Sage Journals: Discover world-class research

Abstract

This study aimed to identify the experiences of students on the reflections of arts education supported by generative artificial intelligence in their abstract art practices. As a qualitative research method, the case study design was used in the study. The sample of the study included 12 last-year students in the Art Education Program of a state university in Türkiye. The implementation process took place within the scope of a 12-week arts major workshop course. Data were collected in semi-structured interviews and using reflective journals and analyzed using the inductive content analysis method. It was determined that the participants were initially prejudiced against generative artificial intelligence and inexperienced in using it, while these attitudes became more positive over time. The participants stated that generative artificial intelligence tools were a source of inspiration, offered support in composition development, and had a guiding role in terms of aesthetics and style. On the other hand, some participants reported that during the arts education process supported by artificial intelligence, they experienced challenges such as communication problems, lack of technical knowledge, and concerns about originality. The results of the study revealed that generative artificial intelligence could be utilized as an educational tool that could support artistic creativity and learning. Hence, it is recommended that elective courses supported by generative artificial intelligence be integrated into the curricula of programs in arts education.

Plain Language Summary

What Students Think About Learning Abstract Art with the Help of Generative AI

This study explored how students experienced using generative artificial intelligence (AI) in making abstract art. The aim was to understand their views on this new approach in art education. Twelve senior students from an art education program in Türkiye joined a 12-week workshop. They used generative AI tools to support their creative process. Researchers collected data through interviews and student journals. At first, many students felt unsure and even negative about AI. They worried that it might reduce their own creativity. Over time, their opinions changed. They said AI helped them generate ideas, plan their work, and improve their artworks. They saw AI as a source of inspiration, not a replacement for creativity. There were still challenges. Some students struggled with the technical side. Others questioned if AI-supported works were truly original. In the end, the study showed that generative AI can support both learning and creativity in art education. The researchers suggest offering elective courses that include AI in art programs.

Keywords

generative artificial intelligence arts education abstract art visual arts

Introduction

Artificial intelligence (AI) is an advanced technology that can imitate skills that are usually considered unique to humans, such as self-improvement (Boden, 2021; Davenport, 2018). Generative AI (GenAI), which is a form of this technology, has the capacity to produce previously nonexistent and unique content based on inputs consisting of text, visuals, sounds, videos, or software code (Lim et al., 2023). Large sets of data are needed for GenAI to learn and develop (Miller, 2022).

Digital technologies supported by AI have become an indispensable part of modern life by transforming the ways individuals think, their behavioral habits, and their communication styles (Chen et al., 2020; Park, 2023). This rapid transformation has also made it necessary for conventional education approaches to be reshaped. These technologies can make students more active in the learning process and increase their interest in classes (Chen et al., 2014; Samala et al., 2025; Wang, 2017; Yim & Su, 2025). In particular, the rapid advancement of GenAI models like ChatGPT significantly influences not only educational environments but also the roles of teachers (Celik et al., 2022; Celik, 2023; Fitria, 2023) and the learning experiences of students (Chen et al., 2023). In this context, it is believed that the shaping of educational processes in the future will largely depend on the development process of AI.

It is seen that today, with the advancements in technology, there has been a substantial transformation in Visual Arts Education. The integration of AI technologies into arts education, in particular, has reshaped artistic production styles, conceptions of creativity, and instruction methods (Kong, 2020; Oksanen et al., 2023; Park, 2023; Pavlik & Pavlik, 2024; Yang, 2020). AI tools with functions such as text-to-image transformation allow students to develop visual ideas, discover different aesthetic approaches, and present unique styles of expression (Heaton, Low & Chen, 2024). In this context, AI used in arts education environments is considered not only a technological tool but also a pedagogical innovation.

The findings of this study are directly related to the personalized learning approach, as they provide a flexible structure in terms of content for students. In this context, AI-supported learning environments have the potential to foster an approach that places the student at the center of the learning process (Luan et al., 2020). Indeed, students tend to be more satisfied in such personalized learning environments, as they can receive timely feedback through AI (Zawacki-Richter et al., 2019). While AI offers many new opportunities in learning environments, it also brings along certain challenges that need to be addressed. The use of AI tools may pose several issues that need to be resolved, such as the lack of technical expertise, the risk of algorithms occasionally producing erroneous or misleading results, challenges related to originality, and concerns that students may become overly dependent or less engaged in the learning process.

Recent studies have demonstrated that AI-based tools have had various effects on student experiences through their integration into arts education. It has been observed that AI tools influence several factors that are important in the learning processes of students, especially self-efficacy (An, 2024), creativity (Heaton et al., 2024; Vartiainen & Tedre, 2023; Yang & Shin, 2025), participation (An, 2024; Heaton et al., 2024; Pavlik & Pavlik, 2024; Sáez-Velasco et al., 2024), and motivation (Heaton et al., 2024; Liao & Cao, 2025; Sáez-Velasco et al., 2024; Su & Mokmin, 2024). These studies have shown that AI tools have turned into powerful learning tools for students.

The Importance and Purpose of the Research

Although there are several studies on the usage of AI in educational environments, there is very limited information about the role of these technologies in artistic practices carried out in workshop environments (Su & Mokmin, 2024). Current research has primarily focused on the effects of AI-based digital media technologies on art education (Liao & Cao, 2025), the role of GenAI in art and design education within higher education (Yang & Shin, 2025), and both the positive and negative impacts of AI tools on art education (Zhang et al., 2025). In addition, studies have examined the influence of intelligent drawing systems on students’ self-efficacy and engagement (An, 2024), the role of AI in shaping student experiences (Fang & Jiang, 2024), and art educators’ experiences with integrating AI into their teaching practices (Heaton, Low & Chen, 2024). Moreover, research has addressed technological and pedagogical factors in AI-supported design courses (Jiang et al., 2024), explored the impact of AI-based visual generation on creativity within STEAM education (Lee et al., 2024), investigated students’ attitudes toward AI-based drawing technologies and the factors shaping those attitudes (Wang, 2024), and conducted comparative analyses of the experiences of artists and non-artists in AI-supported art production. However, the literature lacks research investigating students’ experiences with abstract art practices in the context of GenAI-supported art education. This gap clearly highlights the research deficiency. Therefore, this study aims to explore the experiences of fourth-year students in the Art Education Program regarding the reflections of GenAI-supported art education on their abstract art practices.

Research Questions

The central research question of this study is: How do fourth-year students in the Art Education Program evaluate the impact of GenAI-supported art education on their abstract art practices? In line with this overarching question, the study addresses the following sub-questions:

What were the students’ initial impressions of the GenAI-supported art education process?

How did the students experience the contributions of GenAI to their artistic creativity and design processes?

What challenges did the students encounter during the GenAI-supported art education process?

What strategies did the students develop to effectively integrate GenAI into their abstract art projects?

Literature Review

Artificial Intelligence and Personalized Learning in Art Education

Personalized learning, unlike the one-size-fits-all approach of traditional education, places the individual characteristics of the student at the center and offers flexibility in terms of pace, method, and content (Bernacki, Greene, & Lobczowski, 2021). In this context, one of the key factors enhancing the effectiveness of personalized learning is AI (Vorobyeva et al., 2025). Education supported by AI offers the opportunity to personalize learning processes by putting the needs of students at the center. It also contributes to the equality of opportunities by increasing the participation of students in education (Chassignol et al., 2018).

