Recommended Open-Source Ai toy for Campus Maker Education

　　Campus maker education emphasizes hands-on practice, interdisciplinary integration, and open innovation. Open-source AI toys, with their customizable hardware, accessible programming interfaces, and supportive developer communities, have become ideal tools for cultivating students’ AI literacy, engineering thinking, and creative problem-solving skills. Below are tailored recommendations covering entry-level to advanced scenarios, aligned with K-12 and vocational school maker curricula.

　　Core Selection Criteria for Campus Maker Education

　　Open-Source Attribute: Publicly available hardware schematics, firmware code, and compatible with mainstream open-source platforms (e.g., Raspberry Pi, Micro:bit).

　　Educational Empowerment: Supports AI algorithm practice (machine learning, computer vision, natural language processing) and STEAM project-based learning (PBL).

　　Teaching Adaptability: Equipped with teaching resources (lesson plans, tutorials) and suitable for classroom teaching, club activities, and maker competitions.

　　Cost-Effectiveness: Budget-friendly for bulk purchases, with scalable upgrade paths.

　　Safety & Usability: Complies with campus safety standards, with user-friendly programming interfaces (Scratch/Python support).

　　Recommended Open-Source AI Toys by Skill Level

　　1. Entry-Level: For Primary & Junior High (Ages 8-14)

　　▶ FoloToy Open-Source AI Companion

　　Core Open-Source Features:

　　Hardware: Open circuit diagrams and 3D printing files (compatible with Micro:bit extension).

　　Software: Supports TensorFlow Lite for microcontrollers; open-source Python/Scratch 3.0 SDK.

　　Data Security: Localized AI processing (no cloud dependency) with encrypted interaction logs.

　　Education Empowerment:

　　Basic AI: Voice recognition, emotion analysis, and simple image classification (e.g., identifying objects via camera module).

　　Cross-Disciplinary Projects: Combine coding, art (custom 3D-printed shells), and language education (interactive story creation).

　　Adaptable Scenarios: Classroom group activities, after-school maker clubs, and beginner AI workshops.

　　Reference Price: \(40-\)60 (bulk discount available for campuses).

　　▶ Shifeng AI Magic Star (Open-Source Edition)

　　Core Open-Source Features:

　　Collaborates with Baidu Smart Cloud; open API for customizing voice interaction logic.

　　Compatible with Arduino-based sensor expansion (e.g., temperature, motion sensors).

　　Education Empowerment:

　　Emotional Computing: Teaches students to design empathy-driven AI (e.g., comforting responses for frustrated users).

　　Basic Programming: Drag-and-drop Scratch coding for triggering actions (e.g., singing, storytelling) via sensor inputs.

　　Adaptable Scenarios: Primary school AI enlightenment, parent-child maker events, and low-cost campus maker labs.

　　Reference Price: \(30-\)50 (200-500 yuan, per Chenghai toy industry data).

　　2. Intermediate: For Junior & Senior High (Ages 12-18)

　　▶ UBTECH UGOT AI Education Robot

　　Core Open-Source Features:

　　Open uCode programming platform (supports Python/Blockly); compatible with DeepSeek/Qwen large models.

　　Hardware expansion slots for Raspberry Pi and IoT modules (Wi-Fi/Bluetooth open protocols).

　　Education Empowerment:

　　Advanced AI: Logical reasoning visualization (e.g., displaying programming loop structures dynamically) and multi-modal interaction (voice + gesture recognition).

　　STEAM Integration: Combines AI programming, mechanical control, and environmental sensing (e.g., building a smart classroom monitor).

　　Adaptable Scenarios: High school AI courses, inter-school maker competitions, and PBL projects (e.g., "AI-assisted elderly care robots").

　　Reference Price: \(150-\)200 (supported by 800+ school campus deployments).

　　▶ TensorFlow Lite AI Kit for BeagleBone

　　Core Open-Source Features:

　　Open-source object detection scripts (integrates OpenCV and pre-trained COCO models).

　　Supports real-time video streaming and custom model training (e.g., training the AI to recognize school-specific objects).

　　Education Empowerment:

　　Embedded AI Practice: Teaches edge computing, model quantization, and sensor data fusion.

　　Engineering Project: Designing AI surveillance robots, automated plant care systems, etc.

　　Adaptable Scenarios: Vocational school electronics courses, senior high school AI electives, and maker competition entries.

　　Reference Price: \(80-\)120 (kit includes BeagleBone board + camera + sensors).

　　3. Advanced: For Senior High & University Maker Teams

　　▶ "Build Your Own Robot Friend" Open-Source Module (USC)

　　Core Open-Source Features:

　　Full-stack open-source (mechanical structure, firmware, and AI algorithms) from the University of Southern California.

　　Supports custom training of social assistive AI models (e.g., peer tutoring robots).

　　Education Empowerment:

　　Human-Centered AI: Explores AI ethics, human-AI interaction design, and machine learning model optimization.

　　Interdisciplinary Research: Combines computer science, psychology, and mechanical engineering.

　　Adaptable Scenarios: University maker labs, high school elite maker teams, and academic research projects.

　　Reference Price: \(100-\)150 (DIY materials; free open-source documentation and assembly manuals).

　　Buying Guide for Campuses

　　Target Group: Entry-level toys are tailored for primary and junior high school students (ages 8-14), while intermediate/advanced options are suitable for junior high, senior high, and university learners (ages 12+).

　　Teaching Goal: Entry-level products focus on AI enlightenment and basic coding skills, ideal for laying foundational knowledge; intermediate/advanced toys prioritize advanced AI practice and competition preparation, supporting in-depth project development.

　　Budget Priority: Entry-level toys emphasize low cost and bulk purchasability, fitting campus budget constraints for large-scale deployment; intermediate/advanced options strike a balance between performance and scalability, suitable for specialized courses or competitive teams.

　　Technical Foundation: Entry-level toys require no prior coding experience, with user-friendly interfaces for beginners; intermediate/advanced products recommend basic Python programming or electronics knowledge to fully utilize their functions.