We have noted that in a 2025 survey, over 70% of programming teachers explicitly stated: students' main help-seeking method has shifted from browsing documentation to asking ChatGPT (an AI conversational tool), and the core teaching proposition must change—from "teaching how to write" to "teaching how to judge whether what is written is actually correct."
What this is
A recent article aggregating reflections from multiple Web development educators points to three observations.
The first is behavioral change. Over 70% of teachers observed students turning to AI as the primary help channel, with traditional documentation lookups falling below 30%. Students' first reaction to an error has shifted from "check MDN" (a well-known technical documentation site in the programming field) to "ask ChatGPT."
The second is divided evaluation. Supporters see efficiency gains: "Previously, one class covering flex layouts (a webpage styling approach) had students spending half the time debugging. Now two classes can complete a responsive page." Opponents see the cost: many students submit work that "looks fine," but the moment they are asked to explain the core logic, they stall. One instructor asked three students to describe their solution approach for the same problem, and two gave highly consistent answers—despite the problem involving rather niche technical details. The instructor judged: "That kind of consistency is unlikely to be a coincidence."
The article's structural judgment: when AI can stably output "above the passing threshold" code, the teacher's core task is no longer to help them "write faster," but to let students judge whether that code actually passes.
Industry view
Supporters accept the structural claim that "teachers who use AI will replace those who do not," believing instructors can move from "teaching how to write" to the higher-order "teaching why it is written this way."
But the opposing view deserves more attention. The article notes that AI is not omnipotent—it produces factual errors or amplifies information bias in certain scenarios. If teachers themselves do not upgrade their understanding of AI, the so-called "teaching transformation" may simply be outsourcing judgment to the tool.
A deeper risk: judgment cannot be acquired through memorization, only built through the repeated process of reviewing what AI produces. Skip that process, and what looks learned is essentially still unlearned.
Impact on regular people
For enterprise IT: changes in programming education will affect the Web developer talent pipeline. When evaluating candidates, "clearly explaining why the code is written this way" may matter more than "delivering a feature that appears to work."
For individual careers: this has reference value for all knowledge workers. When AI can deliver "above the passing threshold" output, individual value lies not in "generating," but in "judging."
For the consumer market: if online programming courses and bootcamps continue to anchor their curricula around "teaching syntax" and "teaching APIs," they may accelerate their loss of market share. Courses that shift their focus to "teaching judgment" are closer to the next phase of market demand.