Not all AI does the same work — and not all raises the same concerns
What they do: Follow pre-set rules and decision trees without learning from data
How they work: These systems use "if-then" logic programmed by humans. They don't adapt or learn—they simply execute predetermined instructions.
What they do: Analyze large datasets to identify patterns and make predictions
How they work: These systems are trained on historical data and learn to recognize patterns. They improve accuracy over time but don't create new content—they classify, predict, or recommend based on what they've seen before.
What they do: Generate novel content—text, images, code, audio, video—based on prompts
How they work: Trained on massive datasets, these systems use complex neural networks to produce new content that resembles human-created work. They don't copy—they synthesize patterns to create original outputs.
What they do: Support and enhance human capabilities without replacing human judgment
How they work: These tools act as "cognitive scaffolding"—helping users refine, improve, or access their own work. They enhance what humans do rather than doing it for them.
What they do: Monitor, track, evaluate, or detect student behavior and work
How they work: These systems observe student actions—keystrokes, eye movements, browsing patterns, writing style—and make judgments about authenticity, attention, or compliance. They operate as automated surveillance.