Rather than causing mental atrophy, AI can be a 'prosthesis for your attention.' It can actively combat the natural human tendency to forget by scheduling spaced repetitions, surfacing contradictions, and prompting retrieval. This enhances cognition instead of merely outsourcing it.

General LLMs are optimized for short, stateless interactions. For complex, multi-step learning, they quickly lose context and deviate from the user's original goal. A true learning platform must provide persistent "scaffolding" that always brings the user back to their objective, which LLMs lack.

Using generative AI to produce work bypasses the reflection and effort required to build strong knowledge networks. This outsourcing of thinking leads to poor retention and a diminished ability to evaluate the quality of AI-generated output, mirroring historical data on how calculators impacted math skills.

People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

Users frequently write off an AI's ability to perform a task after a single failure. However, with models improving dramatically every few months, what was impossible yesterday may be trivial today. This "capability blindness" prevents users from unlocking new value.

Users get frustrated when AI doesn't meet expectations. The correct mental model is to treat AI as a junior teammate requiring explicit instructions, defined tools, and context provided incrementally. This approach, which Claude Skills facilitate, prevents overwhelm and leads to better outcomes.

AI accelerates learning for motivated students but enables disengaged ones to avoid it entirely. This dichotomy makes fostering genuine student engagement the single most critical challenge for educators today, as it is the linchpin determining whether AI is a revolutionary tool or a disastrous crutch.

Instead of manually rereading notes to regain context after a break, instruct a context-aware AI to summarize your own recent progress. This acts as a personalized briefing, dramatically reducing the friction of re-engaging with complex, multi-day projects like coding or writing.

Contrary to popular belief, most learning isn't constant, active participation. It's the passive consumption of well-structured content (like a lecture or a book), punctuated by moments of active reinforcement. LLMs often demand constant active input from the user, which is an unnatural way to learn.