Lesson
Critical Thinking in the AI Age
Emerging EdTech Trends · Technology
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A current, focused talk grounded in an active research programme. It gives faculty a practical design test for AI use: does the workflow deepen the learner’s contact with evidence and responsibility for judgement?
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Summary, mindmap and highlights
Sarkar opens with an AI-assisted workday: summarised email, generated replies, drafted reports, automated analysis and instant prototypes. Every task gets completed, yet the worker spends little time with the material. He calls this pattern “outsourced reason”: the person increasingly validates machine output and spends less time forming a view through reading, writing and analysis.
He surveys four possible costs. AI-assisted groups can converge on a narrower range of ideas. Workers report less critical-thinking effort when they trust AI more than their own judgement. People may remember less when a system performs the reading or writing. The workflow also creates a demanding management layer because the user must define the goal, divide the task and verify the result.
His alternative is an AI tool for thought. The prototype keeps the user inside the source material, supports strategic close reading, offers critiques and counterarguments, and lets the user build an argument outline. Generated prose follows those substantive decisions.
The practical principles are to preserve direct engagement, introduce useful resistance and prompt reflection on reasoning. A classroom application could ask students to compare sources, state assumptions, build an argument, answer counterexamples and explain which AI suggestions they rejected.
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Outsourced reason describes a workflow where the person mainly approves completed machine output.
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The research concerns cover idea diversity, critical-thinking effort, memory and metacognition.
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A tool for thought keeps the user directly engaged with source material.
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Contextual critiques and counterarguments can stimulate judgement even when the user rejects them.
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Generation follows the user’s decisions about evidence, structure and purpose.
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The three design principles are material engagement, productive resistance and metacognitive scaffolding.
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Additional resources
Explore this carefully selected material to deepen your understanding of the topic and connect it with your teaching practice.
Microsoft Research
The impact of generative AI on critical thinking
The CHI 2025 survey behind a central claim, including its sample, method and limitations.
Science Advances
Generative AI enhances individual creativity but reduces collective diversity
An experiment that found individual gains alongside greater similarity across short stories.
Microsoft Research
AI as a provocateur
Sarkar’s design argument for AI systems that provoke reflection and judgement.
UNESCO
AI competency framework for teachers
Teacher competencies across human agency, ethics, AI foundations, pedagogy and professional learning.
UNESCO
Guidance for generative AI in education and research
Human-centred guidance on privacy, equity, policy and pedagogical design.