ADAPTIVE LEARNING MODELS FOR ART CURATION EDUCATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6776Keywords:
Adaptive Learning, Art Curation Education, Multimodal Analytics, Reinforcement Learning, Artificial Intelligence, Creative Pedagogy, Affective Computing, IEEE Learning TechnologyAbstract [English]
The paper introduces an intelligent learning model of Adaptive Curation Learning Model (A-CLM), an educational architecture that combines artificial intelligence and multimodal analytics and deep reinforcement learning to customize the art curation pedagogy. The model is dynamic and changes the content of instructions, depending on the behavioral, cognitive, and affective profiles of the learners, which enhances more profound and reflective learning. Based on the 120 postgraduate student data in 12 weeks, A-CLM showed significant differences in learning gain (27.3%), cognitive engagement (22.4%) and depth of reflection (18.5%) relative to a stagnant control group. T-tests and ANOVA statistically verified high significance (p < 0.001), and large effect sizes (Cohens d 0.63 and above). The findings prove that adaptive AI can be successfully used to combine computational accuracy with human creativity to facilitate culturally inclusive, information-driven and emotionally responsive art education. The study makes A-CLM a scalable and morally grounded model that complies with the IEEE guidelines of learning technology and opens the door to the integration of explainable and immersive adaptive learning facilities in creative field work soon.
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Copyright (c) 2025 Fehmina Khalique, Josephine, Kumari Shipra, Ayaan Faiz, Pooja Sharma, Ashish Verma, Pooja Ashok Shelar
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