Abstract
In this study, ‘data collection’, ‘data expression’, ‘data analysis, and ’optimization and decision-making’ were selected as the core AI concepts to be dealt with in the mathematics for AI education. Based on this, the degree of reflection of AI core concepts was investigated and analyzed compared to the mathematical core concepts and content of each area of the elective course. In addition, the appropriateness of the content of <AI Mathematics> was examined with a focus on core concepts and related learning contents. The results provided some suggestions for answering the following four critical questions. First, How to set the learning path for <AI Mathematics>? Second, is it necessary to discuss the redefinition of the nature of <AI Mathematics>? Third, is it appropriate to select core concepts and terms for <Artificial Intelligence Mathematics>? Last, is it appropriate to present the relevant learning contents of the content system of <AI Mathematics>?
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
