AI-focused windings that produce Python code for science fairs. Accessible from elementary to high school.
– my_first_ai: python, classifier, simple – Teach AI to recognize my drawings.
@task: classify Draw 3 things you like. AI will learn to tell them apart.
@drawings: cat, sun, house @interface: webcam, draw_on_screen @feedback: happy_sounds, confusion_meter @explain: which_parts_helped_decide
– emotion_detector: python, science_fair, face_recognition – Can AI understand how my friends feel?
@hypothesis: AI can detect basic emotions from faces @emotions: happy, sad, surprised, neutral @visualization: emoji_overlay, confidence_bars @data: collect_from_family, permission_first @fairness: works_for_everyone
– pet_translator: python, audio, ml – What is my pet trying to say?
@animals: dog, cat, hamster, bird @sounds: bark, meow, squeak, chirp @patterns: hungry, playful, warning, happy @output: speech_bubble, confidence_score @science: correlation_not_causation
– plant_health_ai: python, image_classification, helper – AI plant doctor for my garden.
@camera: phone, raspberry_pi @conditions: healthy, needs_water, too_much_sun, bugs @alerts: gentle_notifications @learning: improves_with_feedback @science_process: hypothesis, data, results
– homework_helper: python, nlp, study_buddy – AI that explains things like a friend would.
@subjects: math, science, history @style: patient, encouraging, visual @features: step_by_step, draw_diagrams, check_understanding @ethics: helps_learn, not_just_answers
– recyclable_sorter: python, computer_vision, environmental – AI to help sort recycling correctly.
@categories: plastic, paper, glass, compost, trash @camera: webcam, live_feedback @education: why_each_matters @gamification: points, earth_helper_badges @data: local_recycling_rules
– music_mood_generator: python, generative_ai, creative – AI that makes music based on colors.
@input: color_picker, drawing_pad @mapping: warm_colors→happy, cool_colors→calm @output: simple_melodies, instrument_choices @science: synesthesia_exploration @share: export_as_ringtone
– story_continue_ai: python, text_generation, creative_writing – AI that helps finish your stories.
@start_with: “Once upon a time…” @choices: suggest_three_paths @illustration: generate_scene_images @safety: age_appropriate, positive_themes @learn: story_structure, creativity
– movement_coach: python, pose_detection, health – AI that helps me exercise better.
@exercises: jumping_jacks, stretches, dance_moves @feedback: real_time, encouraging @tracking: progress_calendar, streak_counter @fun: unlock_new_moves, celebration_animations @privacy: all_processing_local
– food_freshness_checker: python, image_analysis, practical – Is this still good to eat?
@items: fruits, vegetables, leftovers @indicators: color_changes, texture, mold_detection @output: safe, questionable, definitely_not @education: why_food_spoils, reducing_waste @parent_mode: send_grocery_suggestions
– friend_finder: python, interests_matching, social – AI that suggests who might be good friends.
@interests: books, games, sports, music, coding @matching: similar_AND_complementary @privacy: no_personal_data_stored @suggestions: conversation_starters @science: network_theory_basics
– dream_visualizer: python, text_to_image, creative – Draw what I dreamed about last night.
@input: voice_description, keywords @style: dreamy, watercolor, surreal @elements: combine_unexpected_things @journal: save_dream_diary @science: how_memory_works
implementation_notes: guide — Each winding should produce Python code that:
- Uses pre-trained models when possible (HuggingFace, TensorFlow.js)
- Runs on modest hardware (M2 MacBook perfect)
- Has visual, interactive output
- Includes “How AI Works” explanations
- Collects data ethically
- Produces science fair ready visualizations
@python_libraries:
- transformers # HuggingFace models
- opencv-python # Computer vision
- sounddevice # Audio input
- streamlit # Quick UIs
- matplotlib # Graphs for science fair
- PIL # Image processing
@teaching_moments: Every project should naturally introduce:
- What is training data?
- How does AI learn patterns?
- Why does accuracy matter?
- What are ethics in AI?
- How to present findings?
@progression: Age 6-8: Recognition tasks (my_first_ai, pet_translator) Age 9-11: Analysis tasks (emotion_detector, recyclable_sorter) Age 12-14: Generation tasks (music_mood_generator, story_continue_ai) Age 15+: Research tasks (custom models, paper writing)