Art studios include skill-based courses such as painting and drawing. However, providing one-on-one guidance to each student can be both exhausting and time-consuming for instructors. As a result, traditional teaching methods often fail to sufficiently account for students’ differences in abilities, learning pace, and interests, thereby limiting the development of their creative potential (Chen, Mokmin, & Su, 2025). In contrast, AI tools generate content tailored to students’ interests and abilities by responding to given prompts, thereby supporting and fostering their creativity (Zhou & Lee, 2024). In this way, students are able to develop both their technical skills and their artistic expression at their own pace and in their unique style. Similarly, Sáez-Velasco et al. (2024) showed that an AI-based arts education intervention increased the interest of students in learning, raised their levels of participation, and had a motivating function.

In art education supported by GenAI, the role of educators is crucial for the effective implementation of personalized learning. The adoption of AI in education depends largely on teachers’ willingness and ability to integrate these technologies into their courses (Barrera Castro et al., 2025). Therefore, art educators need to acquire new skills related to both technology and teaching methods in order to use AI effectively in the art education process.

The Role of Artificial Intelligence in Creative Processes

AI tools are developing new methods in art production, reshaping the creative process. These tools ease time-consuming or technically demanding tasks for artists. This allows artists to express their creativity more freely, without being constrained by technical barriers.

Even though AI does not possess cognitive characteristics unique to humans, it can influence and transform people’s art and aesthetic approaches (Tao, 2022). At this point, it is worth noting that AI not only brings about an aesthetic change in human art but also offers a substantial transformation in the identity of the artist. The interaction between human creativity and AI makes it necessary for us to rethink the definition of arts and their evaluation criteria (Tao et al., 2018). Therefore, in the evaluation of artworks produced with the support of AI in terms of creativity and originality, conventional approaches should be reconsidered (Tao, 2022).

Studies conducted in the context of creativity have revealed that AI applications contribute to students, especially in fields such as idea creation, stylistic diversity, and the creation of alternative compositions (Heaton et al., 2024; Vartiainen & Tedre, 2023). Su and Mokmin (2024) reported that using AI had a motivating effect on students. The authors stated that, in particular, the interest of students in artistic practices increased, and their creativity improved. Yang and Shin (2025) showed that students who used GenAI tools could control the creative process in a faster, more flexible, and more effective way. Xu (2024) emphasized that for students to develop creativity, their skills of effective collaboration with AI should be supported. Moreover, Pavlik and Pavlik (2024) found that AI improved the critical thinking skills of students and contributed to their visual analysis processes.

Some art educators are concerned that AI might completely diminish human creativity. In contrast, many researchers argue that it is more productive to use AI tools not as direct creators of artistic output, but as collaborative instruments that support and enhance the artist’s imagination (Amini, 2025; Park, 2023).

Concerns Regarding the Use of Artificial Intelligence in Art Education

Although AI tools can offer many benefits in education, their integration into learning environments also raises a number of concerns. Since AI applications collect and process students’ personal and academic information, concerns have arisen regarding data security (Ali et al., 2024; Huang, Saleh, & Liu, 2021; Matos et al., 2025). The unconditional trust placed in AI to operate flawlessly (Alwaqdani, 2025) also emerges as a significant risk. While educators play a key role in effectively implementing personalized learning through AI tools, there is also a risk that the emotional interaction between students and teachers could be weakened (Al-Tkhayneh, Alghazo, & Tahat, 2023). The unequal access of students to AI tools may lead to disparities in educational opportunities. The rapid pace of technological advancement may pose challenges for educational institutions in adapting effectively. Technical issues such as insufficient infrastructure may hinder the effective use of AI in classrooms (Song, Mak, & Chen, 2025).

An artwork generated by AI raises the issue of originality, as it is based on patterns derived from a large number of past works (Park, 2023). On the other hand, there are concerns that accustoming students to ready-made information may reduce their critical thinking (Alwaqdani, 2025) and potentially render them more passive (Miralay, 2024).

Method

This study is practice-based art education research that aims to examine the effects of GenAI-supported art education on students’ abstract art practices. Accordingly, uncovering students’ perspectives on the process required the adoption of a qualitative research method. The study, which was carried out during an arts major workshop course, adopted the case study approach, which is a qualitative research method. In the case study method, a system with certain boundaries is investigated in detail using different data collection instruments (Creswell, 2021; Merriam, 2009; Yıldırım & Şimşek, 2021). In case studies, it is essential to clearly define the phenomenon under investigation and its boundaries (Creswell, 2019). In this context, the case of this research was defined based on the participants’ experiences during the GenAI-assisted abstract art education process conducted in the fall semester of the 2024–2025 academic year. In single-case designs, a single event is selected and examined in depth within its full context (Yıldırım & Şimşek, 2021). Accordingly, this study adopted a single-case design. The experiences of a single group of participants within a specific context were examined holistically, using multiple data collection instruments throughout the process.

Participants

This study was conducted with a total of 12 fourth-year students, 2 male and 10 female, enrolled in the Art Education Program at a public university in Türkiye. Although the participants had a general background in art, they possessed no prior experience with abstract art. They were introduced to GenAI tools for the first time during the workshop sessions conducted as part of this study.

In case study research, the purposive sampling method is generally considered more appropriate (Merriam, 2009). Accordingly, purposive sampling was also employed in the selection of participants for this study. In selecting participants, the primary criterion was that individuals had a certain level of experience in artistic practice and demonstrated sufficient competence to produce abstract art, as the study was conducted within the scope of GenAI-supported abstract art practices. Since the study was carried out in an educational setting where the researcher was actively teaching, it was also possible to access a conveniently available group.

Data Collection Instruments

A semi-structured interview form was used as the primary data collection instrument. At the first stage of developing the form, previous studies discussing the relationship between AI and the arts were reviewed, and the main concepts about the topic were identified. Then, the main themes that needed to be addressed in the interviews were determined with the support of the opinions of three academicians specializing in arts education. Brainstorming was performed to create open-ended questions under these themes. In this process, a semi-structured interview form consisting of seven questions was created. A pilot interview was held with one participant to test the applicability of the interview form. During the pilot interview, the scope of the responses of the participant to the questions and the comprehensibility of the questions were evaluated. Based on the results of this evaluation and expert opinions, it was determined that the interview form was applicable. The questions on the form were prepared to collect information about the first impressions of the participants regarding GenAI, their views on the effects of GenAI on the arts education process, its contributions to design and creativity, the challenges that the participants encountered, and their recommendations for solutions. The final version of the interview form is provided in Appendix 1.

Implementation

At the beginning of the implementation process of the study, a briefing meeting was held with students who volunteered to participate in the study. In the meeting, the participants were given detailed information about the purpose, contents, and steps of the study. The planning and implementation process of the study was shaped with the contributions of three academicians specializing in the field of visual arts teaching. The study was carried out within a 12-week period in the fall semester of the 2024-2025 academic year.

