AI-Based Digital Pedagogical Design for Primary School Visual Art Education: Review

AI-Based Digital Pedagogical Design for Primary School Visual Art Education: Review

Roslita Ramli., Norzuraina Mohd Nor., Azlin Iryani Mohd Noor

Faculty of Art, Sustainability and Creative Industry, Sultan Idris Education University, 35900 Tanjong Malim, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000338

Received: 08 September 2025; Accepted: 16 September 2025; Published: 10 October 2025

ABSTRACT

This review aims to explore and critically examine the role and potential of Artificial Intelligence (AI)-based digital pedagogical design in primary school visual arts education. While the integration of AI in STEM subjects has received extensive scholarly attention, its application within creative fields such as visual arts remains significantly underexplored. This gap raises important questions about the pedagogical value, effectiveness, and ethical implications of using AI to support creativity and aesthetic expression among young learners. Accordingly, this review synthesizes existing literature to provide a foundational understanding for innovation in digital visual arts education. A systematic search was conducted across major academic databases including Scopus, Web of Science, Google Scholar, ERIC, and ScienceDirect, focusing on peer-reviewed sources published between 2020 and 2025. The selected articles specifically addressed AI, digital pedagogical design, visual arts education, and primary-level teaching. Key findings suggest that AI holds the potential to personalize instruction, deliver automated and formative feedback, enhance student engagement, and increase teacher efficiency through adaptive learning systems, algorithm-driven assessment, and immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR). However, the review also identifies several critical limitations, including insufficient digital infrastructure, inadequate teacher training, ethical concerns regarding data privacy, and the risk of eroding the humanistic and emotional dimensions that are central to arts education. Furthermore, the limited availability of robust empirical studies and inconsistencies in research methodologies pose challenges for comprehensive synthesis. This review concludes by emphasizing the urgent need for future approaches that are context-sensitive, ethically responsible, and empirically grounded, to integrate AI into art pedagogy in a way that supports inclusive, aesthetic, and holistic student development.

Keywords: Artificial Intelligence, Visual Art Education, Primary School, Digital Pedagogical Design, Educational Technology, Creative Teaching

INTRODUCTION

Artificial Intelligence (AI) has emerged as a key catalyst in transforming the field of education, particularly in the design of digital pedagogy that significantly influences the teaching and learning of Visual Arts in primary schools (J. Chen et al., 2025; Fang & Jiang, 2024). In a technology-driven educational landscape, AI provides opportunities for more interactive, personalized, and data-driven learning experiences (Merino-Campos, 2025; Radif, 2024; Singh, 2024). However, much of the current research remains focused on STEM subjects (Science, Technology, Engineering, and Mathematics), while its integration into visual arts education remains largely underexplored. This has created a significant knowledge gap, alongside ongoing debates regarding the effectiveness and ethical challenges of using AI to support students’ creativity and aesthetic expression. This narrative review aims to explore AI-based digital pedagogical design within the context of primary school visual arts education. The term “digital pedagogical design” refers to a systematic approach to planning learning experiences using digital technology to achieve optimal learning outcomes. This review will examine the application of AI in personalized learning, intelligent visual aids, as well as related pedagogical and ethical issues. This study is both timely and significant, as it provides a strong foundational understanding for shaping current and future practices in visual arts education.

METHODS

This study employed a narrative review approach conducted through a comprehensive analysis of literature related to AI-based digital pedagogical design in visual art education at the primary school level. The literature search was carried out systematically by referring to several major electronic databases, including Scopus, Web of Science, Google Scholar, ERIC, and ScienceDirect. In addition, supplementary manual searches were performed using the snowballing technique, which involved reviewing the reference lists of key articles already identified to capture additional relevant studies.

To ensure a focused and comprehensive search, a variety of keywords were used in different combinations, including (“artificial intelligence” OR “ai” OR “machine learning” OR “deep learning”) AND (“digital pedagogy” OR “pedagogical design” OR “instructional design” OR “teaching methods”) AND (“primary school” OR “elementary education” OR “early education” OR “K-6”) AND (“visual art” OR “art education” OR “art instruction” OR “creative arts”) AND (“education technology” OR “edtech” OR “learning tools” OR “digital tools”)

Filters were applied to ensure relevance, including restricting the review to studies published in English or Malay between 2020 and 2025. Accepted study types included review articles, empirical research, and conceptual papers discussing AI applications in education and visual arts. The population was limited to primary school students, art educators, and classroom environments within basic education.

