Digital Empowerment: Reconstructing the Developmental Evaluation Model for University Teachers’ Teaching Faculty

The strategic advancement of digital pedagogy presents a pivotal opportunity to resolve enduring contradictions within university teaching evaluation, specifically its administrative overemphasis and the consequent marginalization of developmental objectives. In response to national policy directives advocating for educational digitalization and Digital Empowerment Action for Teacher Development, this analysis critically deconstructs the constraints inherent in conventional evaluation frameworks. These limitations pertain to the homogenization of evaluators, simplification of evaluation content, superficial application of data, and a predominant managerialist orientation. The study aims to formulate a novel paradigm for developmental evaluation, intrinsically powered by digital technologies and fundamentally oriented toward the sustained professional growth of instructors. By architecting a synergistic framework incorporating multi-source evidence aggregation, intelligent diagnostic analytics, and personalized feedback loops, the model institutes a recursive, ascending cycle of “evaluation, diagnosis, enhancement, and re- evaluation.” This structure enables a foundational transformation in the evaluation paradigm, shifting its core function from selective judgment to developmental guidance. The Findings indicate that a digitally-empowered developmental evaluation system can effectively catalyze professional self-directedness among faculty. It achieves three critical transformations: from singular judgment to pluralistic development; from static appraisal to dynamic growth; and from external constraint to internal motivation. The study contributes both theoretical and practical scaffolding for the reform of instructional evaluation in higher education. It enriches teacher development theory by integrating an educational evaluation perspective and offers an actionable framework for resolving the long-standing tension between evaluation and development. Future research should explore deeper AI applications, disciplinary adaptability, and the ethical governance of evaluation data to further refine this paradigm.