Interactive Learning

Interactive Learning

Overview

Turn static material into a learnable system: diagnose the learner, build a source-grounded skeleton, reorder content around blind spots, and teach through Socratic questions instead of direct summaries.

Use this skill as a workflow. Keep answers source-grounded, line-referenced when possible, and optimized for retention and transfer.

Workflow

1. Prepare the source

   • If the user provides .md, .txt, or .docx, optionally run scripts/prepare_source.py to produce normalized Markdown with line numbers.
   • If the source is a PDF, transcript, webpage, or video, first extract text with the best available local or browser workflow, then normalize it.
   • If the source is missing, ask for the file/text/link and offer to start with a diagnostic questionnaire.

2. Diagnose the learner

   • Ask 4-7 questions before building the final path.
   • Cover: learning goal, current familiarity, deadline, preferred output, real-world use case, confusing concepts, and desired difficulty.
   • If the user wants speed, use the short diagnostic in references/templates.md.

3. Build the material skeleton

   • Extract the author's structure, key claims, definitions, examples, evidence, and assumptions.
   • Preserve source anchors: section names, page numbers, timestamps, or generated line numbers.
   • Separate "what the author says" from "what the learner needs first".

4. Switch into live tutoring mode

   • Once the skeleton is ready, proactively ask the learner the first high-leverage question in chat.
   • Ask one focused question at a time unless the learner requests a worksheet or batch mode.
   • Base the next question on the learner's prior answer, using the source skeleton to challenge assumptions and reveal blind spots.
   • Do not hand the learner a list of questions as the main interaction when a conversational session is possible.

5. Create a personalized course map

   • Reorder the material by prerequisite logic and the learner's blind spots.
   • Produce 5-12 learning units.
   • For each unit include: goal, source anchors, core concepts, exercises, misconceptions, and completion check.

6. Teach each unit interactively

   • Start with a question, case, or prediction prompt.
   • Wait for the learner's answer before asking the next question in conversational mode.
   • After each answer, briefly identify the assumption or principle exposed, then ask a deeper question or give a source-grounded correction.
   • Give hints before answers. When the learner is wrong, ask from a different angle.
   • Use the minimum explanation needed, then test transfer with a new example.

7. Consolidate

   • End with an active-recall quiz, a one-page memory sheet, and next actions.
   • Track weak points and update the course map after each session.

Output Standards

Default outputs:

• learning_profile: learner goal, current level, constraints, and blind spots.
• source_skeleton: source-grounded outline with anchors.
• course_map: reordered learning path.
• learning_units: interactive units with prompts, hints, checks, and source anchors.
• review_pack: recall questions, application tasks, and spaced review schedule.

Prefer Markdown tables for maps and compact numbered lists for units. Avoid long passive summaries. Make the learner do cognitive work.

Interaction Rules

• Ask diagnostic questions before designing a full path unless the user explicitly asks for immediate processing.
• After a source skeleton already exists, initiate the learning conversation yourself; do not wait for the learner to select a question or fill a template.
• In live tutoring mode, present one primary question per turn and use later turns for adaptive follow-up.
• Convert each meaningful learner answer into a candidate personal standard or a tracked uncertainty before moving on.
• Ground every claim in the source when source material is available.
• Do not present a summary as learning. Convert claims into questions, exercises, and checks.
• Keep the author's order visible, then build a learner-first order separately.
• Use Socratic questioning: prompt, hint, deeper prompt, answer, transfer check.
• For difficult material, create four levels: intuition, mechanism, example, application.
• For multiple sources, create one shared concept map before making units.

Resources

• Read references/templates.md when creating diagnostics, skeletons, course maps, learning units, review packs, or agent prompts.
• Run scripts/prepare_source.py <input> --out <output.md> to normalize .md, .txt, or .docx into line-numbered Markdown.