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php/ijcis/index Being Songwriter and Singer Using Suno as AI Music Generator: Creating English Songs for Young Learners in Teaching Vocabularies Tira Nur Fitria Institut Teknologi Bisnis AAS Indonesia tiranurfitria@gmail.
AbstractAi This research simulates the use of Suno AI .
ttps://suno.
com/) in creating English songs for young learners in teaching vocabulary.
This research is descriptive qualitative.
The analysis shows that Suno offers AI-driven solutions encompassing song creation, sound processing, and data analysis, known for efficiently producing lifelike songs blending vocals with instruments or entirely instrumental compositions.
Teachers utilize Suno AI to create English songs aimed at teaching children vocabulary, benefiting from its user-friendly tools regardless of musical expertise.
This approach enhances vocabulary acquisition in engaging classroom settings, fostering creativity and emotional connections to learning Teachers can customize songs to suit students' preferences and educational needs, promoting motivation and creative skills development.
Using Suno AI involves visiting their website, registering, selecting a music genre and vocabulary theme .
, colors, number.
, and customizing generated melodies and lyrics to accommodate varying proficiency levels.
The platform supports easy song creation, editing, and sharing, facilitating effective vocabulary learning through enjoyable musical experiences integrated into daily classroom activities.
This innovative use of technology supports educators in enhancing language acquisition through engaging and accessible musical content, exemplified by the creation of an English children's song titled "Colors of the Rainbow.
" This song vividly teaches color vocabulary through descriptive lyrics and lively melodies, complemented by a "children's lively" music style that enhances its cheerful and educational impact on young learners.
With Suno AI.
English teachers can assume the roles of songwriters and musicians in education by utilizing its capabilities to create engaging songs tailored for teaching basic English vocabulary.
Suno AI allows teachers to organize lyrics around new words, phrases, or language concepts they aim to teach, fostering a dynamic learning experience that seamlessly integrates language acquisition with musical expression.
This innovative use of technology enhances educational engagement through enjoyable and effective teaching methods.
Keywords: AI Music Generator.
English song.
Suno AI, vocabularies INTRODUCTION Artificial intelligence (AI) has profoundly influenced and integrated into numerous facets of human existence and industries (Fitria, 2023.
Its impact spans fields such as healthcare, finance, transportation, entertainment, education, and In healthcare.
AI assists in diagnostics, personalized medicine, and drug discovery.
Financial sectors leverage AI for fraud detection, algorithmic trading, and customer service In transportation.
AI powers autonomous vehicles and optimizes logistics.
Educational institutions adopt AI for personalized learning experiences and student assessment (Fitria, 2021, 2023.
Entertainment platforms use AI for content recommendation, music composition, and video editing.
AI's ability to analyze vast amounts of data, learn from patterns, and make decisions autonomously has transformed how businesses operate and how individuals interact with technology in their daily Journal IJCIS homepage - https://ijcis.
net/index.
php/ijcis/index Its continued development promises further innovations and efficiencies across diverse sectors, driving forward advancements in science, technology, and society as a whole.
Artificial intelligence involves the study and development of machines capable of performing tasks traditionally requiring human intelligence, such as facial recognition, speech understanding, and decision-making.
In this context, deep learning serves as the foundation for generating music using computers, leveraging a database of musical melodies without the need for expertise from trained musicians.
Music creation is facilitated through sentiment analysis, which detects moods based on sample human images (Gupta et al.
, 2.
The music is saved in a piano-roll format and categorized into various emotional types derived from testing phases.
Deep learning enables the machine to self-train through iterative input from the database.
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php/ijcis/index simplifying the process of generating music based on user facial input and enhancing overall music creation capabilities.
Recent advancements in music generation techniques have shown remarkable progress (Tang et al.
, 2.
Utilizing deep learning neural network frameworks, algorithms are now capable of producing music that competes with human AI's role in algorithmic music involves employing AI techniques as primary tools for generating compositions.
Various AI models are utilized in music composition, algorithms, neural networks, stochastic methods, generative models, agents, decision trees, declarative programming, and grammatical representation (Lopez-Rincon et al.
, 2.
AI music composition stands out as a highly compelling and significant area within artificial intelligence, music, and multimedia (Tan & Li, 2.
Key tasks within AI music composition encompass melody creation, arrangement, performance generation, timbre rendering, sound generation, and synthesis of singing voices.
These tasks span various modalities, such as symbolic music notation and sound, highlighting the diverse applications of AI in the creation of music.
Utilizing artificial intelligence to generate music is captivating from both scientific and artistic perspectives.
Moreover.
AI-driven music creation enhances our understanding of music perception and enables detailed exploration of our creative processes.
a novel and highly experimental approach to composition, it has the potential to profoundly inspire artistic creativity (Rutkowski et al.
, 2.
Creating music is a challenging task that requires balancing personal creativity with adherence to rigorous guidelines (Chu et al.
The evolution of deep learning technologies has expanded the array of methods available to automate intricate processes and foster creativity in musical composition.
However, previous research has generally overlooked the exploration of audience satisfaction as a means to enhance music generation models.
This study addresses this gap by assessing human satisfaction with state-of-theart symbolic music generation models powered by Journal IJCIS homepage - https://ijcis.
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php/ijcis/index deep learning.
(Civit et al.
, 2.
explore the potential applications and availability of existing AI solutions designed to assist in musical Their research demonstrates how publications are distributed globally based on numerous criteria, offering a comprehensive view of the current state of this technology.
Our findings reveal a notable rise in interest among musicians and computer scientists alike in AIdriven automated music generation over recent years, with major companies increasingly involved in this field, whose works we analyze.
We delve into various generation architectures, examining them from both technical and musical perspectives, and identify areas where additional research is warranted.
Soegiono et al.
explain that Artificial Intelligence (AL) has developed tremendously in recent years.
One area that has made significant progress is in the music industry.
Al music generator is a computer program that uses machine learning algorithms to create original musical compositions, which can range from simple melodies to complex orchestral The system is trained on large existing music datasets and uses that information to learn patterns and structures that can be used to produce new music.
The use of Al in music creation has the potential to revolutionize the way we create and enjoy music.
