This is excellent from Helen Beetham, part of a longer essay about the harms of AI in education:
Repurpose AI technologies, where possible, for projects of authentic learning and human flourishing e.g.:
- Develop small scale models using open data and APIs, publicly or community owned, that meet real local needs for learning and cultural expression;
- Design curricula that support inquiry into AI, its inequities and harms and wider contexts as well as its opportunities for use;
- Deliver learning activities with/without AI that reveal its fragilities, fabrications and breaking points:
- Teach and actively support counter-hegemonic data projects: feminist, decolonial, minority-cultural, public and sustainable projects, projects of data sovereignty and data justice;
- Undertake independent research into the social, cultural, developmental and longitudinal impacts of AI in education;
- Encourage slow scholarship and slow learning with AI, refusing speed and productivity to focus on quality and interpretability;
- In all of these activities, refuse anthropomorphism: describe precisely how models produce their outputs and how human agency is involved;
- Be alert to the constraints and contradictions that become visible in these repurposings: notice, teach, discuss and make known the mechanisms and meanings of AI
