An Analysis of the Current Status of Classes and the Perception of Teachers on <Artificial Intelligence Mathematics> Subjects

Objectives This study aimed to investigate the current status and perceptions of the ‘<Artificial Intelligence Mathematics>’ curriculum, providing insights for the effective implementation of the curriculum in real-world settings and offering guidance for ongoing education for practicing teachers. Methods Based on a literature review, the research subjects were selected, and survey questions were developed through preliminary research and expert reviews. A total of 34 survey responses, excluding those with missing data, were analyzed using frequency analysis and independent sample t-tests. Interviews were conducted with two survey respondents, and relevant excerpts were extracted to supplement the analysis of survey results. Results Most teachers actively utilize engineering tools in their <Artificial Intelligence Mathematics> classes, but difficulties have been observed in the preparation and implementation of lessons involving the use of these tools. Teachers identified professional development and a conducive environment for using engineering tools as the most crucial support needed. Desired professional development topics included specific teaching and learning methods, effective utilization of engineering tools, principles of artificial intelligence, and the integration of artificial intelligence with mathematics. The perception of essential prerequisite courses was highest for <Mathematics> and <Mathematics II>. Conclusions Teachers express the need for enhanced support in professional development, particularly focusing on specific teaching and learning methods, the utilization of engineering tools, the principles of artificial intelligence, and the integration of artificial intelligence with mathematics as training topics. It appears necessary to strengthen support for workshops covering these areas. Improvement in the learning environment for utilizing engineering tools in classes related to <Artificial Intelligence Mathematics> is deemed essential. For effective teaching and learning in the <Artificial Intelligence Mathematics> curriculum, an appropriate learning path, at a minimum, involves progressing from <Mathematics> to <Mathematics I>, then to <Mathematics II>, and finally to <Artificial Intelligence Mathematics>.