COGNITIVE DIGITAL TWIN FRAMEWORKS FOR ARTIFICIAL INTELLIGENCE-POWERED PERSONALIZED LEARNING AND INTELLIGENT EDUCATIONAL ASSESSMENT

Abstract

The Conceptualization of Cognitive Digital Twins The concept of Cognitive Digital Twins (CDTs), dynamic and personalize computational counterpart of a learner which aims to predict her/his/their current cognitive state, knowledge, emotion and behavioral sequence. This paper reviews the underlying theory, computational architecture, and applications of CDTs for personalized learning and intelligent educational assessment. We start by surveying the evolution from conventional intelligent tutoring and shallow reactive adaptive systems to deep proactive AI driven “cognitive mirror” systems, which can digest real time multimodal data stream such as click streams,physiological responses and neuro-cognitive signatures for modeling. Then we propose a general architectural blueprint that consists of four integrated levels of data acquiring,semantic merging,cognitive modeling and pedagogical acting, backed by hybrid AI approaches,combining knowledge graphs, deep neural network,large language models (LLMs). The paper critiques existing knowledge tracing methodologies,namely Bayesian Knowledge Tracing,Deep Knowledge Tracing, Graph Knowledge Tracing and Forward-Looking Knowledge Tracing(FINER). In our evaluation, our novel FINER approach attains prediction accuracy of 78%–93%. We then discuss closed loop cognitive modeling and pedagogical actuation via reinforced learning policy generation and sensory modalities' multimodal orchestration to balance student’s skill performance with cognitive load management. Pilot applications across STEM disciplines,vocational learning and post graduate courses reveal that CDTs significantly improve learners performance by cutting down their “time-to-mastery” by up to 37%, prolonging knowledge retentions in up to 28% over 3 months period,and up to 43% cognitive load reduction. We delve into potential risks and opportunities such as cognitive privacy concerns, algorithmic discrimination, and shadow CDTs creation. Finally we consider future research including neuro-symbolic AI integration, multi-agent cognitive simulation as well as open semantic interoperability for pervasive CDTs ecosystems in higher education.