The study protocol consisted of five main stages. In the first stage, the participants were given education about the main principles and historical development of abstract art. The aspects of abstract art that distinguish it from the conventional understanding of art were opened to discussion in the workshop environment. In the second stage, selected examples of artworks from different periods of abstract art were presented to the participants. After examining the artworks, the participants took part in reproduction activities. In the third stage, the participants were provided with theoretical information about the usage of AI in the field of arts, and this information was supported with applied examples. In the fourth stage, using GenAI-assisted applications, the participants created sketches for their works of abstract art. In this process, AI was envisioned as a teacher, and it was recommended that it be used as a guiding tool for the development of ideas by the participants. Each phase of the sketching process was carried out through one-on-one interaction between the participant and the AI tool. At this stage, both ChatGPT and Copilot were used for generating textual and visual content. The students utilized the standard and free versions of these tools. In the fifth and last stage, based on the sketches that they created with the assistance of GenAI, the participants created works of abstract art on canvas. The participants, who used oil paint during this process, kept interacting with the AI tool.

As one of the important components of the study, from the third stage of the study to the end of the semester, the participants recorded their experiences by keeping reflective journals. The researcher also kept comprehensive notes by observing the process. At the end of the 12-week implementation process, semi-structured interviews were held with the participants, and their views about the process were collected. All interviews were recorded using an audio recorder with the approval of the participants. Each interview lasted about 18 min on average. All documents created within the scope of the study were analyzed and archived by the researcher. With the help of this systematic approach, the effects of GenAI-assisted arts education on the abstract art practices of the participants were examined in a comprehensive manner. Additionally, important information about how GenAI could be integrated into abstract art practices was obtained. Detailed information about the study protocol is given in Figure 1.

Figure 1.

Study protocol.

Data Analysis

In the first phase of data analysis, the statements of the participants were transcribed verbatim. A separate file was created for each participant, and these files were named R4-1, R4-2, etc. The letter “R” represents the initial of the word Respondent, and this usage was adopted in order to ensure the complete confidentiality of participants’ identities and to facilitate the systematic analysis of the data. In qualitative research, participants can be identified in various ways; for instance, some studies employ pseudonyms or naming conventions based on participant preferences (Wang et al., 2024). In this study, priority was given to maintaining confidentiality, and a systematic coding method was adopted in the file coding process. In the second phase of data analysis, the transcripts of the interviews were analyzed using the content analysis method. In this process, codes and themes were determined using an inductive approach. A method identifying the similarly expressed views of the participants was taken as a basis in the analysis (Patton, 2018; Yıldırım & Şimşek, 2021). Repeated statements from the participants reflecting their experiences regarding AI-assisted arts education constituted the basis of coding. The codes obtained based on these statements were grouped according to similarities and differences in terms of content and combined under themes. As a result of these analyses, all codes were collected under four different themes.

To ensure the reliability of the coding process, first, the researcher and another person expert in their field independently identified codes and themes. Afterward, the two coders gathered and conducted a comparative evaluation of the identified codes and themes. Next, the codes and themes were discussed again by requesting the opinions of a third expert. A consensus-based process was followed, and the finalized list of codes was obtained. The detailed thematic codebook is provided in Appendix 2. In the second stage, the identified codes and themes were associated with reflective journal entries. This step contributed to the confirmability of the obtained findings. The reflective journals were included in the analysis process as an additional data source supporting the data obtained from the interviews. The themes and categories obtained during the inductive content analysis process are presented in Figure 2.

Figure 2.

Themes and categories obtained in inductive content analysis.

The Researcher’s Role

The researcher assumed a dual role in the process, both guiding and collecting data. This was considered a potential source of bias; however, efforts were made to minimize it by avoiding leading statements. Instead, the researcher primarily maintained a guiding position. To ensure the objectivity of the data, students’ reflective journals and interview transcripts were used as primary sources. At every stage of the study, scientific and ethical principles were strictly observed. The identities of participants were kept confidential, and any expressions that could reveal their identities were removed from the text. The process was conducted on a voluntary basis; informed consent was obtained from students prior to the interviews, and all data were used solely for research purposes.

Results

In this section, students’ perspectives on the impact of GenAI-supported art education on abstract art practices are presented across four themes. First, their initial impressions of artificial intelligence are discussed. Second, the contributions of GenAI to creativity and the design process are examined. Third, the challenges encountered during the process are evaluated. Finally, the strategies for effective use and the recommendations developed by the students are presented.

Initial Impressions of Generative Artificial Intelligence

Table 1 presents the participants’ perspectives regarding their initial impressions upon encountering GenAI. These perspectives were clustered around five codes: prejudice, excitement, astonishment, hesitation, and lack of experience.

Table 1.

Views of Participants on Their Initial Impressions of Generative Artificial Intelligence.

Theme	Codes	Participants
Initial Impressions of Generative Artificial Intelligence	Prejudice	R4-2, R4-6, R4-8, R4-9, R4-10
	Excitement	R4-11
	Surprise	R4-8, R4-9
	Reservation	R4-6, R4-7, R4-8, R4-12
	Lack of Experience	R4-1, R4-3, R4-8, R4-5, R4-9

It was determined that a significant part of the participants approached the GenAI-assisted arts education process with prejudice, and they initially had some reservations, especially about the ability of GenAI to contribute artistic creativity. R4-6 said, “I had doubts about whether AI could give me what I wanted,” to express their reservations. Regarding this process, some participants said they felt excitement (R4-11) or surprise (R4-8, R4-9). On the other hand, many participants reported that they did not have any experience regarding GenAI at the beginning of the process (R4-1, R4-3, R4-5, R4-8, R4-9). This was reflected in the statement shared by R4-9, “I did not know how to use AI. Initially, I did not know what I would encounter.” In general, these results revealed that the participants displayed different initial reactions to GenAI.

The reflective journals of the participants supported the results obtained based on the semi-structured interviews. The reactions of the participants in the first stage of the GenAI-assisted arts education process, such as prejudice, excitement, surprise, reservations, and lack of experience, were observed in their reflective journals. However, over time, these reactions were replaced by the desire to learn and discover, and this transition was clearly seen in the reflective journals. In the reflective journal of participant R4-6, “At first, I did not know what to do or what type of thing I needed to draw. Then, I communicated with the AI and wrote prompts. This way, it provided me with some examples. I created my own design with inspiration from these examples.” This statement also supports the situation. In addition, the screenshot illustrating the student’s interaction with AI (see Figure 3) concretizes this finding.

Figure 3.

Screenshot (1) of interaction with AI.

Contributions of Generative Artificial Intelligence to the Artistic Process

This section presents the participants’ views on the contributions of GenAI to the artistic process. Table 2 presents the main categories and codes that emerged from the analysis, providing a detailed insight into how GenAI inspired students’ design and creativity processes.

Table 2.

Views of Participants on the Contributions of Generative Artificial Intelligence to the Artistic Process.