With Al music generator, musicians and composers can quickly generate ideas and experiment with different styles and genres of music, without the need for extensive training or knowledge of music theory.
Additionally.
Al-generated music has the potential to be used in a variety of applications, from background music in films and video games to personalized music recommendations.
AI technology has profoundly changed the landscape of the music industry, from creation to AI Music Generator is a technology that uses artificial intelligence (AI) to create music automatically.
It involves the use of machine learning algorithms and big data processing to analyze musical patterns from various genres and styles.
AI Music Generator can generate new musical compositions based on given parameters, such as music genre, tempo, instruments, and desired atmosphere.
Methods commonly used in AI Music Generator include Page 181 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index generative models such as neural networks, deep learning, and other machine learning techniques to produce music that is similar to a particular musician's style or genre.
This technology not only helps in the creation of new music but can also be used to support musicians' creativity by providing basic ideas or melodies that they can develop further.
The convergence of artificial intelligence (AI) and music generation has transformed the field of musical composition, opening up unprecedented creative opportunities (Agwan et , 2.
AI-generated music has attracted considerable interest, prompting discussions about the potential effects of this new technology on both the music industry and human creativity (Will, 2.
A work of art created using artificial intelligence (A.
programs, such as text-to-image models and musical generators, is referred to as an example of artificial intelligence art (Sabry.
In an era of rapid technological development, the use of artificial intelligence (AI) applications such as AI Music Generator, for example.
Suno AI known as Suno, is an artificial intelligence music generation program created to produce lifelike songs that blend vocals with instruments or are entirely instrumental.
AI is increasingly applied across artistic domains such as music, film, and other forms of art.
Another significant opportunity presented by AI is providing amateur musicians with innovative methods to enhance their creative processes.
Many experts, researchers, musicians, and record labels are exploring novel ways to integrate AI technology into music production.
Certain software tools can generate compositions in various composer styles, while others utilize machine learning algorithms to create new songs and sounds.
Moreover.
AI can also be utilized to compose entire songs.
there are numerous AI generators available for song creation, such as Suno.
Users can input their own lyrics, specify the desired genre, and instantly generate a complete "original" song ready for listening.
Suno AI, a platform that uses artificial intelligence to automatically create songs, is attracting attention for its promising ability to change the way musicians and music fans interact with musical creativity.
Suno AI, as one of the Journal IJCIS homepage - https://ijcis.
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php/ijcis/index latest innovations in this field, stands out as an AI-based platform that facilitates musicians in their creative process.
With advanced algorithms.
Suno AI can automatically generate melodies, harmonies, rhythms, and even lyrics based on user preferences.
Key features include automatic music generation to provide the basis for new songs, lyric writing capabilities with natural language processing (NLP) technology, as well as help with organizing and arranging song Suno AI also enhances music by improving technical aspects and adding artistic variations, while enabling collaboration between musicians from different locations.
In this way.
Suno AI not only optimizes musicians' creative processes but also indicates the future direction of music that is increasingly integrated with AI According to Yu et al.
Suno has garnered considerable interest for its remarkable abilities, showcasing advancements in technology and creating fresh avenues for music creation.
This marks a significant achievement in the evolution of artificial intelligence (AI) in generating music.
Examining the overall technical structure of AI music generation reveals that it primarily consists of three components: a lyric generation module, a voice synthesis module, and a music generation module.
Initially, the lyric generation module creates lyrics that match the textual inputs provided by users.
Following this, the voice synthesis module transforms these lyrics into singing voices with suitable melodies.
Finally, the music generation module produces accompanying music based on the textual inputs and the generated singing voices.
Using this broad technical framework.
Suno accomplishes the entire creative process from text to song, effectively showcasing its strengths such as seamless integration of lyrics, melodies, and songs, streamlined and diverse music composition capabilities, and facilitation of cross-cultural communication and integration.
The trend has sparked widespread discussion about the impact of Artificial Intelligence (AI) technology on the music industry, including the challenges and opportunities that arise with the adoption of this Despite a warm reception from music fans, there are also concerns about the Page 182 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index authenticity and uniqueness of AI-generated artwork compared to human work.
This trend sparked widespread discussion about the impact of AI technology on the music industry, both in terms of new possibilities for music creation and the ethical and creative challenges that arise.
Despite differing opinions, the popularity of Suno AI shows the great potential of AI technology in changing the creative landscape of modern music, while inviting reflection on the role and identity of musicians in an increasingly digital era.
Figure 1.
Trend of Suno AI in Creating Songs by Eka GustiwanaAos Post Source: https://w.
com/watch?v=4Yuvt6YXZ88&ab_channel=EkaGustiwana A video by Eka Gustiwana on April 2024 reviewing Suno AI as a tool capable of creating songs by combining artificial intelligence (AI) technology went viral and attracted attention among music and technology fans.
Suno AI is known to be able to produce songs complete with vocals and lyrics based on input provided by the user, such as lyrics and the desired music genre.
In the video.
Eka Gustiwana, a well-known musician and content creator, presents a demonstration of Suno AI's ability to create music He discusses how this technology can revolutionize the way the music industry operates by providing fast and efficient creative solutions for creating songs.
Reactions to the video varied widely, with some appreciating technological innovations that allow wider access to music creation, while others expressed concerns about the replacement of human musicians by AI technology.
Figure 2.
The trend of Suno AI in Creating Songs by Indra AzizAos Post Source: https://w.
com/watch?v=bDcwmgecxyY&ab_channel=VokalPlusbyIndraAziz Then, the next video by Indra Aziz on creating songs complete with vocals and lyrics in May 2024 reviewing Suno AI as a tool for Indonesian went viral and received widespread Journal IJCIS homepage - https://ijcis.
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php/ijcis/index attention among music and technology lovers.
the video.
Indra Aziz, a famous vocalist and expert in sound technology, explains how Suno AI can be used by anyone to create songs easily.
Users only need to enter their lyrics and select the desired music genre, and then Suno AI will produce a complete song with a vocal melody and appropriate musical arrangement.
Reaction to the video has been overwhelmingly positive, with many users impressed by the speed and ease of creating music using AI technology.
