Generative artificial intelligence tools for diverse learning styles in design education

Abstract

The integration of Artificial Intelligence (AI) is transforming design education by automating processes and enhancing learning. Early exposure to AI offers students a competitive edge in critical thinking and problem-solving. AI tools can inspire space planning, improve workflow, reduce cognitive load, and foster creativity. This study explores how the VARK model, which categorizes learning styles into Visual, Auditory, Reading/Writing, and Kinesthetic, influences student engagement with AI tools. In an interior design classroom study involving 32 sophomore students, AI tools were used to create floor plans and generate 3D images. The study assessed the correlation between learning styles and technology acceptance. Results revealed no strong correlations between individual learning styles and perceived ease of use, perceived usefulness, or actual use of AI tools. However, visual and kinesthetic learners displayed higher engagement, suggesting that these modalities may benefit more from AI-enhanced design education. The study also confirmed that perceived ease of use and usefulness are critical factors in technology acceptance, aligning with the Technology Acceptance Model (TAM). The findings suggest that AI tools can be broadly effective across diverse learning styles, with a universal perception of their utility in design education. This research highlights the potential of AI to support creative problem-solving and prepare students for future challenges in a digital design landscape. While learning styles influence engagement, AI tools’ usability and educational benefits are consistently recognized, demonstrating their transformative potential in design education.

Keywords

Artificial intelligence design education learning styles visual learner design creativity

Introduction

Artificial Intelligence (AI) significantly enhances human innovation and creativity by broadening the spectrum of possible outcomes. This technology is particularly advantageous in educational settings, where it aids in developing applications, personalizing learning experiences, and automating repetitive tasks. In design education, AI plays a crucial role in skills training, simulation and visualization (including augmented and virtual reality), and generative design. Continuous advancements in AI facilitate the automation of tasks that previously required human intelligence, thereby reducing cognitive load and allowing mental processes to shift from routine tasks to more innovative and creative activities. Despite the increasing exploration of AI’s potential in design education, a substantial knowledge gap remains regarding how students’ preferred learning styles influence their acceptance and effective use of technology. The VARK Learning Styles Inventory is a well-established method for measuring these preferences, as supported by numerous studies.^1–3 The VARK model categorizes learning styles into Visual, Auditory, Reading/Writing, and Kinesthetic, each of which profoundly affects how design students interact with generative AI tools.

Visual learners, who prefer learning through images and spatial understanding, can benefit significantly from generative AI tools that generate visual representations such as 3D models and floor plans. These students thrive on the ability to see and manipulate images, which helps them comprehend complex spatial relationships and design concepts. For instance, AI tools that create detailed 3D models allow visual learners to explore different angles, perspectives, and dimensions of a design, facilitating a deeper understanding of the spatial layout and aesthetic elements. Tutorials featuring visual aids, such as diagrams, infographics, and video demonstrations, provide supplemental support, enabling visual learners to grasp intricate details at their own pace. This visual interaction not only enhances their learning process but also empowers them to experiment with innovative design solutions, fostering creativity and critical thinking.³ Auditory learners, on the other hand, may prefer AI tools that offer verbal explanations, provide auditory feedback, or facilitate interactive discussions on design concepts. These learners benefit from listening to descriptions and engaging in conversations that help them process information and retain knowledge. AI tools equipped with voice-activated commands, spoken tutorials, and interactive dialogue systems can make the learning experience more engaging and accessible for auditory learners. By incorporating auditory elements, these tools help learners understand design principles, receive instant feedback, and participate in collaborative brainstorming sessions, thereby enriching their educational experience and enhancing their comprehension of complex topics.²