Inclusion criteria required that: (1) studies directly discussed the use of AI in the context of art education or digital pedagogical design; (2) studies focused on primary education; and (3) articles were published in recognized academic journals or conference proceedings. Meanwhile, exclusion criteria included: (1) studies focused solely on STEM subjects without addressing visual arts; (2) articles from non-academic sources such as blogs, media reports, or commercial content; and (3) studies lacking clear methodology or outcomes related to AI in art education.

All retrieved articles were manually screened and analyzed based on their relevance to the research objectives, primary education context, and research quality. This entire process was conducted with transparency and rigor to ensure that the resulting narrative is based on credible and relevant evidence, and contributes meaningfully to the advancement of knowledge in AI-based visual arts education.

Fig.1. PRISMA-based Flow Diagram of Document Selection Process for Narrative Review

This review synthesized findings from 19 unique searches, focusing on AI-based digital pedagogy, foundational theories, practical applications, and critiques in primary school visual art education.

DISCUSSION

Synthesis Analysis

This section requires the greatest effort and mental energy from authors, as it involves synthesizing the information gathered into well-written, structured paragraphs with a smooth and coherent flow of ideas. Depending on the topic, this section will be divided into subheadings. It is strongly recommended that these subheadings be identified and distributed among contributors before starting the work. This facilitates categorization of information by topic and helps divide responsibilities. Clinical and medical topics typically include subheadings such as definition, epidemiology, etiology, pathophysiology, clinical presentation, diagnosis, differential diagnosis, management, prognosis, and so forth. Depending on the objective and scope of the review, these categories can be adapted to best suit the needs of the reader. Authors should think carefully about this section, as there is no single, fixed format for writing it (Garrard, 2020).

Definition: AI-Based Digital Pedagogical Design in Visual Arts Education

AI-based digital pedagogical design refers to the systematic process of planning and implementing teaching and learning strategies that employ artificial intelligence (AI) technologies as core tools. In the context of primary school visual arts education, this approach involves the use of AI systems such as machine learning, image recognition, and augmented reality (AR) to enrich students’ learning experiences(S.-Y. Chen et al., 2022; Merino-Campos, 2025; Radif, 2024). AI can support teachers in creating interactive AI can assist teachers in creating interactive instructional materials, automatically assessing students’ artwork, and providing personalized feedback tailored to individual learning styles and developmental stages(S. Chen et al., 2023; Chiu et al., 2024). In this way, AI functions not only as a technological aid that enhances instructional effectiveness but also as a catalyst that fosters creativity and artistic expression among young learners.

Advantages of AI-Based Digital Pedagogical Design in Primary Visual Arts Education

Integrating AI into visual arts education provides several notable benefits for both teachers and students. A primary advantage is instructional personalization. AI systems enable learning experiences to be tailored to students’ cognitive abilities, learning styles, and artistic interests, thereby supporting inclusivity and creativity in diverse classrooms (Kaswan et al., 2024; Katonane Gyonyoru & Katona, 2024; Yanjin et al., 2023).

Another significant benefit is the provision of automated and timely feedback. AI-powered tools can evaluate students’ artwork against predefined criteria, offering consistent, objective, and prompt feedback that helps learners reflect on their artistic progress and refine their skills (Hammad et al., 2024; Owan et al., 2023; Sajida Sultana et al., 2025). This reduces teachers’ workloads while ensuring that students receive constructive guidance.

In addition, AI enhances student engagement through interactive and immersive technologies such as virtual reality (VR), augmented reality (AR), and intelligent design applications. These tools transform traditional art lessons into dynamic, participatory experiences that spark creativity and sustain students’ motivation (Aeni et al., 2025; Naseer & Khawaja, 2025).

Finally, AI contributes to greater teacher efficiency by automating routine administrative tasks such as assessment, progress tracking, and classroom management. This allows teachers to focus on higher-value activities, including individual mentoring, fostering creativity, and cultivating a supportive classroom environment(Mon et al., 2023; Yambal & Waykar, 2025). Teachers can then focus on facilitating learning, offering individual guidance, and cultivating a creative classroom culture. This shift supports a more holistic approach to teaching that emphasizes student-centered learning and developmental support (Arif, 2021)

Overall, AI serves as a powerful pedagogical tool by enabling personalized learning, facilitating real-time feedback, increasing student engagement, and enhancing instructional efficiency. When applied responsibly and in alignment with sound pedagogical principles, AI has the potential to significantly enrich primary visual arts education (Holmes et al., 2022; UNESCO, 2021).

Challenges and Constraints in Implementing AI-Based Digital Pedagogy in Primary Visual Arts Education

While AI integration in visual arts education offers considerable promise, it also presents significant challenges that must be addressed to ensure effective and equitable implementation.