Theme	Categories	Codes	Participants
Contributions of Generative Artificial Intelligence to the Artistic Process	Design and Creativity	Providing Inspiration	R4-1, R4-2, R4-3, R4-4, R4-5, R4-6, R4-7, R4-8, R4-9, R4-10, R4-11, R4-12
		Support for Composition	R4-2, R4-3, R4-4, R4-5, R4-7, R4-8, R4-9, R4-11, R4-12
		Generating Alternative Designs	R4-1, R4-4, R4-6, R4-7, R4-11, R4-12
		Blending Digital and Conventional Techniques	R4-3, R4-9
	Aesthetics and Style	Use of Color and Light	R4-1, R4-2, R4-3, R4-4, R4-5, R4-7, R4-8, R4-9, R4-10, R4-11, R4-12
	Aesthetics and Style	Style Development	R4-1, R4-2, R4-3, R4-4, R4-5, R4-6, R4-7, R4-8, R4-9, R4-10, R4-11, R4-12

Considering the statements of the participants in the design and creativity category, it is seen that GenAI had a particularly significant role as a source of inspiration in abstract art practices. All participants emphasized the inspiring aspects of GenAI and stated that this technology functioned as a tool that affected the artistic process. Such that, R4-1 clearly expressed this situation by saying, “I can create my own unique design through inspiration from the options offered by AI.” Most participants also reported that they used GenAI effectively in the process of creating compositions. Participants including R4-2, R4-4, and R4-7 stated that they shaped their compositions in line with the suggestions provided by GenAI. Some participants revealed that they could produce different design alternatives thanks to the multiple options provided by GenAI. R4-11 said, “We asked again when we did not like [it] and received different alternatives,” emphasizing the contribution of GenAI to the design process. Some participants thought that the simultaneous usage of digital and conventional methods was an effective method in terms of the development of novel and unique ways of expression in the arts. R4-3 and R4-9 were among the participants who adopted this approach.

In the context of aesthetics and style, the vast majority of the participants were found to utilize the suggestions provided by GenAI about the use of colors and lighting. The statement by R4-7 confirming this situation was as follows: “I made the painting stronger using the light-shade arrangements recommended by the AI.” Furthermore, many participants reported that they arranged the recommendations provided by GenAI based on their own interpretations, and this process contributed to the improvement of their individual styles.

The reflective journals of the participants also showed that they reshaped the recommendations provided by GenAI from their personal points of view. In the reflective journal of participant R4-5, “I did not use the visuals that I obtained from the AI in their raw form, but I added my own interpretation and style.” through this statement, they demonstrate their effort to reflect their own artistic identity. In addition, the visuals (see Figures 4 and 5) presenting selected examples of AI-generated outputs, together with the student’s own work inspired by these outputs, concretize this finding. Likewise, the statement by R4-3, “I combined the traditional and the modern by transforming cultural designs into a digital frame,” was an indicator that they were in search of developing novel and unique ways of expression through the blending of digital and conventional methods. Moreover, R4-9 said, “I took the colors and textures from the AI but arranged the lighting effects myself,” indicating their effort to preserve their originality while getting help from GenAI in their abstract artworks.

Figure 4.

Image created with ChatGPT.

Figure 5.

Student’s artwork based on AI outputs.

Challenges in the Use of Generative Artificial Intelligence

Table 3 provides a detailed account of the challenges participants encountered in their abstract art projects conducted within the workshop setting during the GenAI-supported art education process.

Table 3.

Views of Participants on the Challenges of Using Generative Artificial Intelligence.

Theme	Categories	Codes	Participants
Challenges in the Use of Generative Artificial Intelligence	Usage-Related Limitations	Communication Difficulties	R4-1, R4-3, R4-4, R4-6, R4-7, R4-8, R4-10
		Limitations of Free Services	R4-3, R4-5, R4-6, R4-8, R4-9, R4-11
		Lack of Technical Knowledge	R4-1, R4-4, R4-6, R4-7, R4-8, R4-10
		Misleading Results	R4-4, R4-7, R4-8
	Pedagogical Concerns	Concerns about Originality	R4-1, R4-2, R4-3, R4-4, R4-5, R4-8, R4-9, R4-11, R4-12
		Concerns about Encouraging Laziness	R4-4, R4-8
		Overestimation of AI Capabilities	R4-3, R4-10

The participants mentioned the difficulties they encountered in this process within the scope of usage-related limitations and pedagogical concerns. It was determined that usage-related limitations, in particular, directly affected the productivity of the arts education process and led the participants to occasionally experience a loss of motivation during the process. Some participants stated that they found it difficult to communicate effectively with GenAI in this process. They reported that it took time to prompt the AI correctly, and this necessitated a learning curve. For example, saying “It can be difficult to have the AI understand what exactly I want to say,” R4-1 pointed out that this process had an aspect that required learning. It was also observed that the limitations of free services affected the arts education process of many participants adversely. R4-6 mentioned this issue, saying, “There was a daily usage limit because we used a non-premium account,” clearly highlighting the access problems they had.

Lack of technical knowledge was specified as an important factor that made the process difficult for a considerable number of participants. According to R4-10, who said, “I struggled with it every day for about a week and only then was I able to really understand it,” it took time to understand the logic of GenAI and become able to give it effective prompts. This difficulty was also mentioned by R4-4, who said, “I found it difficult to choose the right words because I was unable to understand AI completely.” Some participants also noted that GenAI occasionally produced out-of-context or misleading outputs. R4-8 explained this situation as follows: “It gave very superficial or irrelevant answers to the questions I asked.”

Concerns about originality were among the most frequently encountered topics under the pedagogical concerns category. Most participants specifically emphasized that originality must be preserved during the usage of GenAI. Clearly highlighting their sensitivity regarding the preservation of originality, R4-2 said, “After all, this is the purpose of our work. Inspiration, not imitation.” R4-8 argued that the ready-made ideas offered by AI carried a risk of leading to laziness in students and that this could adversely affect the learning process. Finally, according to some participants, GenAI was assigned much more meaning than necessary, and its capabilities were overestimated. For instance, R4-3 expressed the following: “The fact that AI could produce art this fast and this easily frightened me.”

In the reflective journals, it was observed that the participants expressed different emotional responses while trying to cope with usage-related limitations and pedagogical concerns. For example, in the reflective journal of participant R4-7, “Despite doing everything, I was unable to get the visual that I wanted, and this disappointed me,” expressed that the process demoralized them and affected their motivation negatively. Likewise, R4-8 said, “I felt like I failed when I could not get something from the AI. However, the problem was not about me but about my lack of knowledge about how to control it,” suggesting that their inability to establish healthy communication with the technology damaged their sense of self-efficacy. Nevertheless, some participants tried to turn the limitations that they encountered into opportunities for learning. The statement by R4-5, “I struggled a lot at first but finally learned what to do after I made mistakes,” indicated that the participant considered this process as an area of exploration and development. In the context of pedagogical concerns, the statement by R4-12, “I questioned where I was when too much AI was involved in the process,” revealed their need to rethink their position and role as a student. Still, the overcomplicated meanings attributed to GenAI led some participants to experience feelings of competing against technology. R4-10, who said, “It sometimes makes me feel like it is more creative than me,” expressed their concern about the potential of AI to occasionally overshadow human creativity.

Approaches to the Art Education Process Supported by Generative Artificial Intelligence

Table 4 presents a detailed framework of the categories concerning the Individual Approaches developed by the participants during the GenAI-supported art education process, as well as the Institutional Support Requirements they identified for conducting the process more effectively.

Table 4.

Approaches of Participants to the Arts Education Process Supported by Generative Artificial Intelligence.

