This legislation seeks to ensure that AI transformation is guided by key principles, namely, safety, accountability and trust. We must also ensure that we extend those principles to the transformation AI is having on higher education. AI can, of course, supporting learning but it can also produce essays and other assessments. Now we have AI tools to help us detect AI-generated assessments. This has resulted in a weird arms race, with increasingly sophisticated AI tools to generate assignments and increasingly sophisticated AI tools to try to detect its use. Meanwhile, the impact of AI is threatening the success in widening access to higher education because in many cases, information technology is now being removed from assessments entirely. The only comparator I have to the impact AI is having on higher education is Covid, which resulted in everybody having to work from home and teaching going online. However, there is one key difference. With Covid, there was always the expectation that eventually we would return to campuses and more familiar operations. It is not the same with AI. There is no return to the status quo there. This transformation is permanent. The higher education system is not designed to withstand this level of a systematic shock. It is designed for gradual change and structured review. Assessment methods are regularly evaluated but these are informed by industry, research and engagement with stakeholders before any changes are implemented. This protects academic integrity and ensures students are taught what they need to prepare them for their futures. It is a core element of the design of system. This is not a system designed for overnight transformation. Covid prompted a rapid shift to promote teaching and assessment and staff very much rose to the challenge. They transformed how they worked to support the students they had. However, it required an enormous amount of staff effort and commitment, which were never really fully recognised. It was the same for many other people who had to change how they did things during that time. After only a few years, we are asking those exact same staff to once again transform how they work. Another key difference is that this time we have no established play book. We have no experts out there who have already solved all of these problems. Research is still emerging and best practice is still evolving. Meanwhile, our lecturers and researchers are on the front lines, addressing these challenges in real time on a daily basis. They are redesigning curriculum, rethinking assessment methods, responding to the misuse of AI tools and helping their students navigate AI ethically, all the while maintaining high standards of teaching and research. They are expected to do this in a system that was never designed to be this agile. This is an unrecognised and unacknowledged extra workload and responsibility on our academic staff. This adds to the existing workforce issues, where we have one in five academic staff employed in temporary or short-term contracts. We are now expecting those staff to navigate this changing world of AI and engage in all that adaptation, not knowing if they will have a job next semester or year. If we are serious about ensuring AI is well governed, we need to invest in a national approach to AI in education because there are no ivory towers in a post-AI higher education world. We owe that to our third level staff and students and to the future of our higher education system.

Sentiment score: 0.17