This course is actively under development in 2026. Check back later for additional details.
Description
‘AI literacy’ has emerged rapidly as an educational priority, even as the term itself conceals significant theoretical tensions. The dominant framing of AI literacy as a bundle of functional competencies (Can you prompt an LLM? Can you spot a hallucination?) reduces a complex social, epistemic, and ethical phenomenon to a set of trainable micro-skills. Meanwhile, graduate students, early-career researchers and educators are, we argue, the population for whom a richer, critically-informed AI literacy matters most. They must navigate AI not just as a productivity tool but as a technology with material implications across every stage of the research and teaching lifecycle. These learners and practitioners belong to communities of practice where the stakes of uncritical AI adoption are considerably higher than in earlier educational contexts. And as the scholars, scientists, and educators of the future, graduate learners and researchers will be called upon to communicate complex and contested ideas accurately to disciplinary peers and broader publics alike. A reductive, skills-only AI literacy leaves them unequipped for that task. What is needed is a richer framing: one that prepares graduate and professional learners not only to use AI tools effectively, but to reason about them critically, evaluate them ethically, and communicate about them with clarity and authority.
This graduate-level AI literacy course takes the position that AI literacy is best understood as social and critical practice rather than a checklist of functional competencies. Beginning with the Scaffolded AI Literacy (SAIL) framework (MacCallum et al., 2024; MacCallum et al., 2026) as an organizational spine, it addresses the full complexity of graduate and professional AI engagement – including AI across the research lifecycle, applied disciplinary ethics, risks to scholarly integrity, environmental and cultural dimensions, and development of a capstone personal AI learning plan. Drawing additionally on Horst’s (2025) ‘entangled dimensions’ framing, the competency and assessment literature (Annapureddy et al., 2025; Jin et al., 2025; Long & Magerko, 2020), and a deliberate ‘AI without tears’ pedagogical stance, the course promotes metacognitive awareness, contextual specificity, and evaluative judgment alongside practical skills.
Learning Objectives
After completing this course, you will be able to:
- Explain core AI concepts, tools, and limitations in terms accessible to your peers and colleagues – including how AI systems learn, the distinction between discriminative and generative AI, the current landscape of AI capabilities (such as multimodality, retrieval-augmented generation, and AI agents), and fundamental challenges to AI reliability and trustworthiness.
- Critically analyze how AI is reshaping cognition, society, and the environment – examining impacts on learning and professional practice; the entry points for bias in AI systems and competing definitions of fairness; environmental costs and sustainability trade-offs; and effects on cultural diversity and Indigenous communities – and evaluate how these issues manifest within your own context.
- Evaluate AI risks and apply ethical reasoning to real-world AI dilemmas – identifying principal categories of risk (including privacy, security, intellectual property, and broader societal concerns), assessing current governance and mitigation strategies, comparing major ethical frameworks (principles-based, consequentialist, virtue-based, care, and justice-oriented), and articulating a reasoned, discipline-specific position on when and how to engage with – or refuse – AI systems.
- Use AI tools effectively and critically across scholarly and professional contexts – applying effective prompting and tool-selection strategies, integrating AI responsibly across the research lifecycle with transparent disclosure, and making reasoned judgments about the quality, credibility, and limits of AI-generated outputs by drawing on human cognitive strengths including metacognitive awareness and evaluative reasoning.
- Assess your own AI literacy and develop an evidence-based plan for ongoing learning – using validated instruments to evaluate your competencies, synthesizing course learning into a justified personal AI learning plan for your disciplinary context, and articulating strategies for continuing development as the technology evolves.