Fordel Studios
The Problem
Education has a well-documented scaling problem: personalized tutoring is the most effective form of instruction (Bloom's 2-sigma effect: students with one-on-one tutoring perform 2 standard deviations better than classroom instruction), but it is economically inaccessible at scale. One-on-one tutors cost $40–150/hour. A classroom teacher with 28 students cannot provide individualized pacing.
Digital learning platforms solve the access problem but not the personalization problem. A learner struggling with a concept in a video course watches the next video anyway. A learner who already knows 80% of a topic still sits through the full course. The platform serves the median learner and under-serves everyone else.
Corporate L&D has a parallel version: compliance training completion rates are tracked; actual knowledge retention is not. Employees click through required training, pass minimum-threshold assessments, and retain little. Carnegie Learning's research on adaptive learning in mathematics demonstrates that mastery-based progression — moving forward only when demonstrated, not scheduled — produces significantly better retention outcomes.
The Solution
The Adaptive Learning Agent tutors individual learners through configured knowledge domains using Socratic dialogue, adaptive problem presentation, and mastery-based progression.
The agent assesses current knowledge state through diagnostic interaction rather than a fixed pre-test. It identifies specific gaps and misconceptions, not just overall score. It then selects explanations, examples, and practice problems based on the learner's demonstrated knowledge state, adjusting in real time based on responses.
When a learner is struggling, the agent does not repeat the same explanation — it tries a different approach: a concrete example, an analogy, a simpler prerequisite concept. When a learner demonstrates mastery, the agent advances to the next concept rather than continuing practice at the same level. The interaction is conversational; the agent asks Socratic questions rather than simply providing answers.
How It's Built
A knowledge graph represents the curriculum as a directed graph of concepts with prerequisite relationships and mastery criteria. A learner state model tracks each learner's demonstrated knowledge at the concept level, updated in real time from interaction. The tutoring agent uses the knowledge graph and learner state to select the next concept, generate Socratic questions, evaluate responses, and select follow-up actions. An LLM handles natural language generation with structured constraints from the knowledge graph. Problem generation uses a combination of templated problems and LLM-generated variations with automatic validation against expected solutions.