Research on the Development of New Media Dance Basic Education for Dance Majors in Chinese Universities in the Era of Artificial Intelligence
Pubblicato online: 24 set 2025
Ricevuto: 30 dic 2024
Accettato: 21 apr 2025
DOI: https://doi.org/10.2478/amns-2025-0979
Parole chiave
© 2025 Yangyang Chen, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
In recent years, with the rapid development of digital technology, new media has become an important mode of communication and artistic expression. The traditional way of stage performance can no longer meet the aesthetic needs of modern audiences, and new media technology has brought a broader space for the development of the dance performance profession, which can create diversified stage effects through video, virtual reality and augmented reality and other technological means [1-3]. In the era of artificial intelligence, dance performance majors can be integrated with new media to adapt to the changes of the times and meet the needs of the audience.
On the one hand, the integration of dance majors in Chinese colleges and universities with new media can improve the teaching effect of basic dance education. Through the application of new media technology, teachers can record and upload dance teaching videos to online platforms so that students can arrange learning activities according to their personal time, making learning more flexible and convenient [4-5]. New media can also help teachers to better manage and assess students’ learning, to understand students’ dance learning status in time, so as to carry out guidance and assistance in a targeted manner, and to enhance the effect of dance teaching [6-8]. On the other hand, the integration of dance majors and new media in Chinese colleges and universities can strengthen students’ dance creativity [9]. The new media platform provides students with more diversified inspiration for dance creation, and students can appreciate various styles and forms of dance works online, absorb the essence, and obtain creativity and inspiration from them [10-12]. At the same time, new media also provide students with more communication and learning opportunities, students can watch others’ works, broaden the creative ideas and techniques, and constantly improve their own dance creation level [13-15].
Literature [16] studied the impact of new media education model on students’ artistic literacy, new media dance basic education combined with mobile network, intelligent communication and other means to improve students’ artistic literacy, and accelerate the realization of college students’ employment goals. Literature [17] found that combining new media technology with dance teaching practice not only improves students’ learning interest, but also enhances the quality of teaching, so it analyzes the application and practical ability of new media in dance teaching from the perspectives of improving the teaching content system and enriching teachers’ multimedia literacy. Literature [18] examines the application of digital media technology in dance art education, which is integrated into the content of live art and works through multi-dimensional digital expression to create a stronger aesthetic experience, but also gives new vitality to the dance performance. Literature [19] used digital media technology to analyze the connection between dance students’ physical quality and their special performance, respectively, using regression analysis and neural network prediction model to carry out the research, and found that the constructed neural network model has more reliability and less error in the prediction of dance performance and physical quality development level. Literature [20] assessed the changes in dance technology in basic dance education, compared with the traditional form of education, the popularization of the Internet allows young dancers to have a rapid communication and interconnection of the carrier support, colleges and universities should be quickly introduced to adapt to the social media “creator economy” innovative teaching mode to adapt to the future of dance education. Literature [21] shows that the rational use of new media technology can broaden the development and change of dance art, and make outstanding contributions to the recording, dissemination and teaching of dance art. Literature [22] proposes an interactive dance learning application of virtual reality, which can significantly improve students’ dance training and learning effects by establishing an interactive virtual partner, giving appropriate visual and tactile feedback, and a game mechanism with dance tasks.
In this paper, in the context of new media, artificial intelligence and other technologies are combined with basic dance teaching, which provides a new way to optimize and upgrade the teaching of national dance and deepen the reform of dance teaching. Artificial intelligence technology can be combined with shape context shape context algorithm and DTW dynamic regularization algorithm to identify the dancer’s posture and skeleton key points. Through the accurate skeleton matching results, the similarity of the dancer’s body lines, angles and positions, keyframe times and rhythms are calculated, and then movement analysis and rhythm analysis are carried out. The quality of the dancer’s movements is evaluated by comparing the dancer’s movements with standard movements. This artificial intelligence technology is applied to actual dance teaching and controlled experiments are used to verify its impact on learning efficiency and learning effectiveness.
