66 lines
2.3 KiB
Markdown
66 lines
2.3 KiB
Markdown
# FAQ
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## What is Didactopus?
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Didactopus is a mastery-oriented learning infrastructure that uses concept graphs, evidence-based assessment, and adaptive planning to support serious learning.
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## Is this just a tutoring chatbot?
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No. The intended architecture is broader than tutoring. Didactopus maintains explicit representations of:
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- concepts
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- prerequisites
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- mastery criteria
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- evidence
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- learner state
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- planning priorities
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## How is an AI student's learned mastery represented?
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An AI student's learned mastery is represented as structured state, not just conversation history.
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Important elements include:
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- mastered concept set
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- evidence records
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- dimension-level competence summaries
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- weak-dimension lists
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- project eligibility
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- target-progress state
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- produced artifacts and critiques
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## Does Didactopus fine-tune the AI model?
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Not in the current design. Didactopus supervises and evaluates a learner agent, but it does not itself retrain foundation model weights.
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## Then how is the AI student “ready to work”?
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Readiness is operationalized by the mastery state. An AI student is ready for a class of tasks when:
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- relevant concepts are mastered
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- confidence is high enough
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- weak dimensions are acceptable for the target task
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- prerequisite and project evidence support deployment
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## Could mastered state be exported?
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Yes. A future implementation should support export of:
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- concept mastery ledgers
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- evidence portfolios
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- competence profiles
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- project artifacts
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- domain-specific capability summaries
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## Is human learning treated the same way?
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The same conceptual framework applies to both human and AI learners, though interfaces and evidence sources differ.
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## What is the difference between mastery and model knowledge?
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A model may contain latent knowledge or pattern familiarity. Didactopus mastery is narrower and stricter: it is evidence-backed demonstrated competence with respect to explicit concepts and criteria.
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## Why not use only embeddings and LLM judgments?
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Because correctness, especially in formal domains, often needs stronger guarantees than plausibility. That is why Didactopus may eventually need hybrid symbolic or executable validation components.
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## Can Didactopus work offline?
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Yes, that is a primary design goal. The architecture is local-first and can be paired with local model serving and locally stored domain packs.
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