Accumulation of cognitive debt when using an AI assistant for essay writing task

Cognitive debt and decline

• Many see the results as confirming an intuitive idea: outsourcing thinking to LLMs weakens the neural and cognitive processes you’d otherwise exercise, leading to “cognitive debt” and potentially long‑term decline in critical thinking, creativity, and depth.
• Others argue this is just cognitive offloading, similar to relying on tools in any domain; the real risk comes when people stop doing any hard thinking at all and merely rubber‑stamp AI output.

Programming, system knowledge, and workplace incentives

• Several extrapolate the results to coding: relying heavily on code assistants may erode understanding of codebases, mental models of systems, and ability to debug or extend complex software.
• There’s concern that management optimizes for short‑term productivity, not long‑term expertise, quality, or system stability, and that juniors who learn “through the AI” will never build deep skills.
• Some practitioners say LLMs are huge productivity boosts for experienced engineers but harmful for learning or for non‑experts who can’t evaluate output.

Writing as thinking

• A major thread: writing isn’t just output, it’s the process by which we structure thoughts, build mental models, and test understanding.
• If an LLM generates the essay, the writer often has low ownership and can’t recall or explain it; this is seen as evidence that the thinking never happened.
• Many recommend: draft and reason yourself, then use AI for polishing, shortening, grammar, or critique—not for first‑pass generation.

Analogies and historical precedents

• Comparisons are made to GPS (eroding spatial memory), calculators, assembly vs high‑level languages, cars vs walking, and even Plato’s worries about writing.
• Some say this is another round of “new tech will rot our brains”; others note that unlike calculators or books, LLMs are unreliable and can hallucinate, so you can’t safely let underlying skills atrophy.

Education, equity, and long‑term culture

• Teachers and academics worry assignments and exams will no longer build real skills; essays graded as artifacts lose their value as thinking exercises.
• There’s fear that disadvantaged students may over‑rely on LLMs, short‑circuiting the very “hard learning” they need to advance.
• A counter‑view holds that LLMs can be transformative “mentors” or scaffolds for those without access to human support—if used for explanation and Socratic critique.

How to use LLMs well (proposed norms)

• Suggested healthy patterns: use LLMs to:
  • critique and stress‑test your own writing or code,
  • summarize or clarify dense material,
  • handle routine or boilerplate tasks.
• Unhealthy patterns: letting them originate core ideas, arguments, or designs, then merely skimming and accepting, which encourages repetitive, shallow, biased thinking.

Methodology and scope skepticism

• Some criticize the study: small sample (esp. for EEG), short 20‑minute tasks, SAT‑style reflective prompts, and a narrow domain (essay writing).
• Others stress that, despite limitations, having empirical evidence at all is valuable, and that the pattern (LLM < search < brain‑only) matches broader concerns.