Nevercenter






Mbot | Electus

Aligns with CSTA K–12 CS standards and UNESCO AI literacy guidelines.

| Mode | Description | AI Concept Covered | |------|-------------|---------------------| | | Block-based (Scratch 3.0) control with AI assistance | Perception & action | | Coder | Python with pre-built AI functions (e.g., robot.detect_face() ) | Representation & learning | | Creator | Train custom models on edge (e.g., gesture recognition) | Reasoning & interaction | Mbot Electus

| Metric | Control | Mbot Electus | Effect size (Cohen’s d) | |--------|---------|--------------|--------------------------| | AI concept gain (%) | 12% | 41% | 1.8 | | CTS (creativity) | +0.3 | +1.2 | 1.4 | | Self-reported engagement | 3.2/5 | 4.7/5 | 1.6 | Aligns with CSTA K–12 CS standards and UNESCO

 Mbot Electus
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Aligns with CSTA K–12 CS standards and UNESCO AI literacy guidelines.

| Mode | Description | AI Concept Covered | |------|-------------|---------------------| | | Block-based (Scratch 3.0) control with AI assistance | Perception & action | | Coder | Python with pre-built AI functions (e.g., robot.detect_face() ) | Representation & learning | | Creator | Train custom models on edge (e.g., gesture recognition) | Reasoning & interaction |

| Metric | Control | Mbot Electus | Effect size (Cohen’s d) | |--------|---------|--------------|--------------------------| | AI concept gain (%) | 12% | 41% | 1.8 | | CTS (creativity) | +0.3 | +1.2 | 1.4 | | Self-reported engagement | 3.2/5 | 4.7/5 | 1.6 |