First of all, from the perspective of communication methods, new media have opened up richer channels for the dissemination and popularization of dance. For the audience, this includes the convenience of time and space - the update of the media allows people to no longer limit themselves to the option of “watching live in real time”, but to freely choose the time and place of enjoyment. This diversification of communication channels is also reflected in the transformation of dance through new media. In the public’s view, dance works in the theater have been classified as “elite art” for a long time, and its “niche” different from the art of film also makes a considerable part of the audience “stay away from “. But with a series of television dance competitions, dance variety shows and dance culture programs “out of the circle”, the art of dance gradually into the thousands of households.
Secondly, new media has also provided technical support for the development of dance, turning once unattainable ideas into reality. For example, the combination of dance and artificial intelligence, the combination of dance and digital image, the combination of dancers in immersive space and interactive projection. Although the technical application of new media dance in China is still immature, it is conceivable that the combination of new media and dance art will be an important direction of outward development and inward integration of dance art for a long time to come.
New media technology is conducive to the narrative needs of Chinese national dance. It can not only create realistic and delicate artistic conception, set off dancers, digitize dancers, but also decorate the stage space, expand the stage space, and make the audience feel the changes at multiple levels and angles. The dance work “The Love of Birds” in the CCTV Spring Festival Gala uses holographic technology to project the picture of the deep jungle and the wonderland of other worlds on the background screen, without covering the light of the dancers. The interaction between the image and the dancers in the big screen at the end of the dance presents the glorious scene of the peacock. The audience can feel the vitality of nature and creatures from the interactive technology of figure synchronization, and experience the traditional ethnic people Between the exquisite dance.
According to the legend in the dance drama “Heaven in Heaven”, the scene of the celestial goddess coming down to earth appeared on the birth day of Tsongkaba. For the design of this scene, the main creators used a dancer to pose different dance styles for real shooting, and then copied a celestial goddess into a dozen by copying and pasting the images of auspicious clouds and lotus flowers, which not only improved efficiency but also saved the budget. Computer technology and video technology create the possibility of the combination of virtuality and reality for the stage. Compared with the traditional theater performance, the artistic visual experience exceeds the audience’s previous experience, making the audience concentrate on appreciating and pondering what is going on, breaking the sense of distance between them and the stage.
The new media stage play “Legend of the Warrior Monk” at the Shanghai World Expo mainly spreads traditional Chinese culture through a series of stories about Shaolin monks practicing martial arts and practicing zen. The projection media and stage installation in the play are completed by using the gray rockery stone in the back area of the stage, the front area and the side curtain. When the projection is not needed in the picture, the rockery is used as the purpose of accounting for the environment. The beginning of each piece of the work is narrated through video, sometimes explaining the context of the next performance, and sometimes using flashback to pull the end of the story back to the time and space of the live performance, which is of great help to the audience to understand the content of the work.
The new media stage play “White Snake: Human Apocalypse” transforms Xu Xian into a “bionic man”, Fa Hai into a system rule, and White Snake comes to the AI world to find Xu Xian. This presentation mode can be said to break the expression form of traditional dance art and the inherent image in traditional culture, which not only attracts the audience, but also challenges the audience and brings the audience a new experience.
The new media stage play “Tang Palace Banquet” adopts the method of combining virtual scenes and real stage to present the magnificent Tang Dynasty palace. With the support of high-tech, “Tang Palace Banquet” adopts multi-scene mixed shooting, the creator built a blue-screen “square box” in the studio, and the actors danced on the blue screen, and then displayed it after the post-stage image cutting and synthesis. The other part is in the studio for real scene shooting, mixed cut and multi-angle shooting, the most impressive number to the final Angle shooting. “Tang Palace Banquet” has raised the audience rating of Henan Satellite TV in a straight line, enhanced the public’s attention to dance, promoted traditional culture, promoted the surrounding cultural creation, and improved the popularity of dance.
The success of the above cases has brought new ideas to the innovation of national dance basic education. The introduction of new media technology into dance teaching can not only improve the quality of dance teaching, but also promote students’ interest in learning.