Theme	Categories	Codes	Participants
Approaches to the Art Education Process Supported by Generative Artificial Intelligence	Individual Approaches	Step-by-step guidance	R4-1, R4-7, R4-10
		Trial-and-error method	R4-1, R4-3, R4-5, R4-7, R4-9, R4-10, R4-11, R4-12
		Providing clear and detailed instructions	R4-8, R4-9
		Progressing through question-and-answer	R4-5, R4-9, R4-10
		Using AI as a guide	R4-7, R4-8, R4-9, R4-10
		Originality-oriented use	R4-1, R4-2, R4-3, R4-4, R4-6, R4-7, R4-8, R4-9, R4-10, R4-11, R4-12
	Institutional Support Requirements	Attending training seminars	R4-1
		Access to free software	R4-5, R4-11
		Suggestions for elective courses	R4-5
		Receiving technical support	R4-10

It was found that while performing their GenAI-assisted abstract art activities, the participants developed individual approaches. In this process, some participants adopted an approach based on step-by-step instructions, whereas others used the trial-and-error method. For example, R4-1 pointed out that giving prompts step by step while working with GenAI provided more effective results. On the other hand, some participants chose to produce solutions by giving the AI detailed and clear instructions or using the question-and-answer method. R4-8 stated that the results they obtained were particularly more satisfactory when they gave more detailed and clearer prompts to the AI. It was seen that some participants used GenAI as a guide. This was expressed by R4-10 as follows: “I can say that it steered me like a guide in this process.” Notwithstanding, most participants not only gave prompts to GenAI but also adopted an originality-oriented use approach by reshaping their designs based on the feedback and directions provided by the AI. R4-9 described this situation as follows: “AI gave me a general frame, but it [my work] became more original with my touches.”

The participants offered some recommendations to increase the productivity of GenAI-assisted arts education programs. These recommendations included organizing training seminars, facilitating access to free software tools, offering elective courses, and providing technical support. R4-1 stated that training seminars should be organized for students to use AI more effectively. R4-5 and R4-11, who expressed that it would be a great convenience to access free AI tools, especially for financially disadvantaged students, thought that such a solution would support equality of opportunities. R4-10 asserted that students should not be deprived of help against technical difficulties, and establishing technical support units in this context would make the process more robust. R4-5 recommended that elective courses such as “Design Using Artificial Intelligence” be included in arts education curricula.

According to their reflective journal entries, the participants developed individual approaches to overcome the challenges that they encountered during the GenAI-assisted arts education process. In the reflective journal of participant R4-1, “The greatest challenge I face was that it [AI] sometimes never understood what I asked of it. To resolve this situation, I divided my commands into stages in my head, gave them one by one, and [I saw that] more productive results could be obtained when I wrote it in a step-by-step manner.” Through this statement, the participant emphasized that they learned how to communicate more effectively with AI during the process. R4-12, who adopted the trial-and-error method as a learning approach, stated the following: “As I gave it [AI] the wrong prompts, I came to understand what to do better. Each erroneous try led me to a better outcome.” The participants thought of the interaction they had with AI not only as a context limited to a technical tool but also as a partnership in which AI guided their arts education process and exchanged ideas with them. This was clearly seen in the statement by R4-7 as follows: “AI was not just a tool, but it was like a partner who gave me ideas.” Indeed, the interaction screenshot containing the suggestions the student received in response to their questions to AI (see Figure 6) provides concrete support for this finding. Furthermore, the reflective journal entries of the participants demonstrated how their experiences during the arts education process changed over time and what forms of support they needed in this process.

Figure 6.

Screenshot (2) of interaction with AI.

Discussion

This study focuses on revealing students’ perspectives regarding the impact of GenAI-supported art education on their abstract art practices. Within this scope, student experiences were examined and discussed in relation to four sub-research questions.

The results of the analyses showed that the participants had prejudices and concerns about GenAI and were inexperienced in using it at the initial stages of the arts education process. In fact, such reactions can be considered a frequently encountered form of resistance seen in times when new and unfamiliar technologies are introduced into an educational environment for the first time (Vartiainen & Tedre, 2023). Similarly, in the study conducted by Pavlik and Pavlik (2024), it was reported that students experienced feelings of surprise, hesitancy, and confusion when they first met images produced by GenAI. However, at further stages of the process, this uncertainty turned into an area of exploration for the participants, and the interaction formed with GenAI became a learning opportunity. The statements included in the reflective journals of the participants about this transformation also confirmed these results. A similar transformation was indicated in the study performed by Heaton et al. (2024) in Singapore. The authors stated that the initial concerns of students about GenAI-assisted arts education were replaced by a more positive and productive learning process over time.

According to the statements of the participants in this study, GenAI inspired them during the arts education process, supported them in terms of creating compositions and developing different design options, and reinforced creativity from different aspects by allowing for the combined usage of digital and conventional elements. In this study, all participants believed that GenAI had an inspiring role in the arts education process. This finding can be explained by the students’ need for external stimuli during their creative processes (Csikszentmihalyi, 1997). In the course of art education, students often experience blockages when attempting to develop new ideas. The alternative suggestions offered by GenAI tools can help them overcome these creative impasses. The results of some studies in the literature supported these findings. Similarly, Heaton et al. (2024), Vartiainen and Tedre (2023), and Xu (2024) reported that AI-assisted tools offered a source of inspiration to students in arts education in terms of developing new ideas. According to Zhao et al. (2021), AI is particularly effective in presenting new ideas fast. Additionally, Mokmin and Ridzuan (2022) and Tiwari et al. (2024) emphasized that AI could be an effective tool that supports students in the learning process and facilitates learning. In this sense, the role of AI in both artistic and educational processes is increasingly more important in today’s world, where educational technologies rapidly change (Hall et al., 2022).

In the literature, while some studies argue that AI plays an inspiring role in creative processes, there are also perspectives suggesting that it may diminish the uniqueness of human artistic contribution (Hall & Schofield, 2025). Since art is a distinctly human endeavor, the artist’s contribution is essential to the aesthetic value of a work. Therefore, in AI-generated artworks, the question arises as to what extent these human qualities can truly be attributed to the machine (McCormack, Gifford & Hutchings, 2019). In this context, a study found that although AI-generated artworks resemble human-made art in technical terms, participants evaluated them as more superficial and emotionally weaker. This situation demonstrates that art is not solely about aesthetics; it also derives meaning from human experience and emotions (Mazzone & Elgammal, 2019). Nevertheless, although GenAI has controversial and problematic aspects, it not only opens new horizons for art education but also paves the way for novel and creative experiences in art itself.

The data collected in this study demonstrated that GenAI not only supported design and creativity but also took on a guiding role in terms of aesthetics and style. The vast majority of the participants thought that their artistic works were enriched by the recommendations provided by GenAI regarding plastic elements such as color, lighting, and shading. This situation directly contributes to the visual language development processes of students and helps them utilize artistic techniques in a more conscious and controlled manner (An, 2024; Chiu et al., 2024; Pavlik & Pavlik, 2024). The expression in the reflective journals of the participants indicated that they reshaped the suggestions provided by GenAI with their own interpretations and made efforts to preserve their artistic originality in this process. This result suggested that while AI systems guide students in terms of technique and aesthetics, they also offer a creative space where they can maintain their unique artistic approaches (Chiu et al., 2024; Heaton et al., 2024; Pavlik & Pavlik, 2024). Nevertheless, although some researchers accept that AI can be an inspirational tool, they argue that it cannot replace the human factor (Sáez-Velasco et al., 2024; Yang & Shin, 2025). Indeed, AI should not be regarded as a replacement for artists’ creativity, but rather as a collaborative partner that supports and enhances artistic creativity.

