Designing music tasks with EDIPT and AI

Designing an AI-supported EDIPT model for music teaching and core competencies  

Guo Yumei  

Xingpu Experimental Middle School, Suzhou Industrial Park  

Abstract:The new curriculum standards for art education emphasize that core competencies should be used as the goal of education. However, the development of these core competencies cannot rely solely on the imparting of knowledge; it requires practical application to truly be achieved. Therefore, a new teaching model that is scientifically designed and effectively implemented with a task-oriented approach is necessary. This paper utilizes the EDIPT design model from Stanford University, and by leveraging AI to enhance music teaching practices, it plays a significant role in promoting the development of students’ core artistic competencies.    

Keywords:Core competencies; EDIPT design; AI-driven enhancement    

The “Curriculum Standards for Art Education in Compulsory Education” issued by the Ministry of Education in 2022 clearly states that core competencies should serve as the guiding principle in education. The core competencies of art courses include aesthetic perception, artistic expression, creative practice, and cultural understanding. The curriculum standards also recommend that classroom teaching content should be selected and organized in a task-driven manner. This requires us, as art educators, to move away from the traditional teaching model that focuses primarily on the transmission of knowledge, and instead develop a new teaching model that emphasizes the development of core competencies through tasks. The goal is to help students shift from merely learning musical knowledge to effectively mastering the ability to express themselves through music. In this context, how to design teaching tasks in a scientific and reasonable way, and how to effectively utilize these tasks to foster the development of core competencies, become important theoretical issues that we, as music educators, must carefully consider.  

I. Challenges faced by teachers and students: How to overcome the difficulties in music teaching  

In the music teaching practices of teachers, influenced by the traditional knowledge-transfer-based teaching model, the tasks designed for music teaching mainly focus on melody imitation, rhythm practice, and memorization of musical theory. Despite extensive classroom instruction, these efforts fail to effectively develop students’ core competencies such as aesthetic perception and creative practice. Music teachers often find themselves in a difficult situation where they desire to achieve these core competencies but are unable to do so, and they struggle to find effective ways to motivate students to develop these essential skills.  

In students’ musical learning experiences, there is a common phenomenon of having a liking for music but a lack of interest in music classes. Students are indeed interested in music itself; singing and dancing are natural aspects of children’s nature. However, many students are not interested in music classes. The reasons for this are as follows: first, the knowledge taught in music classes is boring and complex; second, even if students learn this music knowledge, they cannot apply it in real life. This difficulty in applying what they have learned leads students to gradually lose interest in music classes.  

How can the challenges faced by teachers and students in music education be overcome? This paper proposes using the EDIPT model of design thinking from Stanford University as a framework to discuss in depth how teaching tasks oriented towards core competencies can be designed scientifically and rationally. During the task-driven teaching process, various AI technologies can be effectively utilized to help students overcome various obstacles they encounter in the learning process. In this way, “application of knowledge” can be achieved effectively, and students can truly understand how to “express themselves through music”. Ultimately, this contributes to the effective development of the core competencies in art education.  

II. EDIPT teaching design: Focus on core competencies and AI enhancement  

(1) Overall framework  

What is learned from books seems insufficient; to truly understand something, one must experience it through practice. Core competencies can only be effectively developed by guiding students to feel and experience them in practice. Therefore, it is crucial to design scientific and reasonable learning tasks. We cannot remain within the traditional knowledge-transfer model; instead, we should design learning tasks that focus on applying knowledge in real-world situations, such as singing melodies or memorizing musical theory. The EDIPT model from Stanford University can provide effective guidance on how to design such “applied-learning” tasks. The EDIPT model represents a framework for designing learning activities. It originated in the design industry and, after being further developed at Stanford University’s School of Design, has been widely used around the world. The EDIPT model consists of five stages, all of which are problem-solving-oriented. It starts with empathy (E), then clearly defines the problem to be solved (D), develops a prototype solution (P) through creativity (I), and finally optimizes and improves the prototype through continuous testing (T). This approach helps to create a model of learning tasks that are practical and continuously improved.  

On the basis of designing scientific and reasonable learning tasks, it is also necessary to guide students to effectively complete these tasks, so as to truly achieve the goal of “applied learning” and effectively develop their core competencies. However, completing these learning tasks is not always easy. Especially for middle school students, fulfilling the task of “expressing through music” involves many obstacles such as a weak knowledge base, insufficient creative ability, and limited opportunities for presentation and evaluation. Therefore, it is essential to design a scientific approach to help students effectively complete their learning tasks. In this case, various AI tools such as Doubao AI agent, Kuyou AI music composition, and Yi Jia Academy platform are used to assist students throughout the entire process of completing the learning tasks. This helps students overcome various obstacles and ultimately experience the real application of what they have learned, thereby effectively developing their core competencies. In general, the overall design of this case is a Stanford EDIPT model powered by AI; it consists of two main aspects: EDIPT task design and AI assistance. The former addresses the issue of “how to design tasks,” while the latter addresses “how to complete tasks.” Together, they contribute to the development of core competencies driven by tasks.  

