Teaching Students to Critically Think in the Age of AI
Artificial intelligence has emerged from the edges of traditional education to the center of modern teaching and learning. Similar to the rapid adoption of smartphones, the question is no longer whether learners are using AI; they already are.
This raises the question: What should students learn, and how can they demonstrate deeper understanding when AI is always available?
The conversations around integrating AI into teaching and learning spaces have primarily focused on academic integrity, plagiarism, AI detection, and restricting AI use. While these are valid concerns, the more immediate issue is the need to address AI literacy skills.
While modern learners are quite adept at using large language models, that does not mean they actually understand how to use them. Learners can create polished work that appears to address the content and learning objective, but lack the AI literacy skills needed to identify hallucinations or flawed reasoning. The goal of educators should be to scaffold learners from AI consumers to AI-informed, literate decision-makers.
Educators should shift the narrative from Did the learner use AI to:
• Why was AI used?
• What content did the learner revise, requiring human-facing discernment?
• How can the learner demonstrate they have achieved the learning objective?
These questions evolve the narrative from the final product to the actual cognitive processes.
This is particularly important in online learning. Learning online can make a learner’s thinking less noticeable. Process-based activities and assessments can help address this by requiring students to document their initial thinking, interactions with AI, evaluations, revisions, and reflections.
This requires learners to be AI literate in four distinct areas:
Functional: Can learners use AI effectively?
Critical: Can learners question and verify AI-generated information?
Ethical: Can learners understand appropriate use, privacy, attribution, and responsibility?
Metacognitive: Do learners understand AI’s broader functionalities, while still being capable of producing the same results independently?
Thus, the defining question is no longer:
How do we prevent students from using AI?
The question evolves to:
How do we design instruction to enable learners to think critically when AI is available?
That is the function of discrete AI literacy.
Rebecca Blankenship
About the Author
Rebecca J. Blankenship is an award-winning educator and researcher with over 25 years of teaching experience. Her current research examines the ecologies of meanings as a systems-based, hermeneutic approach to ethics in AI and gen-AI teaching and learning modalities. She is currently an Associate Professor in the College of Education at Florida Agricultural and Mechanical University.