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The six learning methods

Each method has a stance, a strength, a weakness, and a characteristic style for the AI to take during sessions. The 42-cell prompt matrix (6 methods × 7 steps) implements these stances per cycle step.

Deductive

Stance: Theory first. State the rule completely, then demonstrate with examples, then ask the learner to apply.

Strong when: the topic has clean, statable rules (formal grammar, mathematical proofs, type systems). The learner already accepts that rules govern the domain and wants to internalise them efficiently.

Weak when: the rules are fuzzy, contested, or context- dependent. Pure deductive teaching of "good taste" or "clarity" lands flat — the learner needs to see many examples before the implicit pattern crystallises.

AI's style: precise, structured, complete. Spells out the rule in plain language, demonstrates with prototypical worked examples, then asks the learner to solve a fresh instance.

Inductive

Stance: Examples first. Show three to four carefully chosen examples of the same phenomenon and let the learner derive the rule themselves. Reveal the rule only after the learner has formed a hypothesis.

Strong when: pattern recognition is exactly the cognitive skill the learner needs to develop. Language learning, music theory, chess tactics, machine learning intuition — all benefit from inductive practice.

Weak when: speed matters. The inductive route is slower than deductive when the rule is simple and unambiguous. "Always free your memory" doesn't need three examples; just state it.

AI's style: presents examples side-by-side, holds back from explaining, asks "what pattern do you see?" or "what's the next item in this series?".

Error-based

Stance: Provoke mistakes, then learn from them. Pose tasks specifically designed to trip the learner into the topic's classic pitfalls. Then explain why the trap is so tempting.

Strong when: the topic has well-known traps (subject- verb agreement in long sentences, off-by-one errors in loops, common fallacies in argumentation). The learner benefits from feeling the trap pull before they understand the corrective mechanism.

Weak when: the learner is fragile, anxious, or new. The "productive frustration" can tip into "I'm bad at this" without a careful framing. The AI's step 3 (Error) prompt in this method explicitly says "diagnose precisely without padding" — that's a teaching choice, not a personality defect.

AI's style: confrontational about the mistake, then deeply explanatory about its mechanism. "That's the classic trap X — you fell into it because Y. Here's why it's so tempting."

Dialogic

Stance: conversational exchange, low pressure. Frame tasks as invitations, not tests. Affirm what's right explicitly before delivering corrections. Let the learner co-steer.

Strong when: the learner has anxiety, fragile confidence, or has hit a wall. The relaxed tone restores agency. Also strong when the topic itself is conversational (rhetoric, debate, presentation skills).

Weak when: the learner wants direct instruction and is frustrated by "want to give it a try?" framing. Some learners read dialogic prompts as evasive.

AI's style: warm, curious, low-density. Asks "what led you there?" before correcting. Affirms partial correctness explicitly. Suggests tempo or focus shifts.

Contextual

Stance: real-world scenarios first. Set up a concrete situation where the topic is immediately needed; theory comes only after the learner has tried to act in the scenario.

Strong when: the topic is applied or domain-specific (business communication, clinical reasoning, engineering trade-offs). The learner needs to feel the situational pressure to understand which theoretical knob actually matters.

Weak when: the topic is genuinely abstract (set theory, formal logic, music theory in a vacuum). Forcing a scenario makes the lesson feel contrived.

AI's style: scene-setting. "You're at the door of a client meeting and they ask…". Asks for the learner's next concrete action. Shows consequences inside the scenario.

AI-adaptive

Stance: the AI picks per turn. Reads the profile and the session history; selects whichever of the other five methods fits this exchange. Justifies the choice in one sentence.

Strong when: the learner has a balanced profile (no dominant method) or is in a session where multiple methods might work. Also strong for advanced learners who can articulate when a method isn't landing.

Weak when: the learner wants a stable, predictable teaching style. The constant method-switching can feel jittery if not justified well per turn.

AI's style: meta-aware. Names the method it's choosing ("Let me try inductively..."), executes that method faithfully, and switches when the signal says it's not working.

Choosing among them

Your assessment gives you a 6-method profile. The dominant method is what new sessions start in. But:

  • The session evaluator may suggest staying, advancing, or — rarely — stepping back per cycle step.
  • The method-switch heuristic detects stagnation (three sessions of flat understanding + high stress) and surfaces a "want to try [other method]?" banner.
  • You can manually pick a method on the Session page's start button. Useful when you know the topic calls for one specific method.

Method-switching is the goal, not method-loyalty. A learner who has used five of the six methods across their AdaptiveLearner history has a richer mental toolkit than one who's locked into deductive forever.