We need a taxonomy that treats AI not as an optional bolt-on, but as a collaborator requiring students to develop new literacies—deep knowledge, interpretation, prompt-crafting, critical scrutiny, evaluation, and co-creation—while keeping human expertise firmly in the driver’s seat.
1. Knowing (Instead of Remembering)
Building an active, accurate knowledge base and understanding AI’s limits.
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What it is: Mastery of domain facts, concepts, and context—plus knowing where AI typically makes confident but incorrect assertions.
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Why it matters: Without a robust foundation (e.g., planetary radiation levels, biochemical pathways), you can’t recognize AI’s mistakes.
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Strategies for the classroom:
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Annotated source‐checks: Students document and verify every fact AI suggests.
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Concept maps: Visually link key ideas and highlight gaps AI may overlook.
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Mini‐debates: Teams challenge AI-generated claims, forcing deep recall and justification.
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2. Interpreting (Instead of Understanding)
Decoding both subject matter and how AI “thinks.”
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What it is: Grasping concepts well enough to translate, explain, and spot where AI’s pattern-based responses diverge from true reasoning.
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Why it matters: You must see not only what AI says, but how it arrives there—and anticipate its blind spots.
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Classroom moves:
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Ask students to paraphrase AI outputs in their own words, noting any leaps or missing premises.
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Have them diagram AI’s reasoning flowcharts and identify unstated assumptions.
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3. Applying with AI
Using knowledge and AI tools together—broken into two complementary sub-skills.
a. Prompt Crafting
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What it is: The art of writing precise, targeted prompts that steer AI toward useful, accurate answers.
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Why it matters: A vague prompt yields junk. Effective prompting is a technical skill that underpins every AI interaction.
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Classroom moves:
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Prompt‐revision workshops: students iteratively refine queries and compare outputs.
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Prompt templates: “Explain X under constraint Y,” “Compare A and B with respect to C.”
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b. Applying AI Outputs
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What it is: Integrating AI’s suggestions into real-world or project contexts, then refining them with human judgment.
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Why it matters: AI can draft a lab protocol or design outline—but only you can adapt it to your specific data, constraints, and goals.
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Classroom moves:
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Case studies: use an AI-generated draft, then annotate where you’d modify steps based on local variables.
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Peer review: students swap AI-assisted work and suggest domain-specific tweaks.
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4. Decomposing AI Arguments (Instead of Analyzing with AI)
Holding AI accountable by breaking down its claims.
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What it is: Dissecting AI’s output into individual assertions, evidence, assumptions, and logical steps.
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Why it matters: True analysis means finding gaps—e.g., AI may list abiotic pathways but ignore radiation constraints.
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Classroom moves:
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Claim–evidence charts: isolate each AI claim and flag missing or weak support.
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Bias checklists: identify where training data or algorithms may skew results.
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5. Evaluating with AI
Judging the validity, reliability, and relevance of AI’s work.
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What it is: Applying a clear rubric to decide when to trust, modify, or reject AI outputs.
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Why it matters: AI is fallible and overconfident; sound judgment rests on solid criteria.
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Evaluation rubric:
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Source validity: Are references credible and current?
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Logical consistency: Do steps follow rigorously from premises?
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Bias detection: Has the model introduced skew or omitted viewpoints?
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Classroom moves:
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Rubric-based reviews: score AI responses and justify each rating.
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Counter-example exercises: find real cases where AI fails each rubric criterion.
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6. Creating with AI
Co-creating original work by blending AI’s strengths with your expertise.
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What it is: Producing projects—essays, simulations, designs—where AI’s output is only the starting point, refined and directed by your domain knowledge.
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Why it matters: AI can generate ideas en masse, but only you ensure they’re accurate, nuanced, and ethically sound.
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Mini-case narrative:
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Draft hypothesis: A student proposes an abiotic pathway on Europa.
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AI simulation: They prompt a model to generate reaction conditions.
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Refinement: The student adjusts parameters (radiation dose, temperature range).
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Critique: They assess AI’s assumptions—e.g., neglect of ice chemistry—and revise the final model.
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