In this study, the participants expressed that they encountered both technical difficulties in usage and pedagogical concerns during their abstract art production practices involving GenAI. Usage-related limitations included the difficulties experienced by the participants in communicating effectively with GenAI, their lack of technical knowledge, the access-related limitations of free versions of software, and the misleading outputs occasionally produced by the algorithm. Some participants underlined that, especially at the beginning of the process, it took time to establish meaningful communication with GenAI, and their lack of technical knowledge in this stage made the process even more difficult. This showed that not only lack of technical knowledge but also skills of effective communication with AI were determinants in the artistic education process. Indeed, An (2024), Sáez-Velasco et al. (2024), and Tiwari et al. (2024) pointed to similar challenges. Vartiainen and Tedre (2023) found that prospective teachers had difficulties in creating correct and effective prompts while working with AI tools, and this situation could sometimes affect the arts education process adversely. Some participants claimed that the limited access opportunities offered by free AI tools interrupted their learning processes. For this reason, to implement AI-assisted arts education in a sustainable manner, it is highly important to eliminate access-related limitations (Su & Mokmin, 2024). One of the main challenges participants encounter during the art education process is misleading outputs. In certain cases, AI may provide incorrect or inconsistent information. This can lead students to learn fundamental concepts inaccurately or incompletely. Moreover, students may also misinterpret which path they should follow in their artistic production (Lee et al., 2024).

Another important aspect of the challenges experienced by the participants during their abstract art production practices supported by GenAI was their pedagogical concerns. These concerns included those about originality, those about the encouragement of laziness, and the overestimation of the capabilities of AI. In the reflective journals of the participants, it was observed that during the AI-assisted arts education process, they experienced uncertainties regarding whether an idea belonged to them or was a recommendation offered to them by AI. This was associated with the weakening of the contribution of individuals to the creative process and the reinforcement of their concerns about originality (Heaton et al., 2024). Likewise, it was claimed that the ready-made visuals offered by AI could make students more passive by reducing their active participation in the cognitive process (Miralay, 2024).

In traditional art, an approach is followed in which the human presence is fully embedded in the creative process. However, the art produced by AI algorithms is often debated in terms of its originality. New works generated by algorithms that draw upon existing pieces may lead students, often without realizing it, to produce visuals that resemble one another (Black & Chaput, 2024). In this context, AI-generated artworks may give rise to significant ethical debates concerning originality, ownership, and artistic value (Amini, 2025). In this regard, the possible negative consequences of integrating AI into art education should not be overlooked. In particular, there is a risk that students may lose their own artistic autonomy. Another drawback is the uncritical imitation of aesthetic styles suggested by AI by students. An excessive reliance on AI-generated solutions may hinder students from exercising and developing their own creative capacities. To prevent these drawbacks, students should be encouraged to approach AI with a critical perspective. AI should be regarded not as a replacement for creativity, but merely as a tool that contributes to the process (McCormack et al., 2020).

The participants developed individual approaches and sought solutions based on their experiences against the usage-related limitations and pedagogical concerns that they experienced during the GenAI-assisted arts education process. These approaches involved the efforts of the participants to understand how GenAI worked, as well as their initiatives to make this technology a natural part of their abstract art practices in the workshop environment. This was not just a strategy for finding solutions to the problems they encountered, but it also reflected the desire of the participants to maintain their originality and establish a relationship with technology in a responsible manner. According to Heaton et al. (2024), students used AI not only as a tool that automatically produced content but also as a guide that directed the process and provided feedback. This approach allows students to participate in the arts education process more actively and maintain their originality by refraining from being completely dependent on AI. This situation reveals the importance of the human-machine interaction. Moreover, there is a clear need for institutional support for individual approaches to be more effective. For the more effective integration of GenAI into arts education processes, the participants offered recommendations such as providing technical support, offering elective courses in this field in curricula, organizing informative seminars, and making financially accessible AI tools available. Su and Mokmin (2024) asserted that for the effective utilization of GenAI by both students and teachers, it is very important to offer accessible, easy-to-use, and functional tools. Vartiainen and Tedre (2023) stated that while prospective teachers were using text-to-image AI tools, they needed not only technical but also pedagogical support, and this support played an important role in the sound and effective progression of the process.

Conclusion

In this study, participants’ perspectives on GenAI-assisted abstract art practices conducted in a workshop setting were examined. The findings revealed that participants initially held prejudices and reservations toward GenAI and lacked experience in using it during the early stages of the art education process. However, as the process progressed, their perspectives shifted. It was concluded that participants transformed their interaction with generative artificial intelligence into a meaningful learning experience.

Participants reported that GenAI not only inspired students throughout the art education process but also supported them in composing artworks, exploring diverse design alternatives, and provided guidance in terms of aesthetics and style. However, the findings also revealed that participants’ experiences were shaped by both practical limitations and pedagogical concerns. The primary practical challenges identified were difficulties in establishing effective interaction with GenAI, participants’ limited technical expertise, access restrictions associated with free versions, and the occasional generation of misleading outputs by the algorithm. From a pedagogical standpoint, concerns were raised regarding issues such as the problem of originality in artistic production, the risk of students becoming passive and disengaged from the creative process, and the tendency to over-attribute capabilities to GenAI. In response, participants developed individual coping strategies informed by their own experiences.

Limitations and Future Directions

This study has certain limitations. The fact that the researcher assumed both the role of facilitator and data collector during the process may be regarded as a limitation that could increase the potential risk of bias. Moreover, the fact that the participant group was limited to only 12 students restricts the generalizability of the findings. The study explores students’ experiences with abstract art projects carried out with the support of GenAI through a qualitative approach, relying on the subjective views of a limited number of participants. For future research, it is recommended that broader participant groups be examined and that studies be conducted using quantitative or mixed-methods designs.

This study addresses students’ perspectives on the reflections of GenAI-supported art education in their abstract art practices. However, the resulting artworks were not systematically evaluated. Moreover, the study was conducted solely within the context of abstract art. Future research is recommended to determine the creativity levels of student products through objective measurement tools and to conduct similar studies across different art forms.

In this study, students used the standard and free versions of ChatGPT and Copilot during text and visual production processes. The limited features of these versions can be considered a limitation. Future research may employ advanced versions as well as different artificial intelligence applications.

The research findings indicate that GenAI tools can be used as an effective medium in abstract art practices within art education. However, in order to fully realize this potential, it is essential to incorporate AI-focused courses into the curriculum and to strengthen the technological infrastructure. In particular, the inclusion of elective courses centered on GenAI within undergraduate art education programs is proposed as a significant step forward. Our research findings show that students face challenges such as lack of technical knowledge, communication difficulties, misleading results, and issues of originality. Therefore, it is important for these courses not to be limited to the teaching of technical skills but also to raise critical awareness regarding concepts such as aesthetics, ethics, and originality (Pavlik & Pavlik, 2024; Su & Mokmin, 2024).

Footnotes

We thank all participants who voluntarily contributed to this study. In addition,the ChatGPT tool developed by OpenAI was used for linguistic editing purposes. This AI-assisted tool was employed solely for language refinement,and the author(s) remain fully responsible for the content of the work.

