Digital Intelligence Drives the Generation of New Quality Literacy: Constructing a Value-Added Evaluation System for University Teachers’ Deep Learning from a Complex Systems Perspective

Abstract

In response to the national strategy of integrated development of education, science, and technology, and talent, this study addresses the critical need to transform university teacher development from traditional “instrumental training” toward cultivating “new-quality competencies” aligned with new-quality productive forces. Grounded in complex systems theory, this research proposes a novel Deep Learning Value-Added Evaluation (D-VAE) framework to bridge theoretical and methodological gaps in defining and measuring teachers’ deep learning and competency growth. The study defines university teachers’ new-quality competencies as a five-dimensional structure comprising High-Consciousness Learning, AI Symbiosis, Transdisciplinary Integration, Pedagogical Innovation, and Ethical Responsibility. Methodologically, the study constructed a multi-layered D-VAE model integrating input, process, output, value-added, and contextual dimensions, supported by a 5×3×45 indicator cube with explicit data sources, calculation rules, and ethical review mechanisms. Utilizing longitudinal equating and hierarchical linear modeling, the framework enables full-chain estimation of teacher competency growth and teacher–student synergistic value-added. This research contributes theoretically by translating policy discourse into a measurable educational construct and offers a replicable system-level solution for teacher evaluation, promoting the transition from performance accountability to public good governance in higher education.