Reading/Writing learners benefit from AI tools that provide extensive textual information, including detailed design specifications, theoretical explanations, and historical context. These students excel in environments where they can read and write to absorb information. AI tools that offer comprehensive text-based resources, such as digital textbooks, articles, and written tutorials, cater to these learners by providing in-depth knowledge and contextual understanding. Additionally, features like note-taking, annotation, and textual analysis enable reading/writing learners to engage with the material actively, enhancing their ability to synthesize information and apply it to design projects.¹ For kinesthetic learners, who excel through hands-on experience and physical interaction, AI tools that allow interactive manipulation of design elements in a virtual space are most effective. These learners thrive in environments where they can engage directly with the material, using their sense of touch and movement. AI-powered virtual reality (VR) and augmented reality (AR) applications offer immersive experiences where kinesthetic learners can manipulate design components, explore virtual environments, and simulate real-world scenarios. This hands-on interaction helps them develop practical skills, understand spatial dynamics, and experiment with design variations, making the learning process more dynamic and impactful.⁴

Considering the diverse ways people interact with AI tools, it is likely that certain tools will appeal more to specific types of learners based on their individual learning preferences.

Recognizing these diverse learning preferences is imperative for educators aiming to optimize the educational benefits of AI tools for design students. By aligning AI applications with the VARK learning styles, educators can ensure that each student receives the most effective support tailored to their individual learning preferences. This alignment not only enhances the overall learning experience but also fosters greater innovation and creativity in the field of design education. As AI continues to evolve, understanding and integrating these learning preferences will be key to maximizing its potential and transforming educational practices to better prepare students for the future.

The research questions in this study are as follows

RQ1: How do different learning styles, as defined by the VARK model (Visual, Auditory, Reading/Writing, Kinesthetic), influence students’ engagement with generative AI tools in a design education context?

RQ2: Is there a significant correlation between students’ learning styles and their perceived ease of use, perceived usefulness, and actual use of generative AI tools?

RQ3: How does the Technology Acceptance Model (TAM) apply to the use of generative AI tools in design education, and what factors most significantly influence students’ acceptance of these tools?

Literature review

Generative AI

Alan Turing’s⁵ question, “Can machines think?” sought to redefine the definitions of both the terms “machine” and “think”. He then asked, “What is the answer to this new form of the questions” as well as “Is this new question a worthy one to investigate?”. His suggestion was that if humans could use reason as well as information for problem-solving, then perhaps machines could, as well. First, however, computers of the time would have to be developed beyond their existing capabilities: they could only execute commands, not store them. By 1957, this problem was eliminated, and Artificial Intelligence (AI) became a reality: computers could perform preset tasks using predetermined algorithms. These algorithms were designed to excel at specific tasks, such as voice and speech assistants, search engines, or even playing cognitive games.

Artificial intelligence (AI) is defined variably by different stakeholders, reflecting general, operational, philosophical, and functional perspectives. Broadly, AI can be described as the capacity of machines, particularly computers, to emulate human intelligence.⁶ Additionally, AI encompasses systems or algorithmic functions to perform users’ tasks and enhance their performance based on data collection. It is also characterized as a discipline situated at the intersection of computer science and cognitive science.⁷ Synthesizing these definitions, AI represents the integration of technological advancements, algorithmic science, computing, and communication innovations, enabling computers to perform functions that are nearly or fully equivalent to human capabilities.

AI was trained to do a job well but lacked the ability to create. However, as technology continues to evolve, a new generative form of artificial intelligence has emerged, designed to assist with creative tasks. Generative artificial intelligence is still often misunderstood to be a substitute for human creativity when in fact, it is an aid to creativity. The algorithms within AI generative systems address problems uniquely, managing complexity by breaking it down into simple, repeated tasks, in contrast to designers who approach problems comprehensively. By eliminating the usual constraints of scale and scope found in human-driven design, AI can elevate creativity to new levels.⁸ Empirical and mechanical knowledge in new generative AI technology has simplified the generation of new designs by eliminating redundancy and reducing inefficiency. AI has assisted in “… Consistently discovering, developing, and implementing sophisticated computer-aided design technologies to streamline the process and target efficient and reliable structural designs”.⁹

Upon mentioning the use of AI tools in today’s world, the collaboration between AI and human thought is essential for addressing current challenges and opportunities. Multiple possibilities exist through which such collaboration is possible. The creation of content through AI has made a profound impact on many industries, including education.¹⁰

While AI in education has been extensively studied, research on the interplay between AI and various learning styles remains limited. This study examines the influence of different learning styles, as defined by the VARK model, on students’ perceptions and interactions with AI devices in design education. It investigates how these learning styles shape students’ engagement with AI technology and subsequently impact their intention to utilize this technology in the future.