One major concern is the potential loss of the humanistic dimension in teaching and learning. Visual arts education is inherently relational and emotional, emphasizing creativity, empathy, and personal expression. Overreliance on AI systems may diminish these aspects, as algorithms cannot replicate the nuanced sensitivity and emotional guidance provided by teachers (Holmes et al., 2022).

A second challenge is unequal access to digital infrastructure. Implementing AI effectively requires reliable internet connectivity, adequate hardware, and technical support—resources that are often limited in rural or underfunded schools. This digital divide risks exacerbating existing educational inequalities by privileging students in well-equipped schools while leaving others behind (UNESCO, 2021;Maja, 2023).

Teacher readiness also remains a critical barrier. Successful AI integration depends not only on technology but also on educators’ confidence, competence, and willingness to adopt new tools. Many art teachers lack formal training in AI applications and may feel resistant or anxious toward digital innovations. Continuous professional development programs are therefore essential to build teachers’ pedagogical and technical capacity (Ding et al., 2024; Dogan et al., 2025)

Finally, AI adoption in education raises pressing ethical and privacy concerns. Many AI systems collect and process student data to enable personalized learning, creating risks related to data protection, informed consent, and algorithmic bias. Without strong ethical guidelines and regulatory frameworks, such risks may undermine student trust and compromise educational equity (Williamson & Eynon, 2020).

In sum, the integration of AI into primary visual arts education must be approached cautiously, with careful attention to preserving humanistic teaching values, bridging infrastructural gaps, preparing teachers, and upholding ethical standards. Only through a balanced and inclusive approach can AI effectively enhance, rather than compromise, creative learning

Future Potential and Educational Implications

The integration of AI into visual arts education opens the door to continuous innovation. With appropriate teacher training, investment in digital infrastructure, and the establishment of strong ethical guidelines, AI-based pedagogical design has the potential to improve learning outcomes and student engagement in the arts. Furthermore, it can enhance the learning of visual arts in the digital age without neglecting its foundational aesthetic and humanistic values (UNESCO, 2021;Paschal & Melly, 2023)

From a pedagogical standpoint, AI-based tools can support differentiated instruction, helping teachers adapt lessons to varied skill levels and learning needs. This promotes inclusivity by ensuring that all students regardless of background or ability can engage meaningfully with creative tasks. Furthermore, immersive AI-driven technologies, such as virtual and augmented reality, can expand the boundaries of traditional classrooms, offering students opportunities to explore, experiment, and express themselves in dynamic digital environments (Shukurova & Ma’murov, 2024)

At the institutional level, schools adopting AI must prioritize professional development to ensure that art teachers are both technically competent and pedagogically confident. This requires not only technical training but also critical reflection on the role of AI in shaping creativity, identity, and cultural expression. In this way, AI integration becomes a collaborative effort that aligns technological advancement with the holistic development of learners (Ding et al., 2024; Dogan et al., 2025).

Looking ahead, AI presents opportunities for interdisciplinary collaboration among educators, policymakers, and technology developers. Such collaboration is essential to design context-sensitive and ethically responsible AI systems that complement, rather than replace, the teacher’s role. If implemented thoughtfully, AI can serve as a catalyst for fostering creativity, innovation, and lifelong learning among young learners, positioning visual arts education at the forefront of digital transformation (UNESCO, 2021).

To provide a clearer synthesis of the literature, the key applications of AI in primary visual arts education are summarized in Table 1. The table highlights not only the pedagogical benefits of AI but also the practical challenges associated with its implementation. Presenting these elements side by side offers a concise overview for readers, allowing them to quickly identify how AI tools contribute to teaching and learning, while also recognizing the infrastructural, pedagogical, and ethical issues that must be addressed. This structured summary complements the detailed discussion provided in the preceding sections.

Tab.1. AI Applications, Benefits, and Challenges in Primary Visual Arts Education

Practical Recommendations for Teachers and Schools

To ensure that the integration of AI into primary visual arts education is both effective and sustainable, several practical recommendations can be considered.

1. Teacher Professional Development

Continuous training programs should be provided to help teachers build both technical and pedagogical confidence in using AI. These programs should focus not only on tool functionality but also on strategies for fostering creativity and emotional expression alongside digital innovation (Ding et al., 2024; Dogan et al., 2025).

2. Infrastructure and Resource Support

Schools need adequate infrastructure—including reliable internet access, appropriate devices, and technical support—to ensure that AI applications can be implemented effectively. Policymakers should prioritize bridging the digital divide, particularly in rural or underfunded contexts (UNESCO, 2021; Maja, 2023).

