Beyond the Tool: How Educational, Economic, and Power Structures Shape AI Adoption in Learning
Original framing: “Should you use AI for a task? Here’s a simple way to decide | Bruce Schneier” — The Guardian - Technology
Non‑Western case studies—from Indian public‑funded AI labs to African language‑centric tools—show that alternative governance models can mitigate corporate capture and enhance cultural relevance. These examples illustrate that AI is not a uniform Western export but a contested field shaped by local values and state strategies. The original piece ignores these comparative insights.
The decision to use AI in academia is less a matter of individual preference than a convergence of neoliberal funding, corporate data extraction, and unequal access that reproduces historic patterns of technological disruption.
Cross‑cultural evidence shows that alternative governance models—publicly funded, open‑source, and community‑driven—can break this cycle, while indigenous and artistic perspectives remind us that learning is a relational, embodied practice. By foregrounding marginalised voices, applying rigorous scientific scrutiny, and employing a trickster lens to expose paradoxes, we can design AI policies that safeguard equity, preserve critical thinking, and align with long‑term societal wellbeing.