The VARK model and learning styles

Classroom environments and instructional practices elicit diverse responses from students with varying social and knowledge backgrounds. By exploring how new educational content is approached, a more thorough understanding of learning styles can be achieved.¹¹ First established in 1987, the VARK Learning Styles Inventory has since been updated to include four modalities: visual, aural, reading/writing, and kinesthetic.¹²

Many previous studies have typically focused on identifying these learning styles and examining students’ performance across various contexts across different age ranges. These studies have highlighted the importance of tailoring educational strategies to accommodate the distinct preferences and strengths associated with each learning style.¹³ However, there is a need for further research to explore the dynamic interplay between learning styles and emerging technologies, such as AI, to enhance educational outcomes.

The VARK learning styles and the various media that students prefer to use based on their learning preferences are critical in shaping effective teaching strategies. These preferences help tailor emerging digital tools to accommodate the diverse ways in which students absorb and process information. In this regard, visual learners prefer to take in information in graphic representation, through maps, diagrams, charts, and other devices that create meaningful symbols. Aural (or auditory) learners have a preference for information that they can hear or speak, such as that obtained from lectures, discussions, or email. These learners often talk to themselves as well as out loud. When text-based input is favored, manuals and essays are useful, and the learners enjoy lists, dictionaries, and the Internet. Finally, the kinesthetic modality interaction: touching, feeling, tasting, or doing.¹⁴

Many students do not fall into only one of these categories. The VARK Inventory assigns these to one of two multimodalities. Type One is flexible and context specific, choosing one mode to their situation. Type Two tends to take more time with gathering information, using all of their preferred modes to make decisions. In between Types One and Two are individuals that are called Transitional. All of the learning styles and multimodalities have their strengths; the VARK Inventory has been used by many researchers ^15–17 and is used in this study to determine whether learning styles correlate to technology acceptance.

Technology acceptance (TAM)

To better understand new technological devices, including AI tools, it is essential to evaluate the technology acceptance level by users and observe how they interact with these tools. Technology acceptance refers to the process through which individuals adopt and utilize new technology. This process encompasses understanding how people perceive, accept, and integrate technology into their routines or work practices. Numerous models and theories have been developed to study technology acceptance, with the Technology Acceptance Model (TAM) being one of the most prominent established theoretical frameworks.¹⁸

TAM posits that perceived ease of use and perceived usefulness are determinants of an individual’s intention to use a technology, ultimately influencing their actual use (Figure 2). Perceived ease of use refers to the degree to which a person believes that using a particular system would be free of effort, while perceived usefulness is the degree to which a person believes that using the system would enhance their job performance. These factors shape the user’s attitude towards the technology, which in turn affects their behavioral intention to use it, and ultimately, their actual usage behavior.

This model has been extensively applied across various contexts, including social media to evaluate platform adoption and usage,¹⁹ mobile applications to examine trends in habit formation and usability,²⁰ online meeting platforms,²¹ and virtual and immersive technologies to assess their perceived usefulness.²² Additionally, it has been utilized to evaluate AI technology through the technology acceptance model approach.²³

In this study, we employ the technology acceptance model to investigate the correlation between students’ learning styles and their acceptance levels of generative AI technology.

AI-empowered design education

Several studies have highlighted the advantages of integrating AI technologies in educational settings across various student levels, ranging from middle school ^24,25 to graduate programs.²⁶ Furthermore, Kaplan et al. ²⁷ indicate that educators, irrespective of their teaching methodologies, generally possess favorable views towards AI. The sustained use of AI has amplified this positive outlook, with many educators recognizing its potential to enhance their professional development and serve as a valuable educational tool for students. Design education, in particular, has evolved significantly with the growing interest in generative AI. The potential impact of this technology on design education has been met with both skepticism and curiosity.

