Title: Evaluation of classroom teaching effectiveness empowered by AI teachers based on bat algorithm random forest classification

Authors: Qingmei Lu; Junli Li; Yuan Gao

Addresses: School of Innovation and Entrepreneurship, North University of China, Taiyuan, 030051, China ' School of Innovation and Entrepreneurship, North University of China, Taiyuan, 030051, China ' School of Innovation and Entrepreneurship, North University of China, Taiyuan, 030051, China

Abstract: The large amount and complex types of data empowered by AI teachers in classroom teaching result in evaluation outcomes that fail to accurately reflect teaching effectiveness. To address this, research is conducted on evaluating the effectiveness of AI teacher-empowered classroom teaching based on bat algorithm-optimised random forest classification. First, an evaluation index system for AI teacher-empowered classroom teaching effectiveness is constructed, covering multiple dimensions. Second, variance inflation factor and principal component analysis are employed to screen indicators, ensuring efficiency. Finally, a bat algorithm-optimised random forest classification model processes complex data to build an accurate and highly generalisable evaluation model for teaching effectiveness. Results indicate that the method achieves a mean square error below 0.2, a Spearman rank correlation coefficient exceeding 0.95, and an evaluation time of up to 3.75 seconds.

Keywords: AI teacher empowering classroom; teaching effectiveness evaluation; random forest classification algorithm; bat algorithm.

DOI: 10.1504/IJCEELL.2026.152131

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.1/2, pp.138 - 153

Received: 03 Dec 2024
Accepted: 24 Sep 2025
Published online: 09 Mar 2026 *

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