- Dates: Asynchronous online: June 15-27, 2025 (10 hours | In-person: July 2-5 July, 2025 (approx. 29 hours: 6 hrs per day, plus 1-2 plenary sessions)
- Instructors: Dr. Teresa Dobson, Dr. Jen Jenson, Dr. Sam McCready
- Location: Virtual (synchronous and asynchronous) and in-person on the UBC Vancouver campus
- Please note: students must have already completed ETEC 500 to take a MET Summer Institute
Image Credit: Selman Designs: MIT Technology Review
Intelligence has always been an artificial construct, and no more so than when being used to refer to what is now broadly being defined and organized as “artificial intelligence” (AI). First coined by John McCarthy in the 1950s, AI refers to a machine or algorithm that mimics human intelligence. Machines can, sort of, mimic human intelligence, but not without a whole lot of human input (think meta and training data). In fact, so much human input is used in these “artificially intelligent” systems, that Kate Crawford convincingly argues such systems are “neither artificial nor intelligent,” and comments extensively on wider social structures and systems of power that are in flux as AI encroaches upon and disrupts existing political, economic, social, and educational frameworks (Crawford, 2021).
In 2025, few people will not have heard of generative AI (Gen AI) and its uses for creating content (Chat GPT, Co-Pilot, Gemini, images [Dalle-E], and music [AVIA], for example). In this course, we will explore Gen AI from the form it is in as of June/July 2025: its application/s, uses, biases, and blind spots. Taking an exploratory approach, we will examine how GenAI is used in our everyday realities, whether at home, school, or work, with a view to understanding the affordances and limitations of GenAI for learning and teaching. GenAI is already coming to dominate information and media spaces – our task is to understand what this means now and in the near future.
The deeply problematic positive vision of AI futures for education (or automation, or institutional efficiency) are at this point well-rehearsed and offer little in the way of a critical point of entry aimed at better understanding what GenAI does, what it costs, and why it matters. To address this, we will investigate the sustainability of AI, including the costs to power a single search, the resources it takes to maintain AI, as well as the costs in human suffering to “train” the data. Readings will include general and politically nuanced accounts of “AI”, as well as specific readings on its use/s in education, medicine, and business. Students will be invited to explore potential applications, challenges and ethical considerations of using Gen AI in educational contexts, as well as the wider technological, ethical, social, and political implications of Gen AI use.