AI Study Buddy

Learning with a body, not just a screen

I wanted an AI study companion that helps students learn through personalised quizzes, flashcards and study schedules, with progress tracking and a learning path that adapts as they go. StudyBuddy is the physical version of that idea: ESP32 hardware, OLED displays and servos combined with AI software, giving children visual, audio and physical feedback in real time.

Goals

• Design an interactive robot that uses AI to personalise lessons in real time.
• Make learning more engaging through speech recognition, movement and responsive feedback.
• Bridge the gap between digital and hands-on learning.

Hardware and software

Hardware

• ESP32: the core microcontroller that manages every interaction.
• Two OLED displays: one for facial expressions, one for text.
• Servo motors: physical movements and gestures, like nodding.
• Speaker: audio feedback and spoken responses.

Software pipeline

• Speech recognition: Vosk for offline speech-to-text.
• Language processing: TinyLlama through Hugging Face Transformers.
• Knowledge base: Wikipedia API for factual answers.
• Adaptive learning: a lightweight Bayesian skill estimation algorithm.

The Python code is split into two main classes: one that talks to the hardware and one that runs the learning experience.

class ESP32Communicator:
    # Handles communication with ESP32 hardware
    # Controls displays, servos, and audio

class StudyBuddyAI:
    # Main AI companion managing the learning experience
    # Handles speech recognition, response generation

Tracking what each child knows

StudyBuddy keeps a skill estimate between 0 and 1 for every subject and topic. After each answer it nudges that estimate up or down. A "surprise factor" makes the update bigger when the result is unexpected, such as a correct answer on a topic the child was struggling with.

# Key adaptive learning algorithm (simplified)
def update_subject_skill(self, subject, topic, correct):
    # Current estimates
    current_skill = self.profile['skills'][subject][topic]

    # Bayesian update weights
    surprise_factor = 1.5 if (correct and current_skill < 0.5) or \
                          (not correct and current_skill > 0.5) else 1.0

    # Update skill estimate
    if correct:
        new_skill = current_skill + (1 - current_skill) * 0.2 * surprise_factor
    else:
        new_skill = current_skill * (1 - 0.2 * surprise_factor)

    # Store updated skill
    self.profile['skills'][subject][topic] = new_skill

This lets the robot:

• Scale difficulty to the skills a child has shown.
• Recommend practice that targets weak areas.
• Adapt to different learning styles (visual, auditory, kinesthetic).

What a session looks like

Interaction flow

• Startup: a friendly greeting with animation and sound.
• Main menu: learning modes are chosen by voice.
• Learning activities: Q&A, vocabulary, maths and science.
• Feedback: expressions, sound and movement together.

Example: science quiz

StudyBuddy: "Let's learn about photosynthesis! This is the process plants use
to make food using sunlight. Would you like to take a mini-quiz?"

User: "Yes"

StudyBuddy: [Displays a thinking expression]
"Great! Here's your first question: What do plants need for photosynthesis?
Is it: A) Just water, B) Sunlight, water, and carbon dioxide, or C) Soil only?"

User: "B"

StudyBuddy: [Displays happy expression, servo nods]
"Correct! Plants need sunlight, water, and carbon dioxide to make their own food
through photosynthesis. They release oxygen as a byproduct, which we breathe."

Example: adaptive maths

StudyBuddy: "Based on your previous answers, I'll give you a slightly more
challenging subtraction problem. What is 43 - 18?"

User: "25"

StudyBuddy: [Displays thinking face]
"Let's check that... I got a different answer. Let me help you work through this.
First, we can subtract 3 - 8, but since 8 is larger than 3, we need to borrow.
40 + 3 becomes 30 + 13. Now 13 - 8 = 5, and 30 - 10 = 20.
So 43 - 18 = 25. You were right! Great job!"

[System updates user's math subtraction skill from 0.65 to 0.71]

Testing and findings

Key findings

• AI-driven personalisation made students more engaged.
• Physical interaction through gestures and expressions helped them focus.
• Combining visual, audio and physical feedback makes learning more inclusive and accessible.

What I'd build next

Stack

See it running

A walkthrough of StudyBuddy's voice interaction, expressions and movement.