AI music teaching innovation research based on artificial intelligence technology
With the rapid development of artificial intelligence technology, the application of AI technology in the field of music teaching has attracted more and more attention, but there is a problem that innovation and optimization are not ideal. The traditional music teaching mode cannot solve the problem...
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| description | With the rapid development of artificial intelligence technology, the application of AI technology in the field of music teaching has attracted more and more attention, but there is a problem that innovation and optimization are not ideal. The traditional music teaching mode cannot solve the problem of innovation and optimization in the field of music education, and the evaluation is unreasonable. Therefore, this paper proposes a neural network algorithm for innovative optimization teaching innovation analysis. Firstly, based on pedagogical theory, the existing AI music teaching methods are analyzed in order to scientifically classify the teaching quality evaluation requirements, so as to reduce the undesirable factors in teaching innovation. Secondly, pedagogical theory can be used to evaluate the quality of AI music teaching, formulate corresponding teaching quality evaluation schemes, and comprehensively analyze the results of innovative practices in teaching. The results show that under the same evaluation criteria, the neural network algorithm is superior to the traditional music teaching mode in terms of quality evaluation accuracy and innovative effect of AI music teaching. |
| doi_str_mv | 10.1063/5.0230283 |
| format | Conference Proceeding |
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The traditional music teaching mode cannot solve the problem of innovation and optimization in the field of music education, and the evaluation is unreasonable. Therefore, this paper proposes a neural network algorithm for innovative optimization teaching innovation analysis. Firstly, based on pedagogical theory, the existing AI music teaching methods are analyzed in order to scientifically classify the teaching quality evaluation requirements, so as to reduce the undesirable factors in teaching innovation. Secondly, pedagogical theory can be used to evaluate the quality of AI music teaching, formulate corresponding teaching quality evaluation schemes, and comprehensively analyze the results of innovative practices in teaching. 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The traditional music teaching mode cannot solve the problem of innovation and optimization in the field of music education, and the evaluation is unreasonable. Therefore, this paper proposes a neural network algorithm for innovative optimization teaching innovation analysis. Firstly, based on pedagogical theory, the existing AI music teaching methods are analyzed in order to scientifically classify the teaching quality evaluation requirements, so as to reduce the undesirable factors in teaching innovation. Secondly, pedagogical theory can be used to evaluate the quality of AI music teaching, formulate corresponding teaching quality evaluation schemes, and comprehensively analyze the results of innovative practices in teaching. The results show that under the same evaluation criteria, the neural network algorithm is superior to the traditional music teaching mode in terms of quality evaluation accuracy and innovative effect of AI music teaching.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Innovations</subject><subject>Music</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Pedagogy</subject><subject>Quality assessment</subject><subject>R&D</subject><subject>Research & development</subject><subject>Teaching methods</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNotkE1LAzEQhoMoWKsH_8GCN2Frkkmym2MpWgsFEXrwFrL5aFO22ZrsCv33rrangZdn5mUehB4JnhEs4IXPMAVMa7hCE8I5KStBxDWaYCxZSRl83aK7nPcYU1lV9QR9zlfFYcjBFL3TZhfitggxdj-6D10skstOJ7MrGp2dLcZEpz74YIJuR653bRu2Lho3bptd7Npue7pHN1632T1c5hRt3l43i_dy_bFcLebr8igASitk44FR4Bobq5231rO6Jkwy7rUXhIiGGipt5YWHxjoQnElCsDeM1NzAFD2dzx5T9z243Kt9N6Q4NioYTdSkElyO1POZyib0_z-pYwoHnU6KYPVnTHF1MQa_3jpd8Q</recordid><startdate>20240919</startdate><enddate>20240919</enddate><creator>Yun, Gao</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20240919</creationdate><title>AI music teaching innovation research based on artificial intelligence technology</title><author>Yun, Gao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p633-d69bf34235a0cdaefddf48814945faf6116b2c29d7f6f3bde36549110fc4185c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Innovations</topic><topic>Music</topic><topic>Neural networks</topic><topic>Optimization</topic><topic>Pedagogy</topic><topic>Quality assessment</topic><topic>R&D</topic><topic>Research & development</topic><topic>Teaching methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yun, Gao</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yun, Gao</au><au>Shukla, Ankita</au><au>Narayanaswamy, Nagesh Kallollu</au><au>Mishra, Brijesh</au><au>Singh, Vivek</au><au>Dwivedi, Ajay Kumar</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>AI music teaching innovation research based on artificial intelligence technology</atitle><btitle>AIP conference proceedings</btitle><date>2024-09-19</date><risdate>2024</risdate><volume>3131</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>With the rapid development of artificial intelligence technology, the application of AI technology in the field of music teaching has attracted more and more attention, but there is a problem that innovation and optimization are not ideal. The traditional music teaching mode cannot solve the problem of innovation and optimization in the field of music education, and the evaluation is unreasonable. Therefore, this paper proposes a neural network algorithm for innovative optimization teaching innovation analysis. Firstly, based on pedagogical theory, the existing AI music teaching methods are analyzed in order to scientifically classify the teaching quality evaluation requirements, so as to reduce the undesirable factors in teaching innovation. Secondly, pedagogical theory can be used to evaluate the quality of AI music teaching, formulate corresponding teaching quality evaluation schemes, and comprehensively analyze the results of innovative practices in teaching. The results show that under the same evaluation criteria, the neural network algorithm is superior to the traditional music teaching mode in terms of quality evaluation accuracy and innovative effect of AI music teaching.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0230283</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2024, Vol.3131 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
| recordid | cdi_proquest_journals_3106817659 |
| source | AIP Journals Complete |
| subjects | Algorithms Artificial intelligence Innovations Music Neural networks Optimization Pedagogy Quality assessment R&D Research & development Teaching methods |
| title | AI music teaching innovation research based on artificial intelligence technology |
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