Many see great potential in using AI like Suno AI to help amateur musicians express their creativity without having to have deep technical skills in music Overall, the video by Indra Aziz introducing Suno AI in creating Indonesian songs automatically reflects the enthusiasm and great potential of AI technology in penetrating the world of musical creativity, while inviting deep reflection on the role of AI in the future of the global music industry.
Suno AI represents a significant advancement in artificial intelligence tailored for music creation, setting itself apart from other AI tools in several key ways.
Unlike traditional AI tools.
Suno AI is specifically designed to democratize music creation by simplifying the process for anyone, regardless of musical Its primary objective is to eliminate the complexities typically associated with music production, allowing users to effortlessly generate music compositions.
Suno AI is its comprehensive approach to music creation.
It not only assists in composing melodies but also aids in crafting lyrics based on voice input, thereby streamlining the creative process further.
This capability makes it particularly appealing for users who may not possess formal musical training but wish to express their musical ideas Moreover.
Suno AI acts as a virtual personal composer, capable of customizing compositions to suit diverse musical preferences and requirements.
Whether users seek to create a pop ballad or an upbeat rock anthem.
Suno AI adapts to their creative vision, adjusting elements like genre, instrumentation, and vocal style Suno AI, a platform that enables the automatic creation of songs with lyrics and vocals based on a given input, has great potential for use Journal IJCIS homepage - https://ijcis.
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php/ijcis/index in the world of education.
Teachers can use it to enrich language and literature learning by creating songs that strengthen understanding of vocabulary or literature curriculum content (Pudjiati et al.
Additionally, in the context of music arts education.
Suno AI can be used to teach students various musical genres or composition styles, demonstrating how AI technology can be applied in creating music with a variety of different instruments and styles.
In addition.
Suno AI can also be a means to stimulate students' creativity by allowing them to create their own music based on the concepts they choose.
The use of Suno AI in education also provides an opportunity to teach students about artificial intelligence, machine learning algorithms, and the application of technology in the context of human creativity.
Thus, the integration of Suno AI in education not only broadens students' learning experiences but also opens the door to leveraging technology to increase student engagement, creativity, and understanding of concepts in music and language With the rapid development of artificial intelligence (AI), music generation has gained widespread attention (Li, 2.
Significant advancements in Artificial Intelligence-based music production have revolutionized the field of musical composition (Pathariya et al.
, 2.
Using artificial intelligence (AI) techniques to generate music has intrigued researchers from the early days of computing (Kaliakatsos-Papakostas et al.
, 2.
Music possesses self-referentiality, meaning it often exists independently of external concepts and is structured across multiple interconnected levels of abstraction, making it a unique domain for testing AI methodologies.
There has been notable advancement in music generation techniques employing deep learning.
Nevertheless, integrating these techniques into the everyday music-making practices of musicians and artists remains challenging (Tokui, 2.
Creating a sophisticated artistic creation like a musical composition necessitates demonstrating a specific degree of creativity, which is influenced by factors associated with the structure of musical language (Hernandez-Olivan & Beltryn, 2.
Music generation has algorithmic approaches and is now increasingly Page 184 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index applying deep learning models, similar to those used in fields like computer vision, to address these challenges.
Mantaras & Arcos .
surveyed three primary categories of computer music systems utilizing AI techniques: .
compositional, .
improvisational, and .
performance systems, providing brief descriptions of representative examples in each category.
then delves deeper into the challenge of imbuing these performances with the expressiveness typically found in human-generated music, a recent focus in computer music research.
A key difficulty in modeling expressiveness lies in capturing the performer's nuanced "touch," which encompasses the accumulated knowledge applied during musical performancesAia skill humans acquire through extensive observation and Traditional approaches, centered on following musical rules to capture interpretative knowledge, have shown significant limitations.
An alternative approach, more aligned with human observation and imitation processes, involves directly leveraging implicit interpretative knowledge gleaned from examples extracted from recordings of human performers, rather than attempting to explicitly define such knowledge.
Casini et al.
center on music and the recent strides in applying deep learning to create musical content.
We contend that while these models can produce outputs that resemble music, the influence of human musicians remains crucial in crafting a musical composition.
We explore these constraints and propose envisioning new tools and instruments that can foster innovative forms of interaction while facilitating new processes of creativity and music production.
Williams et al.
introduce an architecture designed to create emotionally coherent music using machine learning-assisted sound synthesis.
Our system employs Hidden Markov Models to generate a small collection of music pieces, which are then labeled with emotional tags derived from questionnaire responses.
This results in a corpus of labeled music that reflects perceptual The analyses demonstrate a direct relationship between the tranquility or intensity of a musical piece, users' GSR readings, and the emotions they experience.
(Louie et al.
, 2.
highlighted generative deep neural networks (DNN.
for their ability to generate innovative Journal IJCIS homepage - https://ijcis.
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php/ijcis/index musical compositions.
However, there has been limited exploration into the challenges and opportunities of collaborative creation with these musical AIs, particularly for beginners.
Through a study using a widely adopted interactive musical AI, it was discovered that users can be overwhelmed by the sheer volume of musical content generated and frustrated by its unpredictable nature.
To address these issues in co-creation.
AI-steering tools like Voice Lanes were developed to control content generation and focus on specific voices.
Ma et al.
introduce AI-Lyricist, a system designed to generate original and meaningful lyrics based on specified vocabulary and a MIDI file input.
This undertaking poses several challenges, including the automatic identification of melodies and extraction of syllable patterns from multi-channel music, the creation of innovative lyrics that harmonize with the style and syllabic structure of the input music, and the fulfillment of vocabulary constraints.
tackle these challenges, they propose an automated lyrics generation system composed of four modules: .
A music structure analyzer that extracts musical structure and syllable patterns from MIDI files, employing expected syllable counts to enhance melody identification.
a SeqGAN-based lyrics generator refined through multi-adversarial training using policy gradients with dual discriminators to ensure text quality and syllable alignment.
a deeply coupled musiclyrics embedding model that aligns music and lyrics in a shared space for balanced comparison of melody and lyrical constraints.
a Polisher module that adheres to vocabulary constraints by masking the generator and substituting words as needed.
Our model was trained on a dataset comprising over 7,000 pairs of music and lyrics, enriched with manually annotated labels for theme, sentiment, and genre.
