Ethan Dickey proposes six ways to judge AI tools used in computing classes. They cover teaching goals, teacher authority, human responsibility, student choice, limits on use, and how to check results. The framework gives schools a way to ask who stays in charge. It does not measure learning gains in K-12 classrooms.
ShowTellArena tests whether AI tools understand a narrated job demo. Its 50 business tasks and 502 questions probe rules, limits, and exceptions, not just copied steps. The early pilot shows why teams should test understanding before trusting a tool with the job.
On 399 Upworthy headline tests with clear winners, a single AI prediction with no invented reader profiles picked the top choice more often than a panel of ten AI-made readers: 49.2% versus 34.6%. This is a test of click predictions, not a verdict on real interviews.
In a trial with 50 designers and 50 product managers, participants tried three design tasks with or without Figma Make. Among those who finished, tool access was linked to about 20% less time. Managers saw larger gains; the benefit for designers may depend on the task.
Ten doctoral students tried research reviews with and without AI help in a one-day class session. AI made it easier to start and surface choices. It also encouraged shallow checking and too much handoff. This was a class observation, not a controlled test of learning gains.
Interviews with 12 expert data storytellers show a boundary around AI help. They handed off some production work but kept control of the story and its meaning. The work moved toward checking results after setting limits. These interviews do not measure what audiences understood.
