Title: A quantitative evaluation of teaching quality in higher education institutions assisted by artificial intelligence

Authors: Fanqi Meng

Addresses: School of Fine Arts and Design, Mudanjiang Normal University, Mudanjiang, Heilongjiang Province, 157000, China

Abstract: A quantitative evaluation method for teaching quality in universities with the assistance of artificial intelligence is proposed to address the problems of large mean absolute error and low accuracy of evaluation results in existing methods. Firstly, teaching data is collected, including teacher basic information, course information, teaching arrangements, and student feedback, and pre-processed. Data features are extracted from processed data and then processed through dimensionality reduction techniques. Secondly, a neural network evaluation model is constructed by determining the number of nodes in the input layer, hidden layer, and output layer, with a linear function selected as the activation function. Finally, the learning rate and loss function of the model are dynamically adjusted, and the optimised neural network model is used to quantitatively evaluate the teaching quality of universities. The experimental results show that the evaluation results of this method are more accurate and reliable.

Keywords: artificial intelligence; university teaching; quality evaluation; neural network; feature dimensionality reduction; L1 regularisation.

DOI: 10.1504/IJCEELL.2026.152138

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.1/2, pp.205 - 221

Received: 27 Dec 2024
Accepted: 07 Oct 2025
Published online: 09 Mar 2026 *

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