Chu et al.
introduce a taxonomy for categorizing music generation models and propose nine subjective evaluation metrics.
They conducted an evaluation study where we gathered over 700 assessments from 100 participants using these metrics.
Their findings highlight the impact of token representation methods and model characteristics on subjective satisfaction.
Through qualitative analysis, we deepen our insights into Page 185 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index AI-generated music and refine our proposed evaluation metrics.
They share insights gained from our study and discuss future avenues for research in deep learning models aimed at enhancing music creation.
Hong et al.
performances generated by AI and the acceptance of AI music generators as musicians.
Drawing on theories of anthropomorphism and creative machine heuristics, a 2 x 2 experiment was This experiment manipulated both the perceived anthropomorphism of the AI .
igh vs.
low anthropomorphis.
and its autonomy in creativity .
ndependent vs.
dependent creativit.
The study revealed that the human-like characteristics of an AI music generator contributed to its acceptance as a musician.
However, whether the AI was autonomous in its creative process did not affect its perception as a genuine musician.
Additionally, the evaluation of AI's independently of its attributes.
Nevertheless, individuals who viewed the AI music generator as a musician tended to appreciate its compositions more than those who did not.
The implications of these findings are discussed in detail.
Agwan et al.
explore two groundbreaking studies that illustrate the profound impact of AI on music: AI-powered music remixing and AI-generated music The first approach utilizes advanced deep learning and neural network architectures to enhance music remixing by identifying complex audio patterns, resulting in highly innovative The second method employs generative adversarial networks (GAN.
and reinforcement learning to create original music pieces that rival compositions made by humans.
Both studies exemplify AI's ability to revolutionize musical creativity, introducing new avenues for artistic This paper not only examines these pioneering studies but also discusses the broader implications of AI in music, emphasizing its potential to transform music production and inspire a new generation of musicians.
Bian et al.
propose MoMusic, an AI real-time music generation system based on motion.
MoMusic utilizes a partially randomized harmonic sequencing model derived from probabilistic analysis of tonal chord progressions, abstracted Journal IJCIS homepage - https://ijcis.
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php/ijcis/index through musical set theory.
This model operates within a dual-dimensional grid framework that translates finger movements and trajectories captured by a camera into coherent, partially improvised harmonic progressions.
MoMusic integrates diverse timbral registers, including traditional classical instruments like the piano and a novel "human voice instrument" created using voice conversion techniques.
Their research showcases MoMusic's interactivity, capacity to inspire musicians, and ability to generate cohesive musical compositions across various timbral MoMusic is designed for potential expansion to incorporate posture-controlled timbral, rhythmic, and dynamic transformations, as well as digital sound processing techniques.
Pathariya et al.
summarize various models and techniques utilized in AI-driven music It explores the necessity for these advancements by highlighting the limitations of traditional music production methods, which often require substantial time and expertise Artificial Intelligence (AI) is presented as a groundbreaking solution to these The review covers several models, including Transformer Architecture.
Long ShortTerm Memory (LSTM).
Generative Adversarial Network (GAN), and others.
According to Dash & Agres .
, music has a profound impact on emotions, and recent advancements in computing have popularized artificial intelligence (AI)-based approaches for creating affective music generation (AMG) These systems have potential applications in entertainment, healthcare, and sensor-integrated interactive system design.
Given the growing interest in this area, this article aims to comprehensively review controllable AIAMG systems.
It discusses the fundamental components of AI-AMG systems and categorizes existing systems based on their core music generation algorithms.
Dash & Agres .
explores key musical features used in composing affective music and the corresponding AI-based methods for customization.
Furthermore, it addresses the primary challenges and unresolved questions in this field, proposing potential solutions to guide future research.
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php/ijcis/index into the methodologies employed, facilitating exploration and advancement in this dynamic Recent advancements in AI have enabled the generation of sophisticated musical compositions and lyrics, yet there remains a notable gap in applying AI technologies, particularly Suno AI .
ttps://suno.
com/), to create educational English songs tailored for young learners to enhance vocabulary acquisition.
Existing literature predominantly explores the aesthetic and emotional dimensions of AIgenerated music (Casini et al.
, 2018.
Louie et al.
, with limited emphasis on educational applications and pedagogical effectiveness.
focusing on the intersection of AI creativity and educational outcomes, this research aims to pioneer the integration of AI-generated songs into educational settings, specifically targeting vocabulary learning among young learners.
This study is motivated by the need to explore how AIgenerated content can effectively engage and support young learners in educational contexts, addressing gaps identified in current practices (Chu et al.
, 2022.
Agwan et al.
, 2.
The proposed approach involves evaluating the effectiveness of AI-generated songs in facilitating vocabulary acquisition compared to traditional methods, assessing learner engagement and motivation, and designing AI-generated content that aligns with educational objectives and developmental needs.
This research not only contributes to advancing AI-driven music generation but also offers practical insights into leveraging AI technologies for enhancing language learning experiences in educational This research aims to explore the Suno .
ttps://suno.
com/) in generating English songs tailored for young learners to facilitate vocabulary acquisition, addressing a notable gap in current literature focused primarily on the artistic and emotional dimensions of AI-generated music (Hernandez-Olivan & Beltryn, 2023.
Casini et al.
While existing studies have made strides in utilizing deep learning models for music generation (Mantaras & Arcos, 2002.
Williams et , 2.
, they often overlook the specific pedagogical potential of AI-generated content in Journal IJCIS homepage - https://ijcis.
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php/ijcis/index educational contexts.
The challenge lies in adapting AI technologies, traditionally used for compositional creativity and emotional coherence in music (Ma et al.
, 2021.
Louie et al.
, 2.
, to effectively engage young learners and support their language learning processes.
By focusing on creating educational songs that are both linguistically enriching and musically engaging, this research endeavors to pioneer a novel application of AI in education, bridging the gap between technological advancements in AI-driven music and the specific needs of educational This research describes the creation of English songs for young learners in teaching vocabularies using Suno as an AI music II.
METHOD
This research uses a qualitative descriptive approach to understand and describe in detail the experience of using Suno AI in creating English songs for young learners.
