Match the practical strategy with the primary AI literacy skill it is designed to cultivate.
The provided list contains mismatches between the practical strategies and their corresponding literacy skills. The correct, accurate pairings are detailed below:
Prompt Engineering: A student always asks follow-up questions to the AI, asking it to refine its answers or provide more detail.
Why it matches: This represents iterative prompting. By structurally directing the machine to narrow its focus, adjust tone, or expand on complex subtopics, the user actively optimizes the output structure through behavioral prompt design.
Preventing Misinformation: A student always fact-checks AI-generated statistics using a reliable external source.
Why it matches: Generative large language models can fabricate convincing data, historical timelines, or mathematical figures. Cross-referencing outputs against verified, primary databases ensures that hallucinations are caught before they spread.
Evaluating for Bias: A student consciously inspects an AI’s depiction of a group of people for accurate representation.
Why it matches: AI models reflect structural patterns embedded in their internet-scale training data. Actively checking generated text or imagery for oversimplified depictions or regional imbalances builds critical assessment skills regarding cultural and algorithmic fairness.