The application of artificial intelligence technology in dance science is constantly developing and innovating. It provides coaches, athletes and spectators with more personalized and intelligent services and experiences, and promotes the progress and development of sports science. Artificial intelligence technology is able to analyze and mine a large amount of sports data to provide key indicators such as athletes’ movement trajectory, speed, and power. This helps coaches and sports scientists understand athletes’ performance, identify potential strengths and problems, and develop appropriate training programs for them.
Artificial intelligence technology can recognize and analyze a dancer’s movements to assess their accuracy and strengths and weaknesses. This helps dancers understand their performance issues and make targeted training adjustments. Some systems can also provide real-time feedback and guidance to help dancers make timely improvements. Additionally AI technology can use deep learning models or pose estimation algorithms to identify key points in a dancer’s posture and bones. Through accurate posture recognition, AI technology can analyze the dancer’s body lines, angles, and positions to provide dancers with feedback on posture improvement and optimization.
Artificial intelligence technology can provide inspiration and suggestions for dance creation and choreography based on dance rules and styles. By learning and analyzing a large number of dance works, the AI system can generate new movement combinations, music coordination and spatial layout, providing choreographers with creative aids. The current related literature presented in Dance Creation and Choreography.
Intelligent Dance Evaluation Process As shown in Figure 1, AI technology helps provide more objective and fair scoring in dance competitions. By analyzing dancers’ movements, postures, and skill levels, AI systems can generate scores and feedback to help judges make accurate judgments. This helps improve the fairness and credibility of the competition. Firstly, in terms of movement accuracy assessment, AI technology can assess the accuracy of dancers’ movements by recognizing and analyzing their movements. It can compare the dancer’s actual performance with the preset standard movements and key points, determine whether there are deviations or errors, and give the corresponding scores and feedback. Artificial Intelligence The intelligent evaluation system will focus on the standardization and normalization required by the dancers, supplemented by artistry and extensibility, and provide professional guidance, standardized evaluation, and vicarious simulation to dancers with different degrees and needs. To achieve the goal of assisting in teaching and collaborating in training, so that dancers can benefit from interest and progress in convenience, and science and technology can empower the art of dance.
Let the artificial intelligence intelligentized national dance evaluation system be constantly precise and professionalized, and gradually lean the feedback on the body art emotional expression with efficient and precise body and facial recognition technology. At the same time, by continuously enhancing the system’s mastery of the emotional characteristics of national dance movements, it gradually improves the sensitivity to the emotional recognition of movements in specific styles, in addition, it combines the systematic information integration of the artistic expression of body
Design map of AI intelligent national dance evaluation system
Personalized teaching, as shown in Figure 2, in the traditional large classroom, each student has great individual differences, and the teacher is very likely to lack of individual attention. Therefore, the dance practice class should be led by the teacher, information technology to establish individual student database, control the teaching progress, collect individual feedback information, in the classroom for point-to-point teaching at the same time, the artificial intelligence can use more eyes to see more students, assisting the teacher to teach, so as to achieve the real tailored to the needs of the students. Individuality is the life of art, the specialization of art also requires the personalization of art education, only personalized art education, in order to make the art alive Artificial Intelligence + education, in addition to using advanced technology to promote education, but also to use the thinking of Artificial Intelligence to look at the dance education, the supply side of the teaching (teachers) need to innovate the teaching methods, and actively explore the personalized teaching methods, to further widen the demand side (students) ) learning styles. Based on the teaching platform of artificial intelligence + Internet, innovative teaching methods and content to improve students’ learning interest and learning initiative.
Realize personalized teaching
The skeleton matching process combines the shape context algorithm and the DTW dynamic regularization algorithm, firstly inputs two sets of skeleton joint point data sequences, and then uses the shape context algorithm as the “distance” measurement algorithm, and the similarity of the skeleton shape composed of the two skeleton joint points as the “distance” in the DTW dynamic regularization process, and calculates the distance matrix between each element of the two sets of sequences, so as to measure the accuracy of skeleton matching. And the distance between the two sets of sequences can be calculated more accurately, so as to better achieve the purpose of skeleton matching.