A descriptive approach was used because this research aims to gain an indepth understanding of how Suno AI can be used in an educational context, especially in supporting vocabulary learning.
This research aims to simulate the use of Suno AI .
ttps://suno.
com/) in creating English songs for young learners to enrich their vocabulary.
This research aims to explore the potential of Suno AI in an educational context, specifically in learning English vocabulary through automatically generated Data for this research will be collected through several methods including observation of the use of Suno AI, where researchers will observe and record user experiences in creating English songs using Suno AI through their https://suno.
Documentation will also be collected in the form of screenshots of the process of using Suno AI, including the interface, available features, and the resulting songs.
The collected data will then be analyzed through data reduction methods to identify the researcherAos experiences, and the results of the analysis will be presented in the form of images or screenshots of Suno AI that illustrate the application of technology in creating English songs for young learners.
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php/ijcis/index the potential and application of Suno AI in education, especially in the context of teaching English vocabulary, and make an important contribution to the development of AI technology in the field of education.
FINDINGS AND DISCUSSION
Findings There are several steps on how to use SunoAI.
The first step we have to do is visit the Suno AI site or visit the site https://suno.
When we arrive at the main page, we can click the "Sign Up" button to create an account.
Use our email address, password, and other necessary Click the "Create" button then we will enter the editor page to write commands, create instruments, or test lyrics.
After writing the command or pasting the lyrics in the top column and filling in the song title, we can click "Create" at the bottom.
Wait a few moments until the process is complete.
Suno AI will present two song arrangement options for us.
If we feel comfortable with the music created by Suno AI, just download the song.
But if we still feel like the song we made is not enough, we can also edit it first.
There are many tools available in this AI, we can edit, cut, add effects, and change instruments with these tools.
To start creating our song, we can begin by writing down our thoughts or click on "Make Random Lyrics" for spontaneous inspiration.
If we prefer an instrumental approach, choose "Instrumental" and let the melodies speak for themselves.
Next, define our desired style of music by specifying the genre that fits the vibe, mood, tempo, and voice we envision.
If we are uncertain about the genre, opt for "Use Random Style" to discover a surprising genre that might inspire your To extend our song, click on "Extend" located on the song's row .
r under the " on mobil.
Enter additional lyrics that build upon our existing verses, specify the desired duration for the extension, and click Continue.
Once our extended song is created, click on ".
and "Get Full Song" to obtain the complete finished piece.
Now, immerse ourselves in the world of music-making and let our creativity flow Finally, if it is correct, we can save it to our device and then share the music.
We can share music from Suno AI on social media platforms and music streaming platforms.
Figure 1.
Homepage of Suno at https://suno.
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php/ijcis/index Figure 2.
Create an account at Suno Figure 3.
Filling song description at Suno Figure 4.
Custom mode at Suno Journal IJCIS homepage - https://ijcis.
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php/ijcis/index Figure 5.
Filling song description, lyrics, and style of music at Suno Figure 5.
Result of Lyric and Song at Suno .
(Source: https://suno.
com/song/ad04e6d1-3eb0-4be5-86ce-5a651f709.
Figure 6.
Result of Lyric and Song at Suno .
(Source: https://suno.
com/song/ad04e6d1-3eb0-4be5-86ce-5a651f709.
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php/ijcis/index The researcher tries to create an English song for children to teach vocabulary of colors.
The song title above is AuColors of the RainbowAy, the lyrics above clearly describe the vocabulary of colors through the use of vivid and descriptive Each verse of the song illustrates various colors with cheerful images that are easy for children to understand.
For example, red is like a juicy apple, blue is like a wide sky where birds sing happily, yellow is like the sun shining brightly, and green is like thick grass where we play happily.
Apart from that, this song also includes other colors such as orange which is described as a strong tiger, purple which is like a majestic wizard's robe, pink which is like a flower blooming gracefully, and brown which is warm like the hug of a teddy bear.
Engagingly and imaginatively, these lyrics not only teach color vocabulary visually but also introduce them auditorily through cheerful rhythms and melodies.
The song above uses the music of the style Auchildren's livelyAy.
Music with a lively or cheerful style is very suitable for children's songs as illustrated in the lyrics above.
Children's songs generally use music that is upbeat, cheerful, and easy for young listeners to digest.
Lively or cheerful music styles use upbeat melodies, rhythms that encourage movement or dancing, and light instruments such as acoustic guitar, ukulele, light percussion, or piano that produce bright and pleasant sounds.
Music like this not only adds an element of fun and excitement to children's songs but also helps hold their attention and makes them want to sing along and enjoy the songs with enthusiasm.
understand vocabulary in contexts that are relevant to their everyday lives, improving their understanding of the meaning and use of the Music has a strong emotional appeal, so children tend to be more interested and involved with lessons conveyed through songs.
Fun rhythms and entertaining music can increase their motivation to learn English.
Apart from that, songs also help in the development of proper pronunciation and intonation in English.
Children can imitate singers in songs to improve the pronunciation of words and understand how different intonations can change the meaning of a Lastly, songs provide space for children's creativity and self-expression.
They can participate in the accompanying simple movements or dances, which not only enrich their learning experience but also link the vocabulary to their body movements and physical Thus, using songs as an English learning medium to teach basic vocabulary is a smart and useful choice.
This not only helps children expand their vocabulary but also makes learning more fun, interesting, and effective.
The use of songs in teaching English vocabulary has significant benefits in improving students' understanding of words and their use in relevant contexts.
Songs provide concrete examples of how words are used in meaningful sentences, helping students to understand not only the meaning of words but also the way they are used together in phrases and sentences.
Music and rhythm in songs also help students to remember words better because repetition in songs strengthens the mental connection between words Discussion and their meanings.
Additionally, songs have a Songs have a very important role in strong emotional appeal, making vocabulary learning English vocabulary, especially for learning more fun and engaging for students.
children (Fitria, 2023.
In this context, songs are Through songs, students can also learn about the not just a medium of entertainment, but also an cultural context in which words are used, as well effective and interesting learning tool.
One of the as hone their English intonation and song's main strengths is its use of repetition of pronunciation skills.