Traditionally, design students are taught to solve problems through an iterative process. In this context, generative AI can assist by automating basic processes and offering unique solutions that balance complexity and simplify repeated tasks. However, some researchers have noted that many AI techniques were initially developed for general applications, making them less effective in addressing the specific requirements of particular domains, distinct learning activities, or teaching goals. This limitation could hinder the realization of personalized learning experiences. Nonetheless, as AI resources and databases become increasingly extensive and sophisticated, AI can be employed for more complex tasks. By decomposing complex issues into smaller elements, AI can break down a design problem into design components and then use design principles to reassemble these components into a cohesive whole.

Despite these obstacles, AI’s potential in design education remains substantial. Verganti et al.⁸ emphasized that AI, as a decision-making technology, provides opportunities to automate numerous tasks related to learning and solution development. When applied to innovation, AI can revolutionize decision-making processes and the creation and testing of new solutions. McCardle²⁸ anticipated that AI and smart technologies would offer dynamic opportunities for undergraduate design students to engage in research and leverage cutting-edge technology. Recent studies corroborate this perspective, demonstrating that AI significantly impacts the creative design process, improving the quality of design outcomes and reducing cognitive load.²⁹ The integration of AI in design education paves the way for innovative and exciting prospects in creating smart and interactive products.

Methodology

The study was conducted in two phases. In the first phase, participants were invited to experiment with MaketAI, a generative design tool for residential planning³⁰ for 2 h. By leveraging generative AI, MaketAI automates the creation of residential floorplans and 3D renders, significantly reducing the time required for early-stage planning. Users can input design constraints and instantly generate hundreds of floorplan variations. The platform also offers a virtual assistant for expert guidance on materials, costs, and design possibilities, and simplifies compliance with zoning codes through an integrated regulatory assistant.

The students utilized the tool to facilitate the automated creation of basic residential floor plans, experimenting with iterations featuring various numbers and types of rooms. Participants had the ability to add common rooms such as living and dining rooms, kitchens, and bedrooms, as well as specify the inclusion of closets and entryways. Upon completing the floor plans, participants seamlessly transitioned to the planner’s visualization tool, which allowed them to bring their designs to life by creating vivid 3D images that portrayed the envisioned design style of their dream homes.

In the second phase of the study, participants were surveyed to gather insights into two key aspects: the Technology Acceptance Model (TAM) and the VARK Learning Styles Model. The responses were recorded and analyzed using SPSS. The study employed a quantitative research design.

Instruments: Data collection was conducted through a comprehensive questionnaire incorporating elements from both the Technology Acceptance Model (TAM) and the VARK Learning Styles Model. The TAM, known for its extensive applicability in evaluating user acceptance of technology, formed the basis for several questions.^31,32 These questions targeted perceived ease of use, perceived usefulness, and overall intention to use the technology, utilizing a 7-point Likert scale ranging from 1 (extremely likely) to 7 (extremely unlikely).

In addition, the questionnaire assessed participants’ learning styles based on the VARK model, which categorizes learning preferences into four types: Visual, Auditory, Reading/Writing, and Kinesthetic. Specific questions derived from the VARK Learning Styles profiles were included to identify participants’ learning preferences.

Participants: Participants for this study were recruited through purposive sampling of interior design students at a South Western University in the United States, specifically targeting those in their sophomore year or higher. These students were selected because they have completed more coursework and gained practical experience compared to freshmen, enabling them to provide more informed feedback on the integration of AI tools into their education and its impact on their learning process. A total of 32 participants, aged 19 to 21 years (M = 19.8, SD = 0.86), fully completed the questionnaire. According to the responses, all of the participants were female. Two of the participants were Hispanic, three Native Americans, three African American and the rest were Caucasian.

This methodology is particularly effective because it combines theoretical models (VARK and TAM) with practical application and quantitative analysis, enabling a nuanced understanding of how AI tools can be tailored to diverse learning styles in design education. By using this methodology, the study not only answers the research questions but also provides actionable insights for educators seeking to optimize the use of AI in their classrooms.

