General LLMs are powerful but lack the core architecture of a true learning platform. A dedicated educational tool needs built-in pedagogical methods, multimodal content, and a clear structure, which is absent in a conversational, general-purpose AI that was not built for learning at its core.
Related Insights
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.
LLMs shine when acting as a 'knowledge extruder'—shaping well-documented, 'in-distribution' concepts into specific code. They fail when the core task is novel problem-solving where deep thinking, not code generation, is the bottleneck. In these cases, the code is the easy part.
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.
The current limitation of LLMs is their stateless nature; they reset with each new chat. The next major advancement will be models that can learn from interactions and accumulate skills over time, evolving from a static tool into a continuously improving digital colleague.
LLMs learn two things from pre-training: factual knowledge and intelligent algorithms (the "cognitive core"). Karpathy argues the vast memorized knowledge is a hindrance, making models rely on memory instead of reasoning. The goal should be to strip away this knowledge to create a pure, problem-solving cognitive entity.
New features in Google's Notebook LM, like generating quizzes and open-ended questions from user notes, represent a significant evolution for AI in education. Instead of just providing answers, the tool is designed to teach the problem-solving process itself. This fosters deeper understanding, a critical capability that many educational institutions are overlooking.
While AI can "polish" work, it cannot be used well by someone who doesn't already know what good looks like. For students who have only ever used AI, they lack the foundational judgment to guide the tool or recognize its flaws, leading to superficially polished but poor quality output.
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.
Unlike humans, whose poor memory forces them to generalize and find patterns, LLMs are incredibly good at memorization. Karpathy argues this is a flaw. It distracts them with recalling specific training documents instead of focusing on the underlying, generalizable algorithms of thought, hindering true understanding.
A key gap between AI and human intelligence is the lack of experiential learning. Unlike a human who improves on a job over time, an LLM is stateless. It doesn't truly learn from interactions; it's the same static model for every user, which is a major barrier to AGI.