English songs often contain words and phrases in its lyrics, which naturally idioms and expressions commonly used in helps strengthen vocabulary memory in learners.
everyday language, providing opportunities for Children tend to more easily remember words students to understand the meaning and use of they often hear and sing in songs.
In addition, these idioms naturally.
Overall, the use of songs song lyrics often include vocabulary in clear and as a teaching tool not only expands students' meaningful contexts, such as everyday objects, vocabulary but also improves their ability to activities, or emotions.
This helps children speak, listen, read, and write in English.
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php/ijcis/index Music composition has become attractive to both musicians and non-musicians alike, encompassing diverse musical activities such as melody creation, accompaniment, and rhythm generation (Siphocly et al.
, 2.
Teachers can create their songs by writing lyrics related to vocabulary as a creative and effective learning tool for their students.
By writing lyrics that focus on English vocabulary, teachers can present learning material in a form that is interesting and easy to remember for children.
The first step in this process is to determine the theme or topic that you want to teach students, such as the names of objects, adjectives, or verbs that are important in English.
For example, a song focus on objects in the classroom or daily activities.
After determining the theme, the teacher can start writing lyrics that include that vocabulary.
Lyrics should be simple and easy to understand, with repetition of key words to strengthen student The use of rhymes or rhythmic patterns in lyrics can also increase the appeal of the song and make it easier for students to remember new Apart from writing lyrics, teachers can compose a melody that suits the character of the song.
While not all teachers have a formal music background, there are a variety of technological resources and tools, such as the AI Music Generator app, that can assist in creating catchy melodies suitable for learning songs.
Artificial intelligence has emerged as a potent tool in automatic music generation and music recommendation systems, driven by the rapid progress in this field (Mysliwiec, 2.
Technological developments, especially in artificial intelligence such as Suno AI, have significantly changed the paradigm of the music Now, not only professional musicians are dominant, but anyone with a passion for creating songs can do so with the help of Suno AI.
The NLP (Natural Language Processin.
technology used by Suno AI allows users to generate lyrics based on the theme or topic they choose more intuitively.
In addition.
Suno AI also assists in the music composition process, from creating melodies to arranging harmonies and It allows for broad creative exploration, letting users explore various musical styles such as pop, rock, or jazz, and customize Journal IJCIS homepage - https://ijcis.
net/index.
php/ijcis/index the output according to their preferences.
becoming a virtual collaboration partner for musicians and songwriters.
Suno AI opens the door to more inclusive and innovative collaboration, encouraging the creation of more diverse and experimental music.
This is an example of how technology has not only democratized access to creative industries like music but also stimulated creativity among individuals from diverse backgrounds.
In an era of rapid technological development, the use of artificial intelligence (AI) applications such as AI Music Generator, for example.
Suno AI, has provided breakthroughs in English language education, especially in teaching vocabulary.
This application allows English teachers to create songs containing basic English By using the AI Music Generator, teachers can creatively write song lyrics that include basic vocabulary such as names of objects, adjectives, or verbs that are relevant for beginners or children.
This process not only enriches students' learning experiences by utilizing familiar and enjoyable media such as songs but also allows for the personalization of materials to suit their classroom needs.
Thus.
AI applications like Suno AI are not only changing the way teachers teach English but also opening up new opportunities to integrate advanced technology in education in ways that are beneficial and inspiring for students.
In this modern era, artificial intelligence (AI) applications such as AI Music Generator, including Suno AI, have brought significant changes to English education, especially in vocabulary teaching.
Using the AI Music Generator allows English teachers to utilize this tool as a creative tool in creating songs that contain basic English vocabulary.
For example, they can write song lyrics that include the names of objects, adjectives, or verbs that are appropriate for beginner's or children's levels.
This not only harnesses the power of music as a fun and familiar medium for students but also leverages AI technology to personalize learning.
Teachers can integrate specific vocabulary and relevant material in interesting and memorable This not only increases students' interest in learning but also helps them assimilate new Page 192 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index vocabulary more effectively through enjoyable repetition in the context of songs.
With an AI Music Generator, like Suno AI.
English teachers have the flexibility to customize songs to suit their classroom needs.
They can create more dynamic and interactive learning experiences, which not only broaden the way they deliver lessons but also stimulate students' creativity in language learning.
In this context.
AI technology is not only a tool but also a collaborative partner that enables English education to be more inclusive and enjoyable for all students.
Suno AI .
ttps://sunoai.
ai/) is a platform that provides various artificial intelligence (AI) based solutions for various purposes, including song creation, sound processing, and data They offer technology to produce audiovisual content innovatively and efficiently.
Suno AI, known as Suno, is an artificial intelligence music generation program created to produce lifelike songs that blend vocals with instruments or are entirely instrumental.
Suno AI's capabilities in music creation can be effectively utilized by teachers to develop English songs aimed at teaching vocabulary to children.
The platform's AI-driven tools simplify the songwriting process, making it accessible even to educators who may not have extensive musical Teachers' creativity in creating their songs also allows them to adapt songs to their students' styles and preferences.
Teachers can take inspiration from existing children's songs or create something completely new to suit the needs and characteristics of their class.
By using songs as a learning tool, teachers not only strengthen students' vocabulary understanding but also create a dynamic and enjoyable learning environment.
This approach not only increases students' motivation to learn English but also develops their creative skills and builds a positive emotional connection to the learning material.
With Suno AI, an artificial intelligence application capable of creating songs and lyrics based on user input.
English teachers can take on a role similar to a songwriter or musician in an educational context.
They can leverage Suno AI to create songs that are not only musically engaging but also specifically designed to teach children basic English vocabulary.
For example, they can organize song lyrics to include new Journal IJCIS homepage - https://ijcis.
net/index.
php/ijcis/index words, phrases, or language concepts they want to teach their students.
Thus, the use of Suno AI in this context not only creates a fun and engaging learning experience but also enables the creative integration of language learning and musical With Suno AI.
English teachers can assume the roles of songwriters and musicians in education by utilizing its capabilities to create engaging songs tailored for teaching basic English Suno AI allows teachers to organize lyrics around new words, phrases, or language concepts they aim to teach, fostering a dynamic learning experience that seamlessly integrates language acquisition with musical expression.
This innovative use of technology enhances educational engagement through enjoyable and effective teaching methods.
