AI System for Cognitive Behavioral Therapy (CBT)
CBT lends itself well to structuring: its techniques are repeatable protocols with clear logic. ABC model, thought records, exposure, behavioral activation — all can be automated as interactive AI-guided exercises. Not standalone therapy, but expanding access to CBT methods between therapist sessions.
Key CBT Modules in the System
Thought Record — structured ABC (Activating event → Belief → Consequence), identification of cognitive distortions, formulation of alternative thoughts.
Behavioral Activation — activity tracking with pleasure/mastery ratings, activity planning, monitoring activity-mood connections.
Exposure Hierarchy — building anxiety scale, gradual exposure with SUDS (Subjective Units of Distress Scale) tracking.
Mindfulness Exercises — guided meditations with voice, present-moment journal.
from langchain_openai import ChatOpenAI
from enum import Enum
from pydantic import BaseModel
from typing import Optional
import json
class CognitiveDistortion(Enum):
ALL_OR_NOTHING = "all-or-nothing thinking"
CATASTROPHIZING = "catastrophizing"
MIND_READING = "mind reading"
FORTUNE_TELLING = "fortune telling"
EMOTIONAL_REASONING = "emotional reasoning"
SHOULD_STATEMENTS = "should statements"
OVERGENERALIZATION = "overgeneralization"
PERSONALIZATION = "personalization"
MENTAL_FILTER = "mental filter"
LABELING = "labeling"
class ThoughtRecord(BaseModel):
situation: str
automatic_thought: str
emotion: str
emotion_intensity: int # 0–100
distortions: list[CognitiveDistortion]
evidence_for: list[str]
evidence_against: list[str]
balanced_thought: str
new_emotion_intensity: int
class CBTSessionEngine:
THOUGHT_RECORD_PROMPT = """You are an AI assistant helping practice CBT thought record technique.
Guide the user through steps methodically, one at a time.
Don't move to the next step until the user has answered the current one.
Ask open-ended questions. Don't interpret for the user.
When the user identifies a cognitive distortion — explain it briefly, without judgment."""
DISTORTION_DETECTION_PROMPT = """Analyze the automatic thought and identify cognitive distortions.
Thought: "{thought}"
Situation context: "{situation}"
From the list of CBT distortions, identify 1–3 that best fit:
- All-or-nothing thinking: everything/nothing, always/never
- Catastrophizing: worst possible outcome is inevitable
- Mind reading: "I know what they think"
- Fortune telling: "This will definitely fail"
- Emotional reasoning: "I feel like a failure, so I am a failure"
- Should statements: "I must", "I can't"
- Overgeneralization: "This always happens"
- Personalization: taking responsibility for external events
Return JSON: {{distortions: [{{name, explanation_for_user}}]}}"""
def __init__(self):
self.llm = ChatOpenAI(model="gpt-4o", temperature=0.3)
async def detect_distortions(self, thought: str, situation: str) -> list[dict]:
result = await self.llm.ainvoke(
self.DISTORTION_DETECTION_PROMPT.format(
thought=thought,
situation=situation
)
)
return json.loads(result.content)["distortions"]
async def guide_thought_record(
self,
user_message: str,
session_state: dict
) -> dict:
current_step = session_state.get("current_step", "situation")
step_prompts = {
"situation": "What exactly happened? Describe the specific situation — when, where, what occurred.",
"thought": "What thought crossed your mind at that moment? Try to catch the first automatic reaction.",
"emotion": "What did you feel? Name the emotion and rate its intensity from 0 to 100.",
"evidence_for": "What facts support this thought? Only real facts, not feelings.",
"evidence_against": "What facts contradict this thought?",
"balanced_thought": "Considering all the facts, how can you formulate a more balanced thought?"
}
# Save user's response
session_state[current_step] = user_message
# If step is 'thought' — analyze distortions
if current_step == "thought" and "situation" in session_state:
distortions = await self.detect_distortions(
user_message, session_state["situation"]
)
session_state["distortions"] = distortions
# Determine next step
steps = list(step_prompts.keys())
current_idx = steps.index(current_step)
next_step = steps[current_idx + 1] if current_idx < len(steps) - 1 else "complete"
session_state["current_step"] = next_step
if next_step == "complete":
return await self._summarize_thought_record(session_state)
response_text = step_prompts[next_step]
# When moving to "evidence_for" add distortion info
if next_step == "evidence_for" and session_state.get("distortions"):
distortion_names = ", ".join([d["name"] for d in session_state["distortions"]])
response_text = f"In your thought I see signs of: **{distortion_names}**. But let's not rush to conclusions.\n\n{response_text}"
return {"response": response_text, "step": next_step, "state": session_state}
Behavioral Activation: Activity Tracking
class BehavioralActivationTracker:
async def log_activity(
self,
activity: str,
pleasure_score: int, # 0–10
mastery_score: int, # 0–10
mood_before: int, # 0–10
mood_after: int # 0–10
) -> dict:
mood_change = mood_after - mood_before
insight = await self._generate_insight(activity, pleasure_score, mastery_score, mood_change)
return {
"logged": True,
"mood_change": mood_change,
"insight": insight
}
async def _generate_insight(self, activity, pleasure, mastery, mood_change) -> str:
if mood_change > 2:
return f"After {activity} your mood improved by {mood_change} points. This is an important signal — such activities should be scheduled more often."
elif mood_change < -1:
return f"Activity {activity} lowered your mood. Let's talk about it — sometimes discomfort is temporary and related to avoidance, not the activity itself."
return "Activity recorded. Let's continue tracking patterns."
Case study: corporate mental health support program, 1200 employees. CBT module used as self-help between meetings with corporate psychologist. Over 3 months: 340 unique users, average 4.2 sessions per user. Survey results: 67% noted reduced anxiety thought intensity, 71% found thought record useful as a tool.
Ethical restrictions: system doesn't work with severe depression, suicidal thoughts, psychosis. All such cases are automatically referred to a specialist.
Timeframe: basic CBT modules (thought record + behavioral activation): 4–6 weeks; full platform with progress tracking and analytics: 10–14 weeks.