ORCID iD

Yahya Hiçyilmaz

Ethical Considerations

Participants in this study were included entirely on a voluntary basis. Ethical approval for the conduct of the research was obtained from the Ethics Committee for Publications in Social and Human Sciences at Van Yüzüncü Yıl University,with the decision dated January 7,2025,and numbered 27552. Consent was obtained from all participants included in the study.

Consent to Participate

Consent was obtained from all participants included in the study.

Funding

The author received no financial support for the research,authorship,and/or publication of this article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Data Availability Statement

The qualitative data collected during this study are available from the corresponding author on reasonable request.

References

Ali

Murray

P. A.

Momin

Dwivedi

Y. K.

Malik

(2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199(5), 123076. https://doi.org/10.1016/j.techfore.2023.123076

Al-Tkhayneh

K. M.

Alghazo

E. M.

Tahat

(2023). The advantages and disadvantages of using artificial intelligence in education. Journal of Educational and Social Research, 13(4), 105. https://doi.org/10.36941/jesr-2023-0094

Alwaqdani

(2025). Investigating teachers’ perceptions of artificial intelligence tools in education: Potential and difficulties. Education and Information Technologies, 30(3), 2737–2755. https://doi.org/10.1007/s10639-024-12903-9

Amini

(2025). AI in art education: Innovation, ethics, and the future of creativity. Art and Design Review, 13(2), 115–129. https://doi.org/10.4236/adr.2025.132008

(2024). Implementation of intelligent painting systems in art education as a way of developing student self-efficacy and involvement: Post Lingnan Painting spirit. Education and Information Technologies, 29(12), 15173–15189. https://doi.org/10.1007/s10639-024-12461-0

Barrera Castro

G. P.

Chiappe

Ramírez-Montoya

M. S.

Alcántar Nieblas

. (2025). Key barriers to personalized learning in times of artificial intelligence: A literature review. Applied Sciences, 15(6), 3103. https://doi.org/10.3390/app15063103

Bernacki

M. L.

Greene

M. J.

Lobczowski

N. G.

(2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose(s)? Educational Psychology Review, 33(4), 1675–1715. https://doi.org/10.1007/s10648-021-09615-8

Black

Chaput

(2024). A discussion of artificial intelligence in visual art education. Journal of Computer and Communications, 12(5), 71–85. https://doi.org/10.4236/jcc.2024.125005

Boden

M. A.

(2021). L’intelligence artificielle. Paris: EDP Sciences.

10.

Celik

(2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in human behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468

11.

Celik

Dindar

Muukkonen

Järvelä

(2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y

12.

Chassignol

Khoroshavin

Klimova

Bilyatdinova

(2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233

13.

Chen

Mokmin

N. A. M.

Shen

(2025). Leveraging AI in design education: Exploring virtual instructors and conversational techniques in flipped classroom models. Education and Information Technologies, 30(12), 16441–16461. https://doi.org/10.1007/s10639-025-13458-z

14.

Chen

Niu

Zhao

(2014). A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web, 17, 271–284. https://doi.org/10.1007/s11280-012-0187-z

15.

Chen

Xie

Zou

Hwang

G. J.

(2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

16.

Chen

Jensen

Albert

L. J.

Gupta

Lee

(2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161–182. https://doi.org/10.1007/s10796-022-10291-4

17.

Chiu

M. C.

Hwang

G. J.

Hsia

L. H.

Shyu

F. M.

(2024). Artificial intelligence-supported art education: A deep learning-based system for promoting university students’ artwork appreciation and painting outcomes. Interactive Learning Environments, 32(3), 824–842. https://doi.org/10.1080/10494820.2022.2100426

18.

Creswell

J. W.

(2019). Nitel araştırmacılar için 30 temel beceri ( Özcan

, Çev.). Anı Yayıncılık.

19.

Creswell

J. W.

(2021). Nitel araştırma yöntemleri: Beş yaklaşıma göre nitel araştırma ve araştırma deseni (S. B. Demir ve M. Bütün, Çev.). Siyasal Kitabevi.

20.

Csikszentmihalyi

(1997). Flow and the psychology of discovery and invention. New York, NY: HarperPerennial.

21.

Davenport

T. H.

(2018). The AI advantage: How to put the artificial intelligence revolution to work. MIT Press.

22.

Fang

Jiang

(2024). The analysis of artificial intelligence digital technology in art education under the internet of things. Ieee Access, 12, 22928–22937. https://ieeexplore.ieee.org/abstract/document/10423763/

23.

Fitria

T. N.

(2023). The use of artificial intelligence in education (AIED): Can AI replace the teacher's role?. Epigram, 20(2), 165–187. https://doi.org/10.32722/epi.v20i2.5711

24.

Hall

Schofield

(2025). The value of creativity: Human produced art vs. AI-generated art. Art and Design Review, 13(1), 65–88. https://doi.org/10.4236/adr.2025.131005

25.

Hall

Wegerif

Loper

Ní Chróinín

O’Brien

(2022). Digital education futures: Design for doing education differently. Irish Educational Studies, 41(1), 1–4. https://doi.org/10.1080/03323315.2021.2022072

26.

Heaton

Low

J. H.

Chen

(2024). AI art education: Artificial or intelligent? Transformative pedagogic reflections from three art educators in Singapore. Pedagogies: An International Journal, 19(4), 647–659. https://doi.org/10.1080/1554480X.2024.2395260

27.

Huang

Saleh

Liu

(2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3), 206. https://doi.org/10.36941/ajis-2021-0077

28.

Kong

(2020). Application of artificial intelligence in modern art teaching. International Journal of Emerging Technologies in Learning (İJET), 15(13), 238–251.

29.

Lee

Han

Lee

Kim

Lim

(2024). Prompt aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Education and Information Technologies, 29(8), 9575–9605. https://doi.org/10.1007/s10639-023-12150-4

30.

Liao

Cao

(2025). Digital media entertainment technology based on artificial intelligence robot in art teaching simulation. Entertainment Computing, 52, 100792. https://doi.org/10.1016/j.entcom.2024.100792

31.

Lim

W. M.

Gunasekara

Pallant

J. L.

Pallant

J. I.

Pechenkina

(2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The İnternational Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790

32.

Luan

Geczy

Lai

Gobert

Yang

S. J.

Ogata

Tsai

C. C.

(2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11, 580820. https://doi.org/10.3389/fpsyg.2020.580820

33.

Matos

Santos

Zdravevski

Coelho

P. J.

Pires

I. M.

Madeira

(2025). A systematic review of artificial intelligence applications in education: Emerging trends and challenges. Decision Analytics Journal, 6, 100571. https://doi.org/10.1016/j.dajour.2025.100571

34.

Mazzone

Elgammal

(2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 1–9. https://doi.org/10.3390/arts8010026

35.

McCormack

Gifford

Hutchings

(2019). Autonomy, authenticity, authorship and intention in computer generated art. In Liapis

Ekárt

McCormack

Dahlstedt

Brown

(Eds.), Computational intelligence in music, sound, art and design: EvoMUSART 2019. Lecture Notes in Computer Science (Vol. 11453, pp. 35–50). Cham: Springer. https://doi.org/10.1007/978-3-030-16667-0_3

36.

McCormack

Hutchings

Gifford

Yee-King

Llano

M. T.

D’Inverno

(2020). Design considerations for real-time collaboration with creative artificial intelligence. Organised Sound, 25(1), 41–52. https://doi.org/10.1017/S1355771819000451

37.

