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fluffychat/.github/instructions/conversation-activities.instructions.md
wcjord b6f368fa1f docs: add instruction docs for analytics, practice, activities, toolbar; adjust content-word scoring ratio (10:7) 
- Add analytics-system.instructions.md
- Add practice-exercises.instructions.md (with three-tier design direction)
- Add conversation-activities.instructions.md
- Add toolbar-reading-assistance.instructions.md
- Update copilot-instructions.md header
- Change content-word multiplier from 9 to 7 in practice_selection_repo.dart
2026-02-15 15:49:26 -05:00

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lib/pangea/activity_planner/**,lib/pangea/activity_sessions/**,lib/pangea/activity_suggestions/**,lib/pangea/activity_summary/**

Activity System

Activities are how Pangea Chat turns passive chatting into deliberate language learning. Activities are designed according best practices in task-based language teaching and second language acquisition research, with a focus on maximizing engagement and learning outcomes while minimizing friction.

Design Goals

  1. Conversation games: We would like user's to think of and describe these experiences as 'games'. That is how we will know we have arrived.
  2. Minimal front-loaded instruction: Historically, conversation practice is proceeded by learning lists of vocab and grammar, which are quickly forgotten. Instead, we want to get users practicing real conversations as quickly as possible, with any necessary instruction or feedback integrated into the experience rather than front-loaded.
  3. AI-powered assistance: The system should leverage AI to provide personalized assistance, feedback, and content generation to enhance the learning experience.
  4. Data-driven: Every interaction should generate data that can be used to improve the system, understand user behavior, and demonstrate learning outcomes.

Structured Activities

The User Experience

Key Technical Design Decisions

  • Activities are Matrix rooms. This means all existing chat infrastructure (message events, timelines, sync) works automatically. No separate activity backend needed.
  • Course context is inherited. Activity rooms know which course (space) they belong to via parent room relationships. This lets summaries reference the course's learning goals.

Activity Lifecycle States

  1. User picks an activity from their course
  2. User starts activity
  3. User chooses role
  4. User decides who to play with
  • Wait for course participants with optional "Ping" to course
  • "Play with Pangea Bot" option for solo practice
  • "Invite friends" option to share an invite link or invite specific users
  1. Activity in progress
  2. Users mark activity as finished
  3. Activity summary generated and posted in the activity room, visible to all participants
  4. Users archive activity when done reviewing the summary (removes from active view but retains in history) Optionally, users can review past activities in their activity history, which surfaces summaries and outcomes from previous sessions. We intend to implement an explicit practice flow here.

An activity is "started" when all role slots are filled. It's "finished" when all non-bot participants have marked themselves done or left. "Archived" means the user has dismissed it from their active view.

Future Work

Last updated: 2026-02-15

Lifecycle & Session Management

Roles & Bot Behavior

Activity Summaries

Descriptions & Onboarding UX

Activity Content & Media

Planning, Courses & Discovery

New Activity Types & Game Ideas

Teacher Experience