Findings

All participants were female, aged between 19 and 21 years. Among them, 85.7% (28 students) were sophomores, while 14.3% (4 students) were junior interior design students. The majority (80.7%) self-reported their computer proficiency as intermediate, with the remainder identifying as beginners (9.7%) and advanced users (9.7%) (Figure 1).

Figure 1.

VARK Learning styles and multimodal learning preferences.

Of the total participants, 17 used PC computers (54.8%), three used Apple computers (9.7%), and nine reported use of both (29%). Participants were also asked about their familiarity with generative AI tools (Figure 2) : ChatGPT, MaketAI, Leonardo AI, and Midjourney. Of the sample, nine people claimed familiarity with four of these (29%), 13 people with three (41.9%), one person with two (3.2%), and one person with one (3.2%) Figures 3 and 4.

Figure 2.

Technology acceptance model.

Figure 3.

Students’ computer Proficiency Level.

Figure 4.

Familiarity with generative AI tools.

A total of 32 participants were included in the analysis. Descriptive statistics were computed for the VARK Total (M = 32.19, SD = 8.514), Perceived Ease of Use (M = 20.31, SD = 5.975), Perceived Usefulness (M = 17.75, SD = 3.827), and Use of Technology (M = 9.13, SD = 3.842) (Table 1).

Table 1.

Descriptive statistic for VARK.

Variable	Mean (M)	Standard deviation (SD)
VARK total	32.19	8.514
Perceived ease of use	20.31	5.975
Perceived usefulness	17.75	3.827
Use of technology	9.13	3.842

Pearson’s correlation coefficient was used to assess the relationship between VARK learning styles (Visual, Auditory, Reading/Writing, Kinesthetic) and variables from the Technology Acceptance Model (Perceived Ease of Use, Perceived Usefulness, Use of Technology). The results indicated a low correlation between the VARK Total and Perceived Ease of Use (r = .018, p = .923), Perceived Usefulness (r = 0.077, p = .676), and Use of Technology (r = 0.006, p = .973). Similarly, low correlations were observed when analyzing the individual components of the VARK model with the TAM variables. For instance, the correlation between the V Score and Perceived Ease of Use was r = 0.133, p = .467, and with Perceived Usefulness, it was r = 0.031, p = .866.

As expected, and shown through many previous studies, there were significant correlations within the TAM variables. There was a strong positive correlation between Perceived Ease of Use and Perceived Usefulness (r = 0.758, p < .001), and a strong negative correlation between Perceived Ease of Use and Use of Technology (r = -.740, p < .001), as well as between Perceived Usefulness and Use of Technology (r = -.614, p < .001). The significant correlations within the TAM variables confirm the foundational principles of the Technology Acceptance Model, where ease of use and perceived usefulness greatly influence the actual use of technology.

Additionally, there were some significant correlations, especially between Visual (V) and Kinesthetic (K), Auditory (A) and Reading/Writing (R), indicating some level of interdependence or co-occurrence between these learning styles. The correlation between Visual (V) and Kinesthetic (K) learning styles, as well as Auditory (A) and Reading/Writing (R), could be due to the complementary nature of these modalities. Visual learning often involves observing and interpreting images, diagrams, and spatial relationships, which can naturally extend to kinesthetic learning that emphasizes physical activity and hands-on experience. Similarly, auditory learning, which focuses on listening and verbal instruction, can align well with reading/writing preferences, as both involve sequential, language-based processing. This overlap suggests that learners might find multiple, interrelated methods effective for understanding and retaining information.

The lack of strong correlations between VARK scores and TAM variables might suggest that individual learning preferences (visual, auditory, reading/writing, kinesthetic) do not significantly influence how people perceive or use technology in a learning context. This could indicate that technology’s usability and utility are more universally perceived, regardless of individual learning preferences. The observation that there are no strong correlations between VARK (Visual, Auditory, Reading/Writing, Kinesthetic) scores and TAM (Technology Acceptance Model) variables could imply that individual learning preferences do not significantly affect how people perceive or use technology in learning environments. This suggests that the usability and utility of technology in educational contexts might be more universally applicable, transcending individual learning styles. Essentially, regardless of a person’s preferred learning method, technology’s effectiveness and ease of use could be perceived similarly by all learners. Furthermore, it’s important to note that the sample size is relatively small (N = 32), which could impact the generalizability of these results.