1) Shape Context module
When using Shape Context to extract the features of a shape, you can first intercept the smallest region of the image that contains the contour, then sample N points of the contour in this region, and finally scale the coordinates of these points keeping the aspect ratio to the [0-1] interval to complete the scale normalization.
When using Shape Context to match a shape, it extracts a series of vectors from the points around each point based on its characteristics to better represent that point. So each shape is represented by N histogram vectors, and the distance between the vectors is measured. And the chi-square statistic is used to compute the similarity measure matrix
2) DTW dynamic regularization module
The dynamic time regularization algorithm is an effective optimization technique that uses a time distortion function W(n) that satisfies certain conditions to describe the temporal matching between the test sequence and the reference sequence, and solves the distortion function corresponding to the minimum cumulative distance to complete the matching of the two sequences.
Suppose there are two time sequences
If
In order to realize the purpose of aligning two sets of sequences, it is required to construct a matrix of
This module aims to find a shortest path from (1,1) to
The correspondence between
Steps of movement analysis: input skeleton matching results, calculate the similarity between the skeleton joints of the student and the skeleton joints of the teacher, use the ratio of the number of qualified frames as the student movement score, and calculate the rhythm score based on the matching results. A threshold score is set for the deviation value of the skeleton joints, and the matched frames whose deviation value exceeds this threshold will be labeled as unqualified frames. Finally, the ratio of qualified frames to the total number of frames is counted as the action score. Assuming that the student’s video extracted
In the traditional dance classroom, students, as teaching participants, spend most of their time in passive acceptance of dance learning and movement instruction. By introducing AI technology into the dance classroom, it is hoped that AI technology can effectively enhance user services and positively help students’ dance performance and learning efficiency.
In order to understand the impact of the participants’ learning efficiency and learning effectiveness after using AI technology within the national dance classroom, this experiment was designed as a group control experiment, and the impact of the movement scoring technology on the participants was discussed and investigated. The main discussion questions in this section of the experiment are (1) how artificial intelligence technology affects the participants’ dance learning efficiency and whether it can make the participants consume more physical energy. (2) Whether the intervention of AI technology in dance teaching activities will enable trainees to get better dance results.
The data of this experiment relied on an organization’s AI national dance teaching special training activity, which mainly took a control approach by dividing the participants and teachers into two groups, one group adopting AI-assisted dance teaching (Group A), and the other group adopting traditional dance teaching (Group B), with the aim of exploring whether the AI technology has an impact on the dance teaching participants. After collecting the participants’ physical data, the data were counted and analyzed, and based on the statistical results, the impact of AI technology on the participants was explored, providing data support for the subsequent analysis of how the organization will use AI to enhance user services.
In national dance instruction, participants will complete their dance learning by imitating the movements in the classroom, and will continue to practice in the process, thus expending physical energy. Therefore, calorie data is an indicator of the effectiveness of the participants’ dance learning. Experiment through the GT3 accelerometer equipment to collect 60 trainees 7 sessions of calorie consumption, the two groups of trainees in this special training activities in total consumption and knowledge of calorie data, statistical data as shown in Figure 3, both groups of data are not normally distributed. In terms of the overall calorie consumption of the two groups of trainees, there is a significant difference between Group A, which uses artificial intelligence, and Group B, which uses traditional teaching.
The overall calorie consumption of the participants in the process of learning the knowledge points of dance.The mean value of calorie consumption of the participants in group A was 1001.75±43.25 calories, and the mean value of calorie consumption of all the knowledge points of the participants in group B was 1020.86±56.96 calories, which is a significant difference between the two groups of data (p=0.01).