Merriam

S. B.

(2009). Qualitative research: A guide to design and implementation. San Francisco, CA: JosseyBass.

38.

Miller

G. J.

(2022). Stakeholder roles in artificial intelligence projects. Project Leadership and Society, 3, 100068. https://doi.org/10.1016/j.plas.2022.100068

39.

Miralay

(2024). Use of artificial intelligence and augmented reality tools in art education course. Pegem Journal of Education and Instruction, 14(3), 44–50. https://doi.org/10.47750/pegegog.14.03.04

40.

Mokmin

N. A. M.

Ridzua

N. N. I. B

. (2022). Immersive technologies in physical education in malaysia for students with learning disabilities. IAFOR Journal of Education, 10(2), 91–110.

41.

Oksanen

Cvetkovic

Akin

Latikka

Bergdahl

Chen

Savela

(2023). Artificial intelligence in fine arts: A systematic review of empirical research. Computers in Human Behavior: Artificial Humans, 1(2), 100004. https://doi.org/10.1016/j.chbah.2023.100004

42.

Park

Y. S.

(2023). Creative and critical entanglements “with” AI in art education. Studies in Art Education, 64(4), 406–425. https://doi.org/10.1080/00393541.2023.2255084

43.

Patton

M. Q.

(2018). Nitel araştırma ve değerlendirme yöntemi (M. Bütün ve S. B. Demir, Ed. ve Çev.). Pegem Akademi Yayınları.

44.

Pavlik

J. V.

Pavlik

O. M.

(2024). Art education and generative AI: An exploratory study in constructivist learning and visualization automation for the classroom. Creative Education, 15(4), 601–616. https://doi.org/10.4236/ce.2024.154037

45.

Sáez-Velasco

Alaguero-Rodríguez

Delgado-Benito

Rodríguez-Cano

(2024). Analysing the impact of generative AI in arts education: A cross-disciplinary perspective of educators and students in higher education. Informatics, 11(2), 11–37. https://doi.org/10.3390/informatics11020037

46.

Samala

A. D.

Rawas

Wang

Reed

J. M.

Kim

Howard

N. J.

Ertz

(2025). Unveiling the landscape of generative artificial intelligence in education: A comprehensive taxonomy of applications, challenges, and future prospects. Education and Information Technologies, 30(3), 3239–3278. https://doi.org/10.1007/s10639-024-12936-0

47.

Song

Mak

Chen

(2025). Teachers’ and learners’ perceptions about implementation of AI tools in elementary mathematics classes. SAGE Open, 15(2), 1–14. https://doi.org/10.1177/21582440251334545

48.

Mokmin

N. A. M.

(2024). Unveiling the canvas: Sustainable integration of AI in visual art education. Sustainability, 16(17), 7849. https://doi.org/10.3390/su16177849

49.

Tao

(2022). A new harmonisation of art and technology: Philosophic interpretations of artificial intelligence art. Critical Arts, 36(1–2), 110–125. https://doi.org/10.1080/02560046.2022.2112725

50.

Tao

Zou

Ren

(2018, October). The art of human intelligence and the technology of artificial intelligence: Artificial intelligence visual art research. In Wang

Hussain

(Eds.), Proceedings of the 2nd International Conference on Intelligence Science (ICIS 2018) (pp. 146–155). Springer International Publishing. https://doi.org/10.1007/978-3-030-01313-4_15

51.

Tiwari

C. K.

Bhat

M. A.

Khan

S. T.

Subramaniam

Khan

M. A. I.

(2024). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education, 21(3), 333–355. https://doi.org/10.1108/ITSE-04-2023-0061

52.

Vartiainen

Tedre

(2023). Using artificial intelligence in craft education: Crafting with text-to-image generative models. Digital Creativity, 34(1), 1–21. https://doi.org/10.1080/14626268.2023.2174557

53.

Vorobyeva

K. I.

Belous

Savchenko

N. V.

Smirnova

L. M.

Nikitina

S. A.

Zhdanov

S. P.

(2025). Personalized learning through AI: Pedagogical approaches and critical insights. Contemporary Educational Technology, 17(2), Article e529. https://doi.org/10.30935/cedtech/16108

54.

Wang

(2024). Art innovation or plagiarism? Chinese students’ attitudes toward AI painting technology and influencing factors. Ieee Access, 12, 85795–85805. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10552736

55.

Wang

F. H.

(2017). An exploration of online behaviour engagement and achievement in flipped classroom supported by learning management system. Computers & Education, 114, 79–91. https://doi.org/10.1016/j.compedu.2017.06.012

56.

Wang

Ramdani

J. M.

Sun

Bose

Gao

(2024). Naming research participants in qualitative language learning research: Numbers, pseudonyms, or real names? Journal of Language, Identity & Education. Advance online publication. https://doi.org/10.1080/15348458.2023.2298737

57.

(2024). Exploration of the applications of image-based AIGC in art education. Advances in Social Development and Education Research, 1(3), 60–65.

58.

Yang

(2020). Artificial intelligence-based strategies for improving the teaching effect of art major courses in colleges. International Journal of Emerging Technologies in Learning, 15(11), 146–158.

59.

Yang

Shin

(2025). The impact of Gen AI on art and design program education. The Design Journal, 28(2), 310–326. https://doi.org/10.1080/14606925.2024.2425084

60.

Yıldırım

ve Şimşek

(2021). Sosyal bilimlerde nitel araştırma yöntemleri. Seçkin Yayıncılık.

61.

Yim

I. H. Y.

(2025). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. Journal of Computers in Education, 12(1), 93–131. https://doi.org/10.1007/s40692-023-00304-9

62.

Zawacki-Richter

Marín

V. I.

Bond

Gouverneur

(2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0

63.

Zhang

Lee

Yang

Pan

(2025). Is the impact of artificial intelligence generation tools on improving art education positive or negative? Perspectives of professors and students. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2025.2490173

64.

Zhao

Yang

Zhang

Siu

K. W. M.

(2021). Creative idea generation method based on deep learning technology. International Journal of Technology & Design Education, 31(2), 421–440. https://doi.org/10.1007/s10798-019-09556-y

65.

Zhou

Lee

(2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(2), pgae052. https://doi.org/10.1093/pnasnexus/pgae052

An Innovative Approach in Arts Education: Student Experiences of Abstract Art Practices Supported by Generative Artificial Intelligence

Abstract

Plain Language Summary

Keywords

Introduction

The Importance and Purpose of the Research

Research Questions

Literature Review

Artificial Intelligence and Personalized Learning in Art Education

The Role of Artificial Intelligence in Creative Processes

Concerns Regarding the Use of Artificial Intelligence in Art Education

Method

Participants

Data Collection Instruments

Implementation

Data Analysis

The Researcher’s Role

Results

Initial Impressions of Generative Artificial Intelligence

Contributions of Generative Artificial Intelligence to the Artistic Process

Challenges in the Use of Generative Artificial Intelligence

Approaches to the Art Education Process Supported by Generative Artificial Intelligence

Discussion

Conclusion

Limitations and Future Directions

Footnotes

ORCID iD

Ethical Considerations

Consent to Participate

Funding

Declaration of Conflicting Interests

Data Availability Statement

References