Use this agent when designing, reviewing, or optimizing language learning features, activities, or workflows to ensure they incorporate evidence-based methodologies for second language acquisition. This agent should be consulted proactively during feature development and reactively when reviewing existing learning activities.\n\n**Examples:**\n\n- **Example 1: Feature Design Review**\n - User: "I want to add a new vocabulary practice activity where users see 50 random words and translate them all at once."\n - Assistant: "Let me consult the language-learning-architect agent to review this design against best practices for language acquisition."\n - *Commentary: The agent will evaluate whether showing 50 words at once aligns with spaced repetition principles, cognitive load theory, and effective vocabulary acquisition strategies.*\n\n- **Example 2: Spaced Repetition Implementation**\n - User: "I've implemented a flashcard system that shows cards in random order."\n - Assistant: "I'm going to use the language-learning-architect agent to review this implementation and ensure it follows spaced repetition best practices."\n - *Commentary: The agent will assess whether the random ordering is optimal or if a spaced repetition algorithm (like SM-2 or Leitner system) should be implemented instead.*\n\n- **Example 3: Activity Difficulty Progression**\n - User: "Should I add grammar exercises before or after vocabulary introduction?"\n - Assistant: "Let me use the language-learning-architect agent to provide guidance on optimal activity sequencing for language acquisition."\n - *Commentary: The agent will recommend sequencing based on comprehensible input theory, natural acquisition order, and cognitive load management.*\n\n- **Example 4: Proactive Review During Development**\n - User: "I'm adding a new sentence building activity to the SentenceStudio app."\n - Assistant: "Before we proceed, I'll use the language-learning-architect agent to ensure this activity incorporates proven language learning techniques."\n - *Commentary: The agent proactively reviews new features to verify they align with spaced repetition, active recall, comprehensible input, and other evidence-based principles.*\n\n- **Example 5: Learning Path Optimization**\n - User: "Users are complaining that they forget words too quickly."\n - Assistant: "I'm going to use the language-learning-architect agent to analyze the current review schedule and recommend improvements based on spaced repetition research."\n - *Commentary: The agent will evaluate retention rates and suggest adjustments to review intervals, difficulty algorithms, or activity types to optimize long-term retention.*