AI Literacy: Understanding our Relationship with AI (Part 2)

Rethinking AI Literacy in Schools

In the last blog post, we discussed AI literacy as being, in part, a relationship with AI. Now, we would like to consider how this informs how we incorporate AI literacy in schools. Whether or not students are using AI in schools or at home (70% reported using artificial intelligence in the 2023-24 school year, up from 58% in the previous year), as a learning community, we should be teaching students how to thrive in a progressively AI-infused world. With the advent of AI, what it means to be “college and career ready” is inherently changing.

In an era where artificial intelligence is reshaping industries faster than we can update textbooks, how do we ensure our students are truly future-ready?

The answer lies not solely in teaching students to use AI tools (this is also important) but also in developing the critical human skills that will allow them to thrive alongside rapidly evolving technology. Rather than a static, skill-based approach that focuses solely on technical know-how, AI literacy should be seen as a dynamic relationship—one that students must learn to navigate, develop, and refine over time. This means moving beyond traditional AI instruction and embracing progressively student-driven models where learners engage in self-reflection, metacognitive growth, and trial-and-error to develop a nuanced understanding of AI.

Let's explore two skills that nurture intentional, ethical, and adaptable learners.

Metacognition: The processes involved when learners plan, monitor, evaluate, and make changes to their learning behaviours.

In a world where information is abundant, teaching students to reflect on their own learning processes is crucial. Without metacognitive skills, students risk becoming overly dependent on AI without questioning its outputs or recognizing its limitations. Encourage practices like:

“Without metacognitive skills, students risk becoming overly dependent on AI without questioning its outputs or recognizing its limitations”

• Keeping learning journals

• Error analysis: Encourage students to analyze their mistakes on assignments and assessments to identify areas for improvement.

• Self-questioning techniques: Teach students to ask themselves probing questions like "How does this relate to what I already know?" or "What strategy worked well here?"

• Metacognitive discussions: Facilitate class discussions about learning strategies, challenges, and breakthroughs to normalize metacognitive thinking

• Goal-setting exercises: Help students set specific, measurable learning goals and regularly assess their progress.

• Regularly asking, "How did AI tools help or hinder my understanding?"

Hint: Each of these practices will be hard at first. But the more you do them, the better students will get. Develop what we like to call a “metacognitive culture” or a culture that loves to think out loud, question out loud, and debate out loud.

Why Metacognition is Essential in AI Relationships

Metacognition—the ability to think about one’s own thinking—ensures that students remain in control of their relationship with AI. It helps them recognize when they are using AI productively versus when they may be relying on it uncritically. For example, a student who reflects on their AI interactions might ask:

• Did I verify this AI-generated information before accepting it as fact?

• Am I using AI to enhance my thinking, or am I allowing it to replace my own reasoning?

• How has AI influenced my perception of this topic?

By strengthening metacognitive skills, students become more self-directed learners who can assess the role AI plays in their thinking, avoid cognitive shortcuts that may limit their learning, and develop the skills to interact with AI in an ethical and intentional manner.

Inquiry: The importance of inquiry in the age of AI cannot be overstated.

As AI tools become more prevalent, developing students' inquiry skills is crucial for several reasons:

1. Critical thinking: Inquiry-based learning encourages students to question, analyze, and evaluate information, rather than passively consuming AI-generated content.

2. Adaptability: By fostering curiosity and problem-solving skills, inquiry prepares students for a rapidly changing technological landscape.

3. Ethical engagement: Students learn to critically assess AI outputs, understand biases, and use AI tools responsibly.

4. Deep understanding: Inquiry promotes a deeper grasp of concepts, going beyond surface-level information that AI can easily provide.

We will be diving more into inquiry in future blog posts!

Why Inquiry Matters in AI Literacy

Inquiry fuels curiosity and a deeper understanding of how AI operates, rather than just how to use it. Encouraging students to ask questions such as “How does this AI tool generate responses?”, “What data was used to train this AI?”, and “What biases might exist in this AI model?” allows them to develop a habit of critical questioning. This approach ensures that students are not only engaging with AI but also challenging and investigating its outputs rather than blindly accepting them. Inquiry-based learning fosters a growth mindset that prepares students to adapt to AI advancements and innovate with AI rather than be led by it.