Overall consumption and knowledge point consumption contrast
After 12 weeks of teaching experiments, the changes of each ability are shown in Table 1, which can be found that the application of AI-assisted teaching has significantly improved the learning effect of the students in Group A. Except for the purpose situation and the subjective criterion, the P-value of which is greater than 0.05, the rest of the dimensions are significantly different. Especially in the development of learning quality, students in group A performed better than group B, which indicates that AI technology plays a crucial role in the improvement of learning effect. Advances in AI teaching have brought about a dramatic change in traditional educational methods, enabling students to break free from established thinking frameworks and to be more accessible to new things. This results in more positive learning methods and attitudes among students. Teaching with artificial intelligence can significantly improve the effectiveness of national dance classroom teaching in general higher education institutions and can promote students’ literacy development. Through the 12-week teaching experiment, it can be found that the use of artificial intelligence teaching can significantly improve the students’ learning performance, especially in the quality shaping, after the application of artificial intelligence teaching, the performance of the students in group A significantly exceeds that of group B, proving that the application of artificial intelligence teaching can significantly improve the learning effect. With the development of artificial intelligence technology, the traditional teaching mode has been challenged, and students are no longer limited by inherent thinking stereotypes or lack of freshness, so the students’ attitude toward exercise has become better and better. The learning resources of artificial intelligence teaching are rich, so that students can better master the knowledge of national dance. The AI educational environment enables students to express students’ thinking and opinions more autonomously. In addition, group activities and multiple competitions make the lessons more lively and interesting, providing a good foundation for students’ literacy development.
Test of quality change independent sample T
| Dimension | A group | B group | T | P |
|---|---|---|---|---|
| Action index | 24.46±4.38 | 22.92±3.32 | -0.635 | 0.04 |
| Purpose | 40.38±339 | 38.46±4.81 | 0.279 | 0.069 |
| Action consciousness | 22.08±3.17 | 21.16±3.36 | -0.927 | 0.048 |
| Action habit | 31.28±3.28 | 30.05±4.45 | -1.596 | 0.012 |
| Action intention | 23.48±3.42 | 22.67±3.58 | -1.608 | 0.025 |
| Emotional control | 31.15±3.57 | 30.14±3.86 | -0.856 | 0.015 |
| Behavior control | 23.96±3.14 | 25.36±3.78 | 1.512 | 0.046 |
| Subjective standard | 20.96±3.85 | 20.17±3.42 | 0.308 | 0.055 |
Independent Learning Conscientiousness Independent Sample T-test results are shown in Fig. 4. In the 12-week teaching experiment, it can be seen that students in Group A have strengthened the consciousness of independent learning for most of the students through the application of AI technology assisted teaching and the enthusiasm of practicing the basic national dances has been increasing.The overall score of independent learning conscientiousness of Group A is higher than that of Group B. The students in Group A have increased their learning consciousness more efficiently through the application of the the pre-class punch card instruction provided in the artificial intelligence technology to more efficiently enhance students’ self-motivation to learn. After 12 weeks of teaching experiment, the students in Group A performed well and the students’ basic skill level of national dance was improved. After an in-depth study, it can be found that the use of artificial intelligence teaching for teaching national dance courses is more effective than the traditional teaching mode. After the learning assistance of such program software as Artificial Intelligence, the students in Group A have been able to master the movements and knowledge learned and practiced in a lesson and are able to understand the basic movements of national dance based on this knowledge.
Independent study of self-conscious independent sample t test
The results of the independent samples t-test for collaborative awareness are shown in Table 2. The students in both classes were assessed on the basic national movements of the dance steps during the 12-week class and it was decided to test the students’ performance at the end of the period through a final exam. For efficiency, the students in both classes were numbered in random order. This allowed the judges to assess the students’ performance without knowing their classes, thus avoiding bias due to subjective reasons. Based on the WDSF scoring system, students were critically evaluated on their dance skills, musical performance, ability to collaborate between partners, routines, and performance. This method of assessment is designed to measure the overall level of student performance and to provide a fair opportunity for students to utilize their talents. Teachers have assessed that instruction using the AI program platform can significantly improve students’ prior learning.
