I really like Dr. Phil Hardman. Here’s a recent article on what bad things happen if we resist AI.
The invisible cost of resisting AI in higher education | LSE Higher Education
The rise of AI presents the very real risk that universities will become irrelevant – or even obsolete – if they resist it. Philippa Hardman explores how HE might avoid that fate.
blogs.lse.ac.uk
And here’s a summary in my browser using WordTune.AI
The invisible cost of resisting AI in higher education | LSE Higher Education
— A new set of principles was published by the Russell Group on 4 July to help students and staff be ‘AI literate’ in higher education and an increasingly AI-enabled world.
— The implications of generative AI for higher education are perhaps more significant than we realise, with some suggesting that it could be the end of higher education.
The threat of AI – and AI detection
— As a result of this crisis, some institutions have returned to in-classroom examinations, while others have invested in new AI detection technologies such as GPTZero and TurnitIn AI. Both responses are problematic.
— Although AI detection technologies may seem initially appealing to institutions that already use technology to detect and manage plagiarism, early research shows that there is no such thing as perfect AI detection. Even small changes to the text can break detectors, and AI is biased towards non-native English writers.
Reversing the risk of irrelevance
— Higher education should promote the use of ChatGPT to equip students with the skills they need to meet ever changing labour markets.
— Inquiry-based objectives focus on the development of skills and behaviours, such as critical thinking, problem-solving, collaboration, and research skills, by requiring learners to construct their own understanding.
— Students’ interactions with AI should be assessed in order to develop their domain expertise and to be critical consumers of AI.
— Once we have IBOs, we can shift from a lecture plus essay model to a project plus problem set model. This improves motivation, knowledge acquisition, and skill development because learners learn by doing and discovering, not sitting and listening.
— Step 3: design performance-based assessments To gauge learner success in an inquiry-based approach, create a comprehensive mark scheme that considers not only knowledge acquisition but also the skills demonstrated during the inquiry process.
— In the post-AI classroom, skills assessment includes the ability to use ChatGPT to explore and understand the topic, knowledge assessment includes identifying any misconceptions generated from ChatGPT “hallucinations”, and process assessment includes prompting and critique.
Equipping students for the future
— AI and machine learning skills, compounded with analytical thinking, evidence-based argumentation, and communication skills, are predicted to be the most high-value, in-demand skills within the next six months. This system of teaching and assessment outperforms the existing system in two ways.
— If universities want to remain relevant and purposeful, they must embrace AI and invest in the necessary training for both faculty and students to adapt to a rapidly changing post-AI world.
John Weisenfeld 