Conclusion

The integration of Artificial Intelligence (AI) in design education offers significant advantages, enabling the automation of repetitive tasks, enhancing creativity, and fostering innovation. This study explored the relationship between students’ learning styles, as defined by the VARK model, and their acceptance of generative AI tools. The findings reveal that while individual learning styles (Visual, Auditory, Reading/Writing, Kinesthetic) did not strongly correlate with perceived ease of use, perceived usefulness, or actual use of technology, the foundational principles of the Technology Acceptance Model (TAM) were upheld. Strong positive correlations were observed between perceived ease of use and perceived usefulness, indicating that these factors play a crucial role in technology acceptance and utilization. These results suggest that the effectiveness of AI tools in educational contexts might be universally perceived, regardless of individual learning preferences. This universality implies that AI tools designed to enhance design education can be broadly effective across diverse student populations. However, the study also highlighted the complementary nature of certain learning styles, such as Visual and Kinesthetic or Auditory and Reading/Writing, which could inform the development of AI tools tailored to multimodal learning preferences.

Despite the promising insights, the study’s limitations, including a relatively small sample size and the homogeneity of the participant group (all female, aged 19-21), must be considered. Future research should involve larger and more diverse samples to validate these findings and explore the dynamic interplay between learning styles and AI technology further. Additionally, longitudinal studies could provide deeper insights into how students’ interactions with AI tools evolve over time and the long-term impact on their learning outcomes and creative capabilities.

While the results may not be generalizable to all student populations, the study provides valuable insights for design programs looking to progressively integrate generative AI into undergraduate curricula. Firstly, the study suggests that AI tools can be introduced across different learning styles, with a focus on enhancing perceived ease of use and usefulness, which are critical for student acceptance. Secondly, design programs should consider developing AI tools or selecting software that supports multimodal learning, particularly for students who benefit from visual or kinesthetic engagement. Furthermore, to gradually build AI competency, educators could start by incorporating AI in lower-stakes assignments that allow students to explore and experiment with these tools without pressure, progressively increasing complexity as students become more comfortable. Moreover, programs should offer ongoing support and training for both students and faculty to ensure the effective adoption of AI tools, helping to bridge the gap between traditional design practices and emerging digital technologies.

As AI continues to evolve, its thoughtful integration into design education will not only transform the educational landscape but also equip students with the skills and knowledge necessary to thrive in an increasingly digital world. By leveraging these insights, design programs can lead the way in preparing students for the future of design, where AI plays a central role in creativity and innovation.

Limitations

This study, while providing valuable insights into the interaction between VARK learning styles and the use of generative AI tools in design education, has several limitations. Firstly, the sample size was relatively small, with only 32 participants, which may limit the generalizability of the findings. Additionally, the sample lacked diversity, as all participants were female, aged 19-21, and primarily from a single academic program. This homogeneity may influence the applicability of the results to broader populations.

Secondly, the study’s duration was limited to a single session of interaction with the AI tools, which may not fully capture the long-term effects and learning curves associated with the technology. Participants’ initial enthusiasm or novelty effect could have influenced their responses, potentially skewing the data. Moreover, the study relied on self-reported data through questionnaires, which may be subject to biases such as social desirability or inaccurate self-assessment of learning styles and technology acceptance. The VARK model itself, while widely used, may not encompass the full complexity of individual learning preferences and their impact on technology use.

Future research should involve larger, more diverse samples and longer study durations to validate these findings. Additionally, incorporating objective measures of learning outcomes and technology interaction, as well as exploring other models of learning preferences, could provide a more comprehensive understanding of the relationship between learning styles and AI tool utilization in design education.

Footnotes

Declaration of conflicting interests

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

Funding

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

ORCID iD

Tilanka Chandrasekera

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