Core Insights - The traditional vocational education sector faces systemic challenges in integrating industry and education, flexible teaching methods, and precise talent evaluation due to the urgent need for industrial intelligence upgrades. The integration of artificial intelligence (AI) offers a transformative solution aimed at constructing a new digital smart education ecosystem [1] Group 1: Digital Infrastructure Transformation - The transition from "connection" to "empowerment" in digital infrastructure is essential, requiring the construction of a perceptive, intelligent, and integrated digital foundation. This foundation will enable seamless data collection, integration, and intelligent analysis across teaching, management, and training scenarios [2] - The digital foundation consists of three key layers: a ubiquitous sensing layer, an intelligent computing layer, and a data hub layer, which collectively aim to make data the core element driving educational activities [2] Group 2: Teaching Paradigm and Stakeholder Relationships - The core of the new ecosystem lies in the transformation of teaching paradigms and the systemic restructuring of relationships among teaching, learning, evaluation, and production. AI facilitates a shift from a teacher-centered to a student-centered and scenario-driven approach [3] - The evaluation method transitions from focusing on final results to formative assessments based on comprehensive data, evaluating competencies such as skill proficiency and problem-solving strategies [3] - The traditional "teacher-student" binary structure evolves into a multi-faceted collaborative ecosystem involving teachers, AI, students, and enterprises, enhancing the connection between education and industry dynamics [3] Group 3: Governance and Sustainability Mechanisms - A healthy and sustainable digital smart education ecosystem requires a modern governance mechanism that is data-driven, agile, and open to co-governance. This includes transitioning from hierarchical management to precise governance based on data insights [4] - Establishing a risk prevention system that encompasses data security, ethical review, and algorithm accountability is crucial, particularly concerning student privacy and algorithm fairness [4] - Designing open collaborative incentive mechanisms, such as credit banks and micro-certifications, is essential to attract continuous participation from industry and research institutions, fostering a cycle of co-construction and optimization [4] Group 4: Balancing Technology and Educational Essence - Building a digital smart vocational education ecosystem is a long-term and systematic endeavor that requires balancing technological adoption with respect for educational principles. The application of technology must promote holistic human development and ensure educational equity [5] - It is important to avoid the "technological determinism" trap, which could narrow educational goals and impact interpersonal interactions. Humanistic care and ethical considerations should be integrated into technology system design [5] Group 5: Collaborative Progress for Ecosystem Maturity - The maturation of this ecosystem necessitates the collaboration of policy guidance, practical exploration, and academic research, enabling vocational education to empower students' lifelong development and support high-quality growth in the real economy [6]