(II) Design of EDPIPT tasks oriented towards core competencies  

Under the teaching philosophy that focuses on developing core competencies, it is more important for students to learn how to “express themselves through music” than to merely acquire rigid “musical knowledge”. The process of students expressing themselves through music is a problem-oriented, task-driven, and application-focused one. Through this process, students’ core competencies such as aesthetic perception, cultural understanding, creative practice, and artistic expression can be effectively developed. However, “expressing oneself through music” is a complex concept; without proper scientific analysis and in-depth development, students may end up in a state of confusion and disorder during the learning process. In this case, the EDIPT model used by Stanford University will be applied to break down the learning task of “expressing oneself through music” in the context of developing core competencies.  

First is the empathy session. This session corresponds to the aesthetic perception aspect of core competencies. Empathy is the foundation for musical expression. Students must first understand the background, style, and mood of the musical works they are studying. Then, they need to find a point of resonance between the musical works and their own emotional expressions, so as to achieve effective empathy. Only in this way can they establish a solid foundation for later learning how to express themselves through music.  

The second step is the definition phase. This phase corresponds to a deep cultural understanding of the core competencies. In the design of tasks that involve musical expression, defining the content and themes clearly is essential. In musical expression, students may have a wide range of emotions and feelings they want to convey. However, if these feelings are not clearly defined, spontaneous and chaotic expressions will only lead to ineffectiveness and fail to achieve the intended purpose of expression. Therefore, the definition phase is very important; a deep cultural understanding of musical expression is necessary in order to accurately identify and focus on the core themes.  

The third stage is the concept and prototype development phase. These two stages correspond to the creative practices related to core competencies. Once the theme is defined, the concept and prototype design phases begin. These phases constitute the main part of the learning tasks. Students need to decide on the musical style, emotional tone, musical instruments used for accompaniment, and lyrics that will be used in the music, based on the defined theme. In the end, a relatively complete musical work is created.  

The fourth step is the testing phase. This phase corresponds to the artistic expression of core competencies. After the prototype is created, it should not be left untouched; instead, it needs to be tested and refined repeatedly. For musical works, it is even necessary to perform them, gather feedback from listeners, and continuously improve the work.  

(3) EDIPT tasks implemented with the support of AI  

The Stanford EDIPT model can be used to scientifically design learning tasks that focus on core competencies. However, this does not mean that well-designed learning tasks will necessarily be successfully implemented in actual teaching. Especially for middle school students, they may encounter various obstacles during the completion of these tasks, such as a weak knowledge base, insufficient creative ability, and lack of opportunities for presentation. If these obstacles cannot be overcome effectively, it will be difficult to successfully complete the learning task of “expressing oneself through music”, and thus it will be difficult to develop the core competencies. In this case, various currently popular AI tools will be used comprehensively, so as to fully leverage the powerful support of these AI tools and help students successfully achieve their goal of developing musical core competencies through practical tasks. For the four practical stages, corresponding AI-enabled strategies will be applied to address the various obstacles.  

Firstly, during the empathy stage, students will encounter obstacles due to a weak foundation in knowledge. Empathy cannot be achieved in a vacuum; it requires a solid foundation of knowledge. However, middle school students do not have a sufficiently strong knowledge base. Especially when it comes to minority music, students’ knowledge in areas such as humanities, history, and geography is relatively weak. Therefore, we will utilize the extensive knowledge resources and instant Q&A capabilities of AI tools to create intelligent agents like “master musicians” and small teaching assistants in the classroom, thereby effectively expanding students’ knowledge base.  

Secondly, during the definition stage, students encounter difficulties in accurately formulating a clear theme. Based on empathy, the wishes expressed by the students are quickly activated; however, it is more important to focus those expressions. We will utilize the reasoning capabilities of AI tools such as DouBao and DeepSeek to help students transform their expressions into a precise and coherent theme.  

Thirdly, during the concept and prototype stages, students generally encounter difficulties in their creative abilities. Creating musical works requires high levels of skill in terms of melody, instrumentation, and lyrics, which is beyond the capacity of middle school students. Therefore, we will use AI tools such as AI composition, DouBao, and DeepSeek to assist students in creating melodies, instrumentation, and lyrics, thereby enabling them to produce a relatively complete musical work.  

Fourthly, during the testing phase, the main obstacle is the lack of sufficient opportunities for students to present their works. Traditional music classes are limited by time and space constraints, which means there are very few opportunities for students to showcase their creative works. This makes it difficult to provide detailed feedback and improvements. We will utilize the presentation and communication features offered by the Yijia Academy platform, allowing students to post their musical works on the platform. In this way, other students and teachers can give feedback and suggestions without any time or space limitations, thereby helping the musical works to improve continuously.  

