The Conversation published an essay by Professor Christian B. Miller of Wake Forest University arguing that artificial intelligence is training people to expect instant answers and eroding the practice of patience. Miller contrasts past classroom assignments, which required students to patiently gather and synthesize material, with current AI tools that can produce finished outputs for tasks including school assignments, legal writing, sermon preparation, vacation planning, work emails and academic research. The piece defines patience as responding calmly when goals take longer than expected and says the author is concerned about how these shifts may reduce opportunities to develop that capacity. The observations and examples are drawn from Miller's essay in The Conversation.
What happened
The Conversation published an essay by Professor Christian B. Miller of Wake Forest University arguing that artificial intelligence is training people to expect instant answers and thereby eroding the habit of patience. The article contrasts earlier pedagogical practices that required students to search libraries or curate multiple sources with modern AI tools that can produce finished outputs for tasks including school assignments, legal writing, sermon preparation, vacation planning, work emails and academic research. The author defines patience as responding calmly when achieving goals takes longer than desired and writes that he is "especially concerned about what people can do to resist this trend."
Editorial analysis - technical context
Tools that deliver rapid, complete outputs create tight feedback loops that reward immediacy rather than delayed effort. Industry research on human-computer interaction and the attention economy shows similar patterns where shorter latency and immediate reinforcement reduce users' tolerance for waiting and decrease opportunities to practice effortful cognitive tasks. For practitioners, this suggests a technical trade-off: prioritizing lower latency and turnkey responses can improve short-term efficiency while altering the behavioral incentives that support sustained, patient problem solving.
Industry context
Observers tracking UX and education will see this essay as part of a broader conversation about how automation changes skill formation. Companies and platforms have historically shaped user expectations through interface design, and the widespread availability of AI-generated outputs is another vector for that influence. Industry reporting and academic studies on attention, learning outcomes, and long-term behavioral change are the relevant comparator literature for evaluating the claims Miller raises.
What to watch
Researchers and product teams should monitor longitudinal studies that measure tolerance for delay, persistence on multi-step tasks, and changes in educational outcomes as AI assistants become common. Designers and HCI researchers will also track experiments that vary response latency or require incremental user effort to see whether those patterns preserve or rebuild patient behavior. Finally, empirical work that documents causal links between everyday AI use and measures of patience will be needed to move the conversation from plausible mechanism to evidence-based policy or product guidance.
Scoring Rationale #
This is a solid, practitioner-relevant piece about human-AI interaction and behavioral effects. It is not a frontier technical development, but it matters to product design, HCI research, and education policy.
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