Teachers using Suno AI to create English songs for teaching vocabulary can indeed act as both songwriters and musicians.
Suno AI enables teachers to generate melodies and lyrics tailored to specific vocabulary themes, allowing them to customize songs suitable for children's English language proficiency levels.
Like songwriters, teachers can apply their creativity to craft engaging and easy-to-follow songs, choosing music genres that fit desired atmospheres and They personalize these songs to meet individual students' educational needs and preferences, modifying lyrics and melodies to ensure they effectively support the learning While not requiring extensive musical backgrounds.
Suno AI empowers teachers to express their creativity in English language teaching through fun and engaging songs, positioning them as virtual authors and musicians in educational contexts.
This approach harnesses technology to enhance students' learning experiences with meaningful and enjoyable musical content.
To use Suno AI to create English songs that teach basic vocabulary to children, the first step is to visit the official Suno AI website at https://suno.
com/ and register an account by filling in the form provided.
Once the account is verified, log in to the Suno AI platform to start the song-creation process.
Choose an appropriate music genre for children's songs, such as pop or music specifically for children.
Next, determine Page 193 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index the vocabulary theme we want to teach, such as colors, numbers, or the names of everyday Suno AI will help us generate lyrics and melodies that match the vocabulary input we Manually adjust the lyrics and melody as necessary, to ensure they match the child's level of understanding.
Once the song is finished, you can save it and download it to use in classroom learning activities.
By using Suno AI, the process of creating English songs to teach basic vocabulary becomes easier and more creative, providing an interesting learning experience for To create English songs that are effective in teaching vocabulary using Suno AI, the steps start by determining the theme and vocabulary we want to teach to students.
After that, you can use the Suno AI platform to generate a melody that fits the theme.
Next, write song lyrics that are simple and relevant to the chosen vocabulary, making sure the lyrics are easy to understand by students with various levels of English Suno AI can help us as teachers to create music that is catchy and easy to follow, allowing students to learn vocabulary in a fun and interactive way.
Once the song is finished, it is important to revise and proofread to ensure effective storyline and vocabulary learning.
Record the song or share it via digital platforms so students can access it easily.
Finally, integrate this song into daily learning activities in the classroom to strengthen students' vocabulary understanding through repetition and consistent Thus, using Suno AI to create English songs is an innovative and interesting way to support the vocabulary learning process for students.
Suno AI has provided breakthroughs in English language education, especially in teaching vocabulary.
This application allows English teachers to create songs containing basic English By using the AI Music Generator, teachers can creatively write song lyrics that include basic vocabulary such as names of objects, adjectives, or verbs that are relevant for beginners or children.
This process not only enriches students' learning experiences by utilizing familiar and enjoyable media such as songs but also allows for the personalization of materials to suit their classroom needs.
Thus.
AI Journal IJCIS homepage - https://ijcis.
net/index.
php/ijcis/index applications like Suno AI are not only changing the way teachers teach English but also opening up new opportunities to integrate advanced technology in education in ways that are beneficial and inspiring for students.
leveraging Suno AI, individuals, including educators, can harness its intuitive tools to create educational songs or explore their artistic inclinations without the need for extensive technical skills.
This accessibility marks a paradigm shift in the music industry, where AIdriven innovations like Suno AI empower aspiring musicians and educators alike to participate and innovate in music creation.
Thus.
Suno AI stands out not only for its technological prowess but also for its transformative potential in democratizing music composition and fostering creativity across diverse user demographics.
For teachers.
Suno AI offers a userfriendly interface that guides them through the creation of songs tailored to educational purposes.
They can start by selecting themes such as everyday objects, adjectives describing objects, or verbs depicting actions, all essential for building a foundational English vocabulary in young Using Suno AI's features, teachers can craft lyrics that are simple, repetitive, and engagingAiideal for reinforcing language concepts through memorable melodies.
Moreover.
Suno AI's ability to automatically generate music compositions aligns perfectly with the educational Teachers can choose melodies and musical styles that resonate with children, enhancing their learning experience through auditory stimulation and rhythmic repetition.
This approach not only makes learning enjoyable but also reinforces retention of vocabulary naturally and intuitively.
Furthermore.
Suno AI assists in refining song arrangements and adjusting musical elements to suit the educational objectives and preferences of both teachers and students.
Whether it's creating songs that introduce new words or reinforcing previously learned vocabulary.
Suno AI empowers educators to innovate in their teaching methods, transforming traditional lessons into dynamic and interactive musical experiences.
In essence, by leveraging Suno AI, teachers can harness the power of music to create immersive learning environments where children can absorb English vocabulary Page 194 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index This innovative use of AI not only enhances educational outcomes but also fosters creativity and engagement in language acquisition, paving the way for a more effective and enjoyable learning journey for young Creating simple English songs using AI Music Generator has strong relevance in the context of teaching vocabulary to children.
With AI technology like Suno AI, educators can create songs that are not only musically engaging, but also specifically designed to introduce and strengthen basic English vocabulary.
For example, these songs can teach the names of objects, adjectives, or verbs that are often used in children's daily lives.
Utilizing the AI Music Generator allows teachers to adapt song lyrics to specific and relevant learning themes.
With sophisticated algorithms.
AI can help in writing lyrics that are easy to understand and easy for children to remember.
Through repetition of key words in the lyrics, these songs not only provide a fun learning experience but also strengthen students' memory of the English vocabulary Apart from that.
AI Music Generator also supports the process of composing music that is suitable for children's hearing.
Upbeat melodies and catchy rhythms can enrich their learning By using this technology, teachers can create a dynamic and motivating learning environment, harnessing the appeal of music to facilitate vocabulary comprehension more Overall, the use of AI Music Generator in creating simple English songs for teaching vocabulary provides an innovative and effective approach to education.
This not only enriches teaching methods but also prepares children to develop their language skills holistically through a fun and integrated approach with modern technology.
Creating simple English songs is a very effective approach to teaching vocabulary to These songs not only make the learning process more enjoyable but also help strengthen vocabulary retention through rhythmically structured repetition.
In an educational context, making simple English songs can be done using the following steps.
