Time, AI, and the System Students Are Learning to Hack

Source: Screens & Sanity Substack
Author: Sydney Sullivan
Original source: https://screensandsanity.substack.com/p/time-ai-and-the-system-students-are Published: 2026-04-14
Source type: essay

Private backup: the full article text is archived in the private repository at archives/articles/screensandsanity-substack-com-time-ai-and-the-system-students-are.source.md. It is not published on the public Quartz site.

Summary

Sydney Sullivan argues that when students say AI gives them “time back,” they are revealing both a real pressure and a deeper problem in the education system. Students often use AI to compress academic tasks because school already rewards speed, completion, and efficient production of deliverables. That makes AI use a rational adaptation to institutional incentives, not simply a moral failure. The article’s concern is that learning also requires time: slowness, attention, revision, struggle, and intellectual risk-taking. AI therefore forces schools to ask what kind of relationship to time they are cultivating.

Pull quotes

AI as adaptation

“From this perspective, AI becomes less about avoidance and more about adaptation.”

— Sullivan, framing student AI use as a response to workload pressure.

Optimizing for institutional incentives

“Rather than resisting institutional expectations, they are optimizing for them.”

— Sullivan, arguing that students respond to the incentives schools create.

Technology amplifies existing values

“In that sense, the technology amplifies existing values rather than creating entirely new ones.”

— Sullivan, cautioning that AI reflects the surrounding educational system.

Time, slowness, and learning

“Or are we creating space for forms of learning that require slowness, reflection, and intellectual risk-taking?”

— Sullivan, naming the deeper design question for schools.

Big ideas

Claims

Key evidence and examples

  • Sullivan notes that students often describe AI as giving them time back by compressing reading, outlining, drafting, and other academic tasks.
  • She argues this behavior is rational in systems that reward speed, completion, measurable output, and productivity.
  • The article warns that effortful learning requires time for struggle, revision, sustained attention, and risk-taking.
  • Sullivan reframes student AI use as evidence about institutional incentives rather than only as a problem of individual academic integrity.

Education relevance

High relevance for AI-era assignment design, student incentives, productive struggle, higher-education systems, and interpreting student AI use as a symptom of output-focused schooling.

Durability note

The post is tied to a specific moment in AI adoption, but its institutional question is durable: student AI use often reflects what school systems reward, not only what individual students value.

My notes