3. Ethical and Responsible Use

Clear guidelines should be established to protect students’ privacy and ensure equitable use of AI tools. This includes safeguarding data security, ensuring informed consent, and monitoring algorithms to prevent bias (Williamson & Eynon, 2020; Paschal & Melly, 2023).

4. Balanced Pedagogical Approach

Teachers should integrate AI tools as a complement to, rather than a replacement for, human guidance. Emphasizing collaboration, creativity, and critical reflection ensures that the humanistic values of art education remain central while leveraging the affordances of AI (Holmes et al., 2022).

By adopting these recommendations, schools and educators can maximize the potential of AI to enrich creative learning while mitigating risks and challenges

Limitations

Despite offering valuable insights into the integration of artificial intelligence (AI) in digital pedagogical design for primary school visual arts education, this narrative review is not without its limitations, which warrant critical consideration. Firstly, the review is constrained by methodological weaknesses inherent in narrative reviews. Unlike systematic reviews, they do not consistently apply standardized inclusion and exclusion criteria or employ formal quality appraisal tools. This flexibility, while allowing broader thematic exploration, compromises the transparency and reproducibility of the review process. The absence of a reproducible search strategy and the reliance on the authors’ subjective judgment may introduce selection bias.

Moreover, although multiple reputable databases such as Scopus, Web of Science, ERIC, Google Scholar, and ScienceDirect were consulted, the selection criteria were inherently interpretive and potentially guided by the reviewers’ own expertise and perspectives, increasing the risk of publication and disciplinary bias. Given the interdisciplinary nature of the topic, relevant studies may be scattered across educational technology, computer science, and art education fields, which may not be comprehensively indexed in traditional education-focused databases.

A further limitation lies in the limited and fragmented evidence base. Much of the literature on AI integration in primary education—particularly within the arts is still nascent, conceptual, and exploratory in nature. Empirical studies specifically focusing on AI-driven pedagogical strategies in primary visual arts education remain sparse and often lack theoretical depth or context-specific insights. As a result, the synthesis may rely heavily on interpretive generalizations rather than robust evidence-based conclusions.

The heterogeneity of the included studies further complicates synthesis efforts. Variations in research design, educational contexts, AI tools deployed, and outcome measures limit the comparability of findings and hinder the development of coherent themes. The lack of shared theoretical frameworks and consistent terminology across studies poses additional challenges for thematic analysis and interpretation, increasing the risk of narrative and confirmation bias.

Lastly, the potential for publication bias must be acknowledged. Studies reporting positive or significant outcomes of AI in education are more likely to be published and cited, potentially skewing the perceived effectiveness of such technologies. To mitigate these limitations, future research should adopt systematic or scoping review methodologies using rigorous quality appraisal tools to enhance transparency, validity, and reproducibility. Furthermore, there is a pressing need for more context-specific, longitudinal, and theory-driven empirical studies that critically examine the pedagogical applications of AI in visual arts education, including ethical considerations, teacher readiness, and learner engagement

CONCLUSION

This narrative review has synthesized current literature on AI-based digital pedagogical design in primary visual arts education, highlighting its potential to personalize instruction, automate feedback, enhance student engagement, and improve teacher efficiency. These findings directly align with the primary objective of the study: to explore how AI technologies can support and transform visual arts pedagogy at the foundational level of education a gap that has previously received limited attention in educational research. The review also revealed several critical challenges, including the potential loss of humanistic dimensions in learning, disparities in digital infrastructure, limited teacher readiness, and ethical issues concerning data privacy and algorithmic bias. These challenges underscore the need for thoughtful, equitable, and pedagogically grounded integration of AI into creative education. Despite methodological limitations commonly associated with narrative reviews such as selective synthesis of evidence and the heterogeneity of source materials. This study contributes a valuable conceptual foundation for future research. To advance the field, scholars are encouraged to adopt systematic or scoping review methodologies. These should include long-term impact assessments, teacher professional development, and inclusive access to AI tools. Furthermore, interdisciplinary collaboration among stakeholders in education, technology, and public policy is crucial to ensure that AI enriches the visual arts learning experience without compromising the humanistic and aesthetic values at its core. Through such approaches, the role of AI in fostering creativity and innovation among young learners can be more responsibly and effectively realized.

ACKNOWLEDGMENT

The research was funded by scholarships from the Ministry of Education Malaysia, Hadiah Latihan Persekutuan, (HLP) and Sultan Idris Education Univrsity.

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