First, identify the theme or topic you want to teach children, such as the names of objects around them, adjectives to Journal IJCIS homepage - https://ijcis.
net/index.
php/ijcis/index describe objects, or verbs to describe daily For example, songs about objects in the bedroom .
edroom item.
or songs about daily activities .
aily activitie.
Second, write song lyrics that are simple and easy to remember, by including relevant vocabulary according to the chosen theme.
These lyrics should have a pattern that is easy for children to follow and understand.
Third, compose a melody that complements the lyrics and adapts it to the characteristics of children's songs, such as a cheerful rhythm and a melody that is easy to sing.
Fourth, use repetition in songs to strengthen vocabulary memory.
This repetition can be done through repetition of key words in the song lyrics.
Fifth, adjust the style and tone of the song to the personality of the children being taught.
Make sure the song catches their attention and arouses interest in learning.
Finally, use technology such as an AI Music Generator app, such as Suno AI, to assist in music and lyric creation.
This technology can simplify the composition process and allow customization according to educational needs.
By using this approach, creating simple English songs can be an effective tool in introducing and teaching vocabulary to children in an entertaining way.
Suno AI's use of creating English songs to teach basic vocabulary to children offers an innovative and effective approach to education.
With Suno AI, educators can create songs that are not only musically engaging but also specifically designed to introduce and strengthen English vocabulary in a fun and memorable way for First of all.
Suno AI makes it easy for teachers to write song lyrics that match the learning theme, such as the names of objects, adjectives, or verbs that are important in children's daily lives.
Using natural language processing (NLP) technology.
Suno AI can produce lyrics that are simple and easy for children to understand, as well as ensure keywords are repeated rhythmically to strengthen Apart from that.
AI Music Generators such as Suno AI also help in creating cheerful melodies that suit children's listening Enjoyable rhythms and musical styles tailored to their interests can enrich their learning experience while increasing motivation and engagement in the learning process.
Furthermore.
Suno AI enables automatic arrangement and Page 195 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index arrangement of music, ensuring the resulting songs are not only melodically engaging but also support specific educational goals.
Teachers can adapt songs to suitable musical styles to reinforce educational messages and attract children's interest in learning English vocabulary.
Thus, the use of Suno AI in creating English songs to teach basic vocabulary not only increases the effectiveness of teaching but also makes the learning process more interesting and fun for This is an example of how AI technology can be creatively integrated into education, providing a learning experience that is more dynamic and relevant to current IV.
CONCLUSION
Suno offers a range of AI-driven solutions, including song creation, sound processing, and data analysis.
It is known for producing lifelike songs blending vocals with instruments or entirely instrumental compositions Teachers can harness Suno AI to develop English songs aimed at teaching children vocabulary, leveraging its user-friendly tools even without extensive musical backgrounds.
This approach enhances vocabulary understanding in an engaging classroom environment, fostering creativity and emotional connections to learning Teachers can tailor songs to students' preferences and educational needs, creating dynamic learning experiences that promote motivation and creative skills.
Using Suno AI involves visiting their official website .
ttps://suno.
com/), registering, and choosing an appropriate music genre and vocabulary theme .
, colors, number.
for the Suno AI generates melodies and lyrics based on the specified theme, which teachers can further customize to suit different proficiency The platform facilitates easy creation, revision, and sharing of songs, ensuring effective vocabulary learning through enjoyable musical experiences integrated into daily classroom This innovative use of technology supports educators in enhancing language acquisition through engaging and accessible musical content.
The analysis provided details of the steps for using Suno AI to create music, starting with Journal IJCIS homepage - https://ijcis.
net/index.
php/ijcis/index signing up on the website Suno and writing commands or test lyrics.
Users can choose to generate lyrics randomly or opt for an instrumental approach, specifying music genres to define the vibe, mood, tempo, and voice desired.
The tool allows for song extension, editing, adding effects, and changing instruments.
Users can save and share their creations via social media or music streaming platforms.
The researcher's creation is an English children's song titled "Colors of the Rainbow," designed to teach color vocabulary through vivid and descriptive lyrics.
Each verse vividly portrays different colors with cheerful imagery, making it easily understandable for children.
For instance, red is likened to a juicy apple, blue to a vast sky where birds sing happily, and yellow to the bright sun.
The song also introduces colors like orange as a strong tiger, purple as a majestic wizard's robe, pink as a gracefully blooming flower, and brown as a warm teddy bear hug.
This engaging approach not only visually teaches color vocabulary but also introduces it auditorily through cheerful rhythms and melodies.
The song's music style, "children's lively," perfectly complements its cheerful theme.
This style, characterized by upbeat melodies and rhythms that encourage movement, employs light instruments such as acoustic guitar, ukulele, and light percussion.
Such music not only enhances the fun and excitement of children's songs but also maintains their interest, encouraging them to participate enthusiastically in singing and enjoying the music.
With Suno AI.
English teachers can assume the roles of songwriters and musicians in education by utilizing its capabilities to create engaging songs tailored for teaching basic English vocabulary.
Suno AI allows teachers to organize lyrics around new words, phrases, or language concepts they aim to teach, fostering a dynamic learning experience that seamlessly integrates language acquisition with musical expression.
This innovative use of technology enhances educational engagement through enjoyable and effective teaching In research on implementing songs from Suno AI to teach English vocabulary, several limitations need to be considered.
First, this research may be limited by the number and variety of vocabulary covered by the songs Page 196 International Journal of Computer and Information System (IJCIS) Peer Reviewed Ae International Journal Vol : Vol.
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php/ijcis/index produced by Suno.
To evaluate its overall effectiveness, a wider variety of vocabulary is Second, the implementation of Suno AI songs may vary depending on the school context, curriculum, and specific student needs, which may not be explained in depth in this study.
Third, the evaluation method used to measure the impact of Suno's songs on students' vocabulary comprehension and retention needs to be further developed to ensure the validity and reliability of the results.
In addition, this research may not cover all variations in students' perceptions of Suno's songs and how it influences their motivation to study.
Finally, the process of developing the content of Suno's songs and the level of creativity required to ensure their relevance and appeal to students are also aspects that need to be considered in future research.
With these limitations in mind, further research could provide more in-depth and applicable insights into the use of Suno AI in the context of English vocabulary education.
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