AI Literacy

I am currently engaged in operationalizing proposed AI competency frameworks, and developing AI literacy courses for graduate learners whose disciplinary backgrounds, research trajectories, and professional futures are radically diverse.

“AI literacy” has rapidly become an educational priority, but current AI literacy frameworks predominantly target K-12, undergraduate and ‘wider public’ audiences (Almatrafi et al., 2024), leaving graduate and professional learners largely unsupported. Yet, this population urgently needs a critically informed AI literacy to navigate the full research lifecycle, uphold scholarly integrity, and communicate complex ideas authoritatively as they enter their disciplinary communities of practice. At the same time, AI literacy risks being reduced to a checklist of functional competencies, such as prompt engineering or spotting hallucinations. Instead I draw on Street’s (1984) foundational distinction between autonomous and ideological models of literacy, and argue against treating AI literacy as a set of context-neutral micro-skills. Instead, I argue for a richer framing of AI literacy as a complex social, epistemic, and ethical practice.

Drawing on the Scaffolded AI Literacy (SAIL) framework (MacCallum et al., 2024; MacCallum et al., 2026) I hope to address the full complexity of graduate-level AI engagement  – including AI across the research lifecycle, applied disciplinary ethics, risks to scholarly integrity, environmental and cultural dimensions, and development of personal AI learning plans. I am also borrowing from Horst’s (2025) ‘entangled dimensions’ framing, and the competency and assessment literature (Annapureddy et al., 2025; Jin et al., 2025; Long & Magerko, 2020). I am adopting a deliberate ‘AI without tears’ pedagogical stance, with the goal of developing learning experiences that foreground metacognitive awareness, disciplinary specificity, and evaluative judgment alongside practical skills.