To teach in the time of ChatGPT is to know pain

2026-04-13 · Source: AI - Ars Technica · Field: Education & Learning — Educational Technology (EdTech), Online Learning & Digital Education, Academic Research & Higher Education · Depth: Fundamental Awareness, long

Summary

A college Earth science instructor details the profound negative impact of generative AI, specifically large language models (LLMs) like ChatGPT, on asynchronous online education since 2023. The instructor, Scott K. Johnson, highlights how LLMs transform the teaching role into one of "detective and prosecutor," consuming significant time in adjudicating nuanced cases of AI-assisted student work. A College Board survey of 600 high school students revealed 84% used generative AI for schoolwork. The article explains that LLMs undermine the learning process by removing the "friction" of effort, turning formative assessments into wasted exercises, and making traditional writing assignments untenable. For example, a critical thinking question that previously saw a one-in-three success rate now sees over half, with answers frequently containing LLM-generated terms. This situation disproportionately affects online courses, which are vital for diverse student populations, and forces pedagogical compromises even in in-person settings.

Key takeaway

For educators, particularly those in online or hybrid learning environments, you must critically reassess assignment design and assessment strategies. The pervasive use of LLMs by students necessitates a shift away from easily automated tasks towards methods that demand genuine critical thought and cannot be outsourced to AI, even if it means sacrificing some traditional assignment types. Be prepared for increased time spent on academic integrity investigations and consider the pedagogical trade-offs of AI-proof assessments.

Key insights

Generative AI fundamentally undermines educational integrity and the learning process by eliminating necessary intellectual effort.

Principles

• Friction is essential for learning.
• Formative assessment requires genuine student effort.
• LLMs complicate academic integrity beyond simple plagiarism.

In practice

• Re-evaluate writing assignments for LLM susceptibility.
• Consider oral exams or supervised handwritten work.
• Acknowledge the increased burden on instructors for AI detection.

Topics

• Generative AI in Education
• Academic Dishonesty
• Large Language Models
• Critical Thinking Erosion
• Online Course Challenges

Best for: Domain Expert, Policy Maker, General Interest

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI - Ars Technica.