AI Critical Literacy: Understanding Generative AI in University Teaching

Introducing AI Critical Literacy

• 💡 This podcast series, organized by the Teaching for Learning Center at the University of Missouri, focuses on AI critical literacy.
• 🎯 The discussion centers on generative AI and its significant implications for teaching and learning within a university setting.
• 🧠 A core theme is the necessity of skepticism and questioning when engaging with AI tools and their outputs.

The Co-Intelligence Framework

• 📚 The series is guided by Ethan Mollick's book, "Co-Intelligence," which advocates for a human-in-the-loop model.
• 🤝 This model emphasizes that users must always remain in charge and not cede authority to AI systems.
• 👽 AI is conceptualized as an "alien" or "eager intern" that, despite its brilliance, requires mentoring and human guidance.

Defining AI Literacy vs. Critical Literacy

• 🔍 The podcast distinguishes between mere functional AI literacy (knowing how to use AI) and critical AI literacy.
• 🔬 Critical literacy involves interrogating the technology, understanding its limitations, biases, and the underlying systems that create its responses.
• ⚠️ It highlights that AI tools are predicting, not thinking like humans, and can exhibit source reliability issues, bias, and outdated information.

Implications for University Education

• 🚀 Teaching AI critical literacy is crucial for preparing students for a rapidly changing world and future careers.
• ✅ It aims to prevent students from offloading essential practice in critical thinking and skill development to AI.
• 🏛️ Universities should consider how to integrate AI discussions into the curriculum, respecting academic freedom while equipping students.

Practical Teaching Strategies

• 👨‍🏫 Educators can use AI tools like ChatGPT in class to question AI-generated content, turning it into a teaching moment.
• 💬 This approach helps students analyze the accuracy and potential biases of AI outputs, deepening their conceptual understanding.
• 🛠️ The goal is to model and foster critical engagement with AI, rather than passive acceptance of its information.