Ethical Deep Dive into AI in Education
Go deep into the ethical dimensions of AI that most PD glosses over — environmental costs (water, energy, carbon), the human labor behind AI (content moderation, data labeling), algorithmic bias in education tools, data sovereignty, and the geopolitics of AI. Develop a personal ethical framework for technology decisions.
What you'll walk away with
Analyse the environmental costs of AI tools (energy, water, carbon) and applies proportional responsibility reasoning
Examine the human labour supply chain behind AI systems and articulates ethical implications for education
Conduct an algorithmic bias audit of educational AI tools using documented cases and equity frameworks
Develop a school-level ethical adoption protocol addressing all six dimensions: environment, labour, bias, privacy, equity, and pedagogy
How a Learning Series works
Work through the snacks — short, evidence-based lessons that build your understanding. Each one gives you research, a classroom connection, and one thing to try or discuss.
Test your understanding in a low-stakes mini-experiment before you bring it to your classroom. Leverage our downloadable materials to support you in your teaching and classroom practice and let us know how it goes!
For credit towards an Accingo Course certificate, try something from this series in your own classroom, then complete a short, structured reflection on what you learned and what you would bring to your PLC and/or regular practice. You will see "Application Artifact" if required.
