Introduction
The transition from general large models to specialized large models in the field of education is an inevitable trend in the deepening development of AI large model technology. Educational large models are not merely fine-tuning and optimizing general large models, but rather a systemic transformation aimed at reconstructing the future educational landscape, based on open algorithm model architecture, and centered on innovative educational application scenarios. Clarifying the conceptual connotations of educational large models and designing system architectures based on the essence of technology to further create new educational application scenarios has become a critical issue related to the digital transformation and intelligent upgrading of education in this era.
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Core Related Concepts
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AIGC
AIGC refers to the automatic generation of various forms of content through intelligent technologies such as supervised learning, reinforcement learning, pre-trained models, and natural language processing, based on learning and training from existing data.
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Generative Artificial Intelligence
Generative artificial intelligence is a technology that automatically generates response content based on natural language dialogue prompts.
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Large Models
Large models refer to artificial intelligence models with billions to hundreds of billions or even more trainable parameters, which are the result of the joint development of deep learning, GPU hardware, large-scale datasets, and other intelligent technologies.
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Educational Large Models
Educational large models are artificial intelligence models suitable for educational scenarios, characterized by ultra-large scale parameters, and trained by integrating general knowledge and specialized knowledge. They are an integration of large model technology, knowledge base technology, and various intelligent educational technologies, capable of promoting bidirectional construction of human learning and machine learning.
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Typical Application Cases
Globally, educational large models are undergoing extensive and in-depth exploration and development, having already formed solutions in areas such as speaking practice, mathematics learning, sentiment analysis, and personalized recommendations. This research outlines five typical application cases of educational large models (as shown in Table 1), analyzing their application scenarios, technological progress, and existing shortcomings.
Table 1 Typical Application Cases of Educational Large Models
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Open Innovative Architecture
Educational large models are based on general large models, connecting various educational digital applications, continuously training educational scenario models, and constantly improving the ability to solve educational professional tasks. The open innovative architecture of educational large models is divided into three layers: basic capability layer (L0), professional capability layer (L1), and application service layer (L2), as shown in Figure 1.
Figure 1 Open Innovative Architecture of Educational Large Models
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Application Prospects
Educational large models will promote the digital transformation and intelligent upgrading of education from three aspects: learning spaces, learning resources, and teacher roles, forming a new educational ecology of human-machine collaboration and symbiosis.
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Interactive Generation of Learning Spaces
With the support of educational large models, learners can obtain learning support and create learning outcomes through human-machine interaction, constructing personal and collective learning spaces, forming learning scenarios that integrate physical and online spaces, allowing all learners to access any required information anytime and anywhere.
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On-Demand Supply of Learning Resources
Leveraging the learning analytics capabilities of educational large models, the gap between the demand and supply sides of educational resources can be narrowed, providing personalized learning resources for learners and addressing the issue of matching high-quality educational resource supply with learning demand.
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Transformation and Upgrade of Teacher Roles
Educational large models will gradually replace repetitive and inefficient educational labor, enhancing the scientific and creative nature of educational work, and promoting teachers from being “experts in teaching” to “experts in learning,” providing personalized support for each learner through creative teaching design.