy work.
- Create assessments that target higher levels of complexity (higher level of Bloom’s Taxonomy).
- Explain the learning objectives clearly.
- Articulate the real-life benefits of each assessment more clearly.
- Create checklists for assignments that include tips to encourage effective writing.
- Create a plagiarism quiz that incorporates AI policy, and include a syllabus statement on AI.
- Adapt rubrics to target bad writing.
The lecturer also created a 15-minute video packed full of tips for students, part of pre-engagement activities before their classes began. It starts with a question “if AI can do it, why should I bother?”. Lots of interdisciplinary reasons for engagement with meaningful learning activities (rather than relying on AI) are presented. For example, there are benefits of looking at art – it makes you happy, increases blood flow by 10%, and increases our happy hormones. It leads to empathy too, especially when looking at art that’s new and from different cultural backgrounds. Neurological advantages of learning are also showcases – like how everything you learn creates new connection in your brain, increasing neuroplasticity. All this is aimed at getting students to reflect on their role as active participants in their learning.
AI & Design skills
Another talk from colleagues in the USA (A. Bridges, C. Blue, Clemson University) considered how AI can be integrated into the design process. When AI is introduced into the ideation process, and design is treated as problem-solving, students can develop a range of industry-ready skills for an AI-engaged world. There were two starting points: (1) Use in-built generative AI (gen-AI) tools in photoshop to create a postcard design in a time-constrained 30 minute session; (2) Use gen-AI images to develop and prepare workflows using Adobe Illustrator. For the lecturers, advancement in AI in the design world demands a complete recalibration of professional skills and the development of ethical guidance to ensure AI’s potential is harnessed responsibly. A great quote sums up how important AI skills are considered in their context: “AI won’t take your job, the person who learns how to use it properly will”. Student evaluation showed how learners know they need these skills immediately to meet their fast-paced industry.
AI & Workforce changes
Prof B. Barrett of the American University spoke about the world his HR students are entering, one where they have to know the importance of AI prompt engineering in addition to all the ethical concerns commonly discussed around AI. The role of human judgement won’t disappear, but AI skills will be essential and are a current expectation. He referenced the edX/Workplace Intelligence 2023 survey that included over 500 CEOs, showing 59% of people are using AI at work right now, and 85% are expected to use it within the next 5 years. For many, there assumption is a workforce powered by AI.
Reflections
Reflecting on my learnings from the conference, I can really see the potential in adapting approaches for practice from across the world as well as considering how we research our curriculum and staff student views in the future. What might be the most beneficial is to share these insights with our students directly. By placing their learning experience into the global context, they can benchmark their experiences and perspectives with international peers, and reflect on their own behaviours and use of AI.
The papers also offer several provocations on the nature of education itself. For example, many papers dealt with how LLMs can write academic papers for us. Just because something can write for you and save some time, doesn’t mean we should use it. I would argue that the process of writing itself is a thinking process. Should we risk losing not only the skills of writing, but also the benefit of how it transforms how we express ourselves. How will AI transform not only what a university does, but how a university education is valued? Should we be lead by emerging social and employer norms, or should we instead prioritise value emerging from teaching and learning perspectives? We might use AI to improve efficiency but what do we lose in the process? Given all the Intellectual property and environmental issues of using AI, should we even encourage any use at all?
Comment below with your experiences of AI and any feedback from your students? How is education and the meaning of education changing for you? We would also love to hear from you about what the university should or could be doing next.
I will be sharing results from our own research with Bristol students in the very new future, so stay tuned!
