Navigating Structure and Imagination in the Age of Artificial Intelligence The integration of artificial intelligence (AI) into art education presents both opportunity and complexity. AI tools can generate visual imagery instantly, offering students access to anatomical references, hybrid forms, and imaginative compositions that were previously difficult to obtain. However, this accessibility introduces an important tension: while AI can expand creative possibilities, it may also reduce the productive struggle that is essential to meaningful artistic growth. The study of anatomy has long served as a foundation for understanding the figure. From the observational investigations of Leonardo da Vinci to contemporary studio practices, artists have relied on anatomical knowledge to inform expressive representation. AI extends this tradition by providing not only realistic depictions but also manipulated, hybridized, and surreal reinterpretations of the human form. At the same time, the emergence of AI must be understood within a broader shift in digital visual culture. As Odabaşı et al. (2024) argue, students are increasingly immersed in image-saturated environments shaped by algorithms, filters, and generative systems. In this context, art education must move beyond technical production toward fostering visual literacy, critical thinking, and reflective engagement with images. This article proposes that AI’s greatest educational value lies in its dual role as an anatomical mentor, supporting structural understanding through varied visual references and as a surrealist catalyst, enabling imaginative transformation through distortion and symbolism. When intentionally integrated, these roles create a continuum from observation to invention, aligning with contemporary expectations for art education. AI as Anatomical Mentor: Deepening Structural Understanding AI-generated imagery provides students with access to a wide range of anatomical perspectives, including variations in musculature, posture, and expression. Unlike static textbook images, AI allows for iterative exploration, enabling students to generate multiple versions of a pose, adjust proportions, and analyze movement from different angles. This approach aligns with constructivist theories of learning which emphasize that knowledge is constructed through active engagement rather than passive reception. AI supports this process by allowing students to interact with visual material dynamically, comparing, analyzing, and adapting references. Recent research reinforces this potential, indicating that AI-enhanced tools support individualized learning pathways and increase student engagement and creative confidence (Li et al., 2026; Zhang & Chen, 2025). However, Odabaşı et al. (2024) emphasize that the effectiveness of these tools depends on the educator’s ability to guide students toward inquiry-based use rather than passive consumption. These experiences shift anatomy from a fixed set of rules to a flexible system for exploration and understanding. In practice, AI can support exploration of facial expressions across emotional ranges, analysis of gesture and movement, and comparison of human and animal anatomical structures. From Observation to Interpretation: Developing Critical and Creative Thinking While AI expands access to visual references, its educational value depends on how students engage with those resources. When AI outputs are treated as final products, learning is limited. When they are used as starting points for inquiry, learning deepens. Hetland et al. (2013) identify key Studio Habits of Mind, including observing, envisioning, and stretching and exploring. AI can support these habits when framed as a tool for interpretation rather than 38 TRENDS 2026 PAGE
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