What core competencies should middle school students possess to avoid being replaced by AI? In an era of rapid AI technological advancement, students born after 2010 are the new generation of tech natives. They are inspired by the movie “Ne Zha,” they interpret “The Three-Body Problem,” they explore the unknown using Deepseek, and they contemplate a future where 62% of emerging professions have yet to be defined. They will establish a “human intelligence enhancement loop,” using AI to process information and wisdom to create value. So what are they doing now? The “Biomimetic Dog” project at MIT Media Lab involves a team of middle school students who integrate knowledge of biology (biomimetic movement principles), mechanical engineering (3D printed joints), and programming (Arduino control) to create rescue robots capable of adapting to complex terrains. AI is responsible for path calculation, but the innovative architecture resulting from interdisciplinary collaboration exceeds algorithmic presets. At Singapore Chinese High School, students developed an emotional support chatbot as part of the “AI Psychological Counselor” practice, achieving a 92% accuracy rate in a 2022 campus stress survey, yet the actual usage rate was only 37%. Subsequently, a human mentor-led “emotional calibration group” was added, which improved system usage to 81% through facial recognition and empathy training. This shows that machines process data while humans handle emotional connections. In Helsinki, Finland, students in the “AI-Assisted Learning System” program use AI to push knowledge points based on incorrect answers in math learning, but they are required to submit a “learning strategy reflection log” weekly. Data shows that students with strong metacognitive abilities have a knowledge retention rate 58% higher than those who rely solely on AI recommendations, demonstrating the unique adaptability of human learning. At a middle school in Shanghai, students in the “AI Painting Workshop” use Midjourney to generate design sketches but must transform them using physical materials. The award-winning work in 2023, “Mechanical Butterfly,” used AI to generate concept images but achieved a three-dimensional texture through manual soldering of discarded circuit boards. This ability to convert “digital-physical” is something AI struggles to achieve. These cases reveal three essential transformations in education in the AI era: 1. From knowledge storage to thinking architecture (the irreplaceability of problem definition) 2. From singular skills to system integration (the value of interdisciplinary integration) 3. From passive acceptance to active creation (the necessity of practical innovation)
