Enhancing core competencies in digital education by multimodal large language models

Abstract

Under the background of digital transformation of education, it has become a key issue to cultivate the core competence of digital teaching. However, the traditional digital teaching has limitations in situation creation, cognitive interaction and ability evaluation, and it is difficult to effectively support the cultivation of students’ higher-level ability. Multimodal Large Language Model (MLLM) provides a new opportunity to meet these challenges. Based on this, this study constructs a multimodal cognitive enhancement teaching model, which consists of three interrelated modules: multimodal situation generation, adaptive cognitive interaction and core competence evaluation. In this study, 120 college students participated in a 16-week quasi-experiment. The experimental group used the teaching platform based on MLLM, while the control group used the traditional online platform. The data were analyzed by statistical methods such as covariance analysis. The results show that the experimental group has significantly improved in the four core competence dimensions, and the adoption rate of innovative solutions has increased by 32.85% points. The “text + voice + animation” scenario showed the best performance, the accuracy of adaptive interaction strategy selection reached 92.37%, and the assessment module aligned with expert ratings at over 84%. These findings show that the proposed model effectively supports the high-quality development of core competence in digital teaching. It provides a theoretical framework, practical path and evaluation tool for cultivating people-artificial intelligence cooperative literacy, and provides a valuable reference for the digital transformation of education supported by artificial intelligence. Not applicable.