Collaborative conscious independent sample t test
| Dimension | B group | A group | T | P | |
|---|---|---|---|---|---|
| Assisted consciousness | Cooperative cognition | 26.72±4.12 | 26.98±3.02 | -0.402 | 0.005 |
| Collaborative emotion | 11.96±2.46 | 12.5±1.96 | -0.583 | 0.032 | |
| Cooperative intention | 12.95±1.43 | 12.95±1.96 | 0.324 | 0.038 | |
| Interpersonal support | 18.34±1.87 | 18.84±2.62 | -0.945 | 0.047 | |
| Collaborative skill | Conflict management | 16.13±2.12 | 14.96±1.78 | 1.825 | 0.014 |
| Emotional regulation | 10.43±1.94 | 10.02±1.25 | 1.143 | 0.024 | |
| Organizational leadership | 7.45±1.65 | 8.12±1.58 | -1.438 | 0.013 | |
| Inventory of total | 104.75±9.36 | 104.96±7.21 | -0.128 | 0.015 |
In order to understand the experience of teachers who used AI technology for dance teaching in this controlled experiment, teachers in the experimental group were selected for interviews in this subsection. Since there were a total of 7 teaching sessions in this experiment, the interviews were selected to conduct in-depth interviews with teachers after the 1st, 3rd, 5th and 7th sessions. The interviews were anonymous, and the four interviewed teachers were indicated by the letters A, B, C, and D instead.
The results of the interviews, as shown in Table 3, were mentioned seven times by the four interviewed teachers: saving physical energy and making dance teaching easier. Through classroom observation, after using AI technology, 22.58% of the teachers believed that AI technology could demonstrate the movements less directly and correct the movements more, which reduced the calorie consumption and made it easier for the interviewed teachers to finish the dance teaching.
The second benefit mentioned by the teachers interviewed was “engaging the students”. In a traditional dance classroom, one of the pain points for teachers is how to keep the attention of the learners during the teaching activities. With the introduction of AI devices into the dance classroom, multimedia technologies such as animation, music, video, and special effects are implanted to continuously attract the attention of the students so that the teaching activities can be carried out smoothly.
The third advantage is that AI technology can remind teaching priorities and help in the delivery of lessons. This is mainly suggested by teachers who are not experienced enough in teaching. In the course, the dance teachers who are not experienced enough may miss an important dance knowledge point or a difficult dance point in teaching, and this situation is not easy to be noticed by teachers and students, which leads to unsatisfactory learning effect of students. With the introduction of AI teaching into dance teaching, teachers who have missed a key or difficult dance movement will be reminded of the omission through the vibration of the teacher’s bracelet, so that a dance movement can be explained thoroughly, and it is also easy for students to understand and learn. In addition, the interviewee also mentioned two advantages of AI technology, which effectively protects the voice and effectively motivates the students to practice.
The advantages of artificial intelligence technology dance teaching
| Order | Viewpoint description | A | B | C | D | Total | Frequency |
|---|---|---|---|---|---|---|---|
| 1 | Save energy and relax | 2 | 2 | 1 | 2 | 7 | 22.58% |
| 2 | Built-in functions attract students effectively | 2 | 1 | 2 | 5 | 16.13% | |
| 3 | Remind the heavy difficulty and help the lecture | 2 | 1 | 1 | 4 | 12.90% | |
| 4 | Protective voice | 2 | 1 | 3 | 9.68% | ||
| 5 | Effective incentive | 1 | 2 | 3 | 9.68% |
In the basic teaching of AI dance, the combination of shape context shape context algorithm and DTW dynamic regularization is used to statistically organize and analyze the results of skeleton matching from different perspectives, to evaluate the degree of completion of the students’ dance movements and the degree of conformity of the dance rhythm, and to give the quantitative movement scores and rhythm scores. The study shows that there is a significant difference between the overall and learning process calorie consumption between Group A and Group B. Through the independent samples t-test, the participation of artificial intelligence technology in dance teaching can improve the learning effect and learning performance of students, especially in the quality shaping. Students’ dance skills, musical performance, collaborative ability, routines, and performance were improved along with independent learning conscientiousness (P<0.05). The advantages of AI teaching are mainly: saving physical energy, making dance teaching easier, attracting students, and being able to remind the teaching key points to help the lesson. In conclusion, the findings of this paper give relative references and insights for the application of artificial intelligence in national dance teaching.