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.
Language models work by identifying subtle, implicit patterns in human language that even linguists cannot fully articulate. Their success broadens our definition of "knowledge" to include systems that can embody and use information without the explicit, symbolic understanding that humans traditionally require.
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.
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.
Oboe's user data shows that over two-thirds of learners arrive with a clear, objective-based goal, such as upskilling for a job or passing a test. This contradicts the idea that AI learning is for casual exploration and highlights the need for goal-oriented product design to solve a user's specific problem.
Setting an LLM's temperature to zero should make its output deterministic, but it doesn't in practice. This is because floating-point number additions, when parallelized across GPUs, are non-associative. The order in which batched operations complete creates tiny variations, preventing true determinism.
Instead of making users wait for an entire course to generate, Oboe immediately delivers the first module while the rest loads in parallel. This UX decision is critical for building trust and reinforcing the core value proposition that learning is achievable and can be started right away, avoiding user drop-off.
Unlike human teachers who can "read the room" and adjust their methods, current AI tools are passive. A truly effective AI tutor needs agentic capabilities to reassess its teaching strategy based on implicit user behavior, like a long pause, without needing explicit instructions from the learner.
A primary reason users abandon AI-driven learning is the "re-engagement barrier." After pausing on a difficult concept, they lose the immediate context. Returning requires too much cognitive effort to get back up to speed, creating a cycle of guilt and eventual abandonment that AI tools must solve for.