Exploring the Development of Personalizing Learning in Chinese Higher Education: A Systematic Review of Cognitive Evolution Engine by AI

Abstract

The rapid digitalization of higher education has positioned the Cognitive Evolution Engine (CEE)—defined as an adaptive artificial intelligence (AI) system that dynamically models learner cognition and evolves through iterative optimization—as an emerging technology for personalized learning in China. This systematic review examines CEE applications through comprehensive analysis of 14 953 documents from IEEE Xplore, Scopus, Web of Science, and CNKI (2014–2024), with 94 studies meeting rigorous criteria for detailed content analysis. Our analysis reveals both significant progress and implementation challenges. CEE research has grown exponentially since 2019, particularly following China’s 2022 Digital Education initiatives. However, we identified notable gaps between theoretical concepts and practical implementations, with most current systems utilizing established AI technologies rather than fully realized cognitive evolution mechanisms. Geographic distribution analysis indicates that 58% of high-quality research originates from eastern institutions, highlighting regional disparities in research capacity. In addition, system transparency emerged as a key concern, with a majority of empirical studies acknowledging challenges in algorithmic interpretability. Based on systematic synthesis, we propose a 3-D framework integrating technological infrastructure, pedagogical principles, and implementation strategies adapted to Chinese educational contexts. This framework provides guidance for advancing from current AI applications toward authentic CEE systems. Our research contributes the first comprehensive analysis of CEE in Chinese higher education, offering evidence-based insights for enhancing personalized learning while addressing identified implementation challenges.