III. EDPICT teaching practice: Experience the joy of “expressing through music”  

In the teaching of Unit 5 “The Sounds of Tianshan” in the music textbook for seventh grade, second semester, we applied the above design approach. The focus was on the pieces “My Golden Altai” and “Young People’s Dance Music”. In the study of this unit, the task of “expressing through music” means that students should, just as the enthusiastic people of Xinjiang, learn to use music to express their admiration and praise for the beautiful landscapes of their country, their hometown, the good life, and the time of youth.

Firstly, during the empathy stage, in order to help students better understand the aesthetics of Xinjiang, we used the intelligent assistant to explain to them a wide range of interdisciplinary knowledge related to Xinjiang’s beauty, the customs and traditions of the regions along the Tianshan Mountains, its geographical environment, and its history and culture. All these efforts have provided a solid foundation for students to gain a deeper understanding of the musical styles of Xinjiang. Additionally, we developed an AI agent called “Musical Instrument Master”. Through interactions with this agent, students not only acquired knowledge about ethnic minority instruments such as durum and rawop, but also gained a more profound understanding of the formation and development of Xinjiang’s music.

Secondly, during the definition stage, we make full use of the functions of AI tools such as DouBao and DeepSeek, which enable deep thinking, analysis, and reasoning. This helps students engage in meaningful communication with AI and refine the themes they want to express. For example, some students realized that Alatai is a place full of diversity: there are blue skies, grasslands, snowy mountains, as well as sheep and horses. Through multiple conversations with DouBao, they were able to condense their creative theme into the phrase “My colorful Alatai”.

Thirdly, during the concept and prototype stages, we use the Kuyou AI music composition template to help students break down music creation into various modules such as themes and musical styles. Within musical styles, there are further sub-modules like bold, epic, and ethnic styles. Students can choose the appropriate module based on their creative theme, thereby generating the corresponding melodies. As regards lyric writing, students can use AI tools like DouBao and DeepSeek to adapt the lyrics of “Youth Dance Song” to new themes such as school days.

Fourthly, during the testing phase, we used the Yijia Academy platform to upload the musical works created by the students onto the platform. Other groups of students could provide feedback on these works. They commented on issues such as whether the works accurately conveyed the original intentions, and what emotions and associations they evoked. The students gave detailed and vivid evaluations and suggestions, which helped to improve the works further.

The implementation of the EDPICT teaching model enhanced by AI has been widely welcomed by students. Their interest in music lessons has increased significantly, and their enthusiasm for learning has also risen greatly. They are now able to truly experience the joy of “expressing oneself through music”.

One of the joys is that students are able to create their own musical works, and they experience a great sense of accomplishment. Through the entire EDPICT process, students go through a series of steps: from appreciating music, to forming their own creative themes, and finally turning those themes into a complete musical piece. Such unique musical works that are entirely created by the students themselves greatly stimulate their interest and allow them to truly experience the joy of creation. Of course, students also encounter various difficulties during the creative process. For example, they have to make choices among several similar creative themes, and they need to communicate repeatedly with the AI system when the results produced by the AI are not satisfactory. But when they finally create a musical piece that meets their expectations, the joy they feel is indescribable.

Another benefit is that students find music classes no longer boring. Interest is the best teacher; once they develop an interest in music through creative activities, many students begin to explore the mysteries of music actively. For example, the melody of “Youth Dance Song” is lively and smooth, and it is very popular among students. However, the lyrics have lost their freshness due to long-term use. As a result, some students use DouBao to rewrite the lyrics with themes related to school life. This newly created version of “Youth Dance Song” becomes even more popular among students, and it serves as a new form in which traditional folk songs are carried forward in the modern era.

Thirdly, the students found many opportunities to apply their creations. Applying what they have learned is the ultimate goal of learning. Through teaching, we found that by using AI to facilitate the task of “expressing oneself through music,” the students gained a deeper understanding and improved their ability to use music effectively. For example, in the lesson “My Golden Altay”, the students pointed out that this piece of music would be suitable as background music at high-speed train stations in Altay, reflecting a nationalistic style. Another piece of music would be appropriate to play in coffee shops at night, while another piece could help people regain confidence when they encounter setbacks. Through such feedback, the students established a complete cycle between creating and using music, and also developed a deeper understanding of the social functions of music.

Cultivating core competencies is the ultimate goal of music education. However, achieving this goal cannot rely solely on the imparting of knowledge; it requires students’ personal experiences and creative activities. Only through practical application can students truly develop their aesthetic perception, cultural understanding, creative skills, and artistic expression. But practical application requires careful design and effective support. This paper uses the EDIPT design model to scientifically plan learning tasks, and leverages AI tools to provide effective support for students’ practices. As a result, good outcomes have been achieved. It is hoped that our efforts will contribute to the development of core competencies in art education.