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engine/drift.py

@@ -92,11 +92,17 @@ def apply_response_effects(state, student_response):
     return state
 
 
-def generate_teaching_note(state, student_response, mode):
+def generate_teaching_note(state, student_response, mode, persona=None):
     """Generate pedagogical feedback based on the literary interaction."""
     response_lower = student_response.lower()
     notes = []
 
+    # Get character-specific context if available
+    char_name = persona.get("persona_name", "the character") if persona else "the character"
+    source_text = persona.get("source_text", {}) if persona else {}
+    author = source_text.get("author", "the author")
+    title = source_text.get("title", "the text")
+
     # Question quality - literary analysis focused
     if any(word in response_lower for word in ["why did you", "why do you", "why are you"]):
         notes.append("💭 **Literary Technique**: 'Why' questions can sound accusatory. Consider using 'what' or 'how' to explore motivations more naturally (e.g., 'What were you thinking when...' or 'How did that moment feel?')")
@@ -109,7 +115,10 @@ def generate_teaching_note(state, student_response, mode):
 
     # Question depth - connect to literary concepts
     if len(student_response.split()) < 8:
-        notes.append("📖 **Add Textual Context**: Reference specific moments from the story to ground your question. For example: 'When you were at the dock...' or 'You mentioned your mother's promise...'")
+        if author and title:
+            notes.append(f"📖 **Add Textual Context**: Reference specific moments from '{title}' to ground your question. Connect to scenes, relationships, or conflicts from {author}'s narrative.")
+        else:
+            notes.append("📖 **Add Textual Context**: Reference specific moments from the story to ground your question in the character's actual experiences.")
 
     if any(phrase in response_lower for phrase in ["tell me about", "tell me more", "what's that like", "how do you feel"]):
         notes.append("✅ **Strong Question**: Open-ended questions like this encourage the character to reveal subtext, internal conflict, and thematic depth. This is effective literary analysis.")
@@ -119,7 +128,10 @@ def generate_teaching_note(state, student_response, mode):
 
     # Character engagement - literary context
     if mode == "triggered" and state.get("trust", 0) < 0.4:
-        notes.append("⚠️ **Character Defensive**: Notice how the character withdraws when challenged - this mirrors how Joyce/Hemingway/etc. show psychological realism. Try questions that validate their experience first.")
+        if author:
+            notes.append(f"⚠️ **Character Defensive**: Notice how {char_name} withdraws when challenged - this mirrors how {author} shows psychological realism. Try questions that validate their experience first.")
+        else:
+            notes.append("⚠️ **Character Defensive**: The character is withdrawing. Try questions that validate their experience before probing deeper.")
 
     if mode == "trusting" and state.get("anxiety", 0) > 0.6:
         notes.append("✨ **Key Moment**: The character is vulnerable right now - this is where authors reveal crucial backstory and motivation. Press deeper into their fears and desires.")
@@ -132,12 +144,12 @@ def generate_teaching_note(state, student_response, mode):
 
     # Add literary context based on specific terms used
     if any(word in response_lower for word in ["choice", "decision", "decide"]):
-        notes.append("💡 **Theme Connection**: You're exploring agency and choice - a key theme in modernist literature. How does paralysis or action define this character?")
+        notes.append("💡 **Theme Connection**: You're exploring agency and choice - a key theme in literature. How does paralysis or action define this character?")
 
-    if any(word in response_lower for word in ["family", "mother", "father", "parent"]):
+    if any(word in response_lower for word in ["family", "mother", "father", "parent", "sister", "brother"]):
         notes.append("👨‍👩‍👧 **Family Dynamics**: You're examining how family obligations shape identity - analyze how the author uses familial duty to explore broader social constraints.")
 
     if not notes:
-        notes.append("✅ **Good Engagement**: You're exploring the character thoughtfully. Consider connecting their responses to literary themes like paralysis, identity, duty, or social pressure.")
+        notes.append("✅ **Good Engagement**: You're exploring the character thoughtfully. Consider connecting their responses to the story's central themes and conflicts.")
 
     return "\n".join(notes)

engine/responder.py

@@ -272,7 +272,7 @@ Rules:
             state["emotional_memory"].append(memory_tag)
             state["emotional_memory"] = state["emotional_memory"][-5:]
 
-        teaching_note = generate_teaching_note(state, student_prompt, mode)
+        teaching_note = generate_teaching_note(state, student_prompt, mode, persona)
         teaching_note += "\n\n💡 Response generated using HuggingFace Inference API"
 
         return response_text, state, teaching_note
@@ -323,9 +323,9 @@ def generate_response_claude(student_prompt, persona, conversation_history, forc
             state["emotional_memory"].append(memory_tag)
             state["emotional_memory"] = state["emotional_memory"][-5:]
 
-        teaching_note = generate_teaching_note(state, student_prompt, mode)
+        teaching_note = generate_teaching_note(state, student_prompt, mode, persona)
         teaching_note += "\n\n✨ Response generated using Claude AI (Premium)"
-        
+
         return response_text, state, teaching_note
         
     except Exception as e:
@@ -575,7 +575,7 @@ Do NOT write conversations, dialogues, or responses from other people.
         state["emotional_memory"] = state["emotional_memory"][-5:]
 
     # Teaching note
-    teaching_note = generate_teaching_note(state, student_prompt, mode)
+    teaching_note = generate_teaching_note(state, student_prompt, mode, persona)
     teaching_note += "\n\n⚡ Response generated using local TinyLlama model (CPU-optimized)"
 
     return response, state, teaching_note