1. environmental construction: create an intelligent and efficient digital teaching environment.
The integrated application of artificial intelligence technology can provide an intelligent, immersive and multi-dimensional digital support environment for teachers and students to carry out teaching innovation practice. Build a smart classroom with online and offline integration, virtual reality and dual integration, and a digital learning platform with "cloud integration" to create a learning space with organic integration of physical space, resource space and social space.
Build a ubiquitous learning environment after class, fully support the organic integration of learning resources, learning activities, learning processes and learning data, and provide full-process, intelligent and personalized services for course teaching, after-school service, teacher training and teaching reform practice.
2. Resource sharing: Strengthen the construction and application of digital curriculum resources.
Actively explore the use of artificial intelligence technology to build a digital resource platform, design, develop and integrate digital curriculum resources such as curriculum resources, thematic resources and expanded resources, and use the intelligent matching analysis function of big data to screen and recommend the best resource reference for teachers.
For example, the "National Public Service Platform for Smart Education" launched by the state provides rich curriculum resources and teaching services for primary and secondary school teachers and students, encourages teachers to actively learn from the platform resources, enables students to carry out online autonomous learning, provides employment services for college students, and promotes the reconstruction of teaching organizations and the innovation of teaching methods.
3. Evaluation management: realize data mining and analysis in the teaching process.
With the help of intelligent technology to support innovative applications and services in the whole process of teaching and learning, through the process of data clustering, data cognition and decision optimization for big data, the disadvantages of traditional decision-making that rely too much on practical experience and lack of data support and analysis are overcome.
Data analysis is used to characterize students' comprehensive quality evaluation, teachers' professional ability development and teaching application effect. , so that teachers and students can make wise evaluation and decision, and provide effective support for accurately evaluating and improving the quality of education and teaching.