This course is actively under development in 2026. Check back soon for additional details.
Description
Intelligence has always been an artificial construct, and no more so than when being used to refer to what is now broadly defined and organized as “artificial intelligence” (AI). First coined by John McCarthy in the 1950s, AI refers to a machine or algorithm that simulates 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 2026, few people in industrialized nations 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 July 2026: 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.
Positive visions of AI futures for education (or automation, or institutional efficiency) that are often uncritical are at this point well-rehearsed and offer little in the way of a point of entry aimed at better understanding what GenAI does, what it costs, and why it matters. In this course, we will take a critical view, examining the sustainability of AI, including the costs to power a single search, the resources it takes to maintain AI, as well as the human costs of “training” GenAI. Readings will include general and politically nuanced accounts of “AI”, as well as specific readings on its use/s in education, medicine, and business. You 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.
Course Objectives and Learning Outcomes:
- Trace the evolution of AI research paradigms: YOU will analyze the major shifts in AI research methodologies and philosophical approaches, identifying key influences and turning points, including deconstruction of the “AI hype cycle” by critically examining the history of AI, differentiating between actual advancements and overblown promises, identifying key turning points and influential figures.
- Build an understanding of algorithms: You will explore the fundamental concepts of algorithms as the sets of rules and instructions that power AI systems. They will investigate how these algorithms process data, make decisions, and produce outputs, understanding their crucial role in shaping the results of AI applications in decision making in real world settings such as health care, finance, and criminal justice). You will learn to identify the types of algorithms used and the potential consequences of that use.
- Evaluate the epistemological implications of AI: You will investigate how AI impacts knowledge creation, dissemination, and validation, considering its effects on trust, expertise, and evidence-based decision-making, especially in relation to education.
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Consider the environmental and resource footprint of AI: You will read about the environmental impact of AI technologies throughout their life cycle (manufacturing, operation, disposal), and resource consumption.
- Design strategies for mitigating algorithmic bias: You will develop and evaluate methods for identifying, preventing, and mitigating biases within AI algorithms and systems, considering both technical solutions and broader societal interventions.
- Explore pedagogical implications of integrating AI in education: You will go beyond merely using tools to fundamentally redesign learning experiences informed by AI’s potential and limitations.
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Consider pedagogical applications of AI, beyond content generation: You will examine innovative applications of AI in educational settings that go beyond content creation, such as AI-assisted design thinking for curriculum development or AI-driven tools for personalized learning experiences, to understand their potential advantages and challenges in enhancing teaching and learning.
Sample Readings
- Baron, N. S. (2026). Reader bot: What happens when AI reads and why it matters. Stanford University Press.
- Bender, E. M., & Hanna, A. (2025). The AI con: How to fight Big Tech’s hype and create the future we want. HarperCollins.
- Bucher, T. (2025). Beyond the hype: Reframing AI through algorithms and culture. Journal of Communication, 75(1), 81-84.
- Coleman, B. (2021). Technology of The Surround. Catalyst: Feminism, Theory, Technoscience, 7(2), 1–21.
- Crawford, K. (2021). Atlas of AI: Power, Politics and the Planetary Costs of Artificial Intelligence. Yale University Press.
- Hao, K. (2025). Empire of AI: Dreams and nightmares in Sam Altman’s OpenAI. Penguin Press.
- Horvath, J. C. (2025). The digital delusion: How classroom technology harms our kids’ learning—and how to help them thrive again. LME Global.
- Livingstone, V., & Stricker, J. K. (2025, June 13). The disappearance of the unclear question. UNESCO. https://www.unesco.org/en/articles/disappearance-unclear-question
- Suchman, L. (2023). The uncontroversial ‘thingness’ of AI. Big Data & Society, 10(2) https://doi.org/10.1177/20539517231206794
- Vallor, S. (2024). The AI mirror: How to reclaim our humanity in an age of machine thinking. Oxford University Press.
- Winterson, J. (2021). 12 Bytes: How Artificial Intelligence will change the way we live and love. Vintage