Research on Comprehensive Classroom Evaluation System Based on Multiple AI Models
Cong Xie, Li Yang, Daben Wang, Jing Xiao

TL;DR
This paper proposes a comprehensive AI-based classroom evaluation system utilizing image, speech, and language recognition to automate and improve the objectivity and efficiency of teaching quality assessment.
Contribution
It introduces a novel multi-modal AI evaluation framework that automates report generation and provides optimization suggestions for classroom teaching.
Findings
Automates classroom evaluation with AI technologies.
Improves objectivity and efficiency of teaching assessments.
Provides a data-driven approach for teaching improvement.
Abstract
The promotion of the national education digitalization strategy has facilitated the development of teaching quality evaluation towards all-round, process-oriented, precise, and intelligent directions, inspiring explorations into new methods and technologies for educational quality assurance. Classroom teaching evaluation methods dominated by teaching supervision and student teaching evaluation suffer from issues such as low efficiency, strong subjectivity, and limited evaluation dimensions. How to further advance intelligent and objective evaluation remains a topic to be explored. This paper, based on image recognition technology, speech recognition technology, and AI large language models, develops a comprehensive evaluation system that automatically generates evaluation reports and optimization suggestions from two dimensions: teacher teaching ability and classroom teaching…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsEducational Technology and Pedagogy · Advanced Technologies in Various Fields · AI and Multimedia in Education
