DESIGNING INTELLIGENT MENTORING SYSTEMS FOR ART LEARNERS - Suma N G; Deepti; Khurana, Sahil; Katyal, Ashu; Parida, Tanmoy; Lahane, Prashant (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The use of Artificial Intelligence (AI) in art education has spawned Intelligent Mentoring Systems (IMS) that enable art learners to have personalized learning experiences. These systems are a combination of adaptive learning algorithms, visual analysis and affective computing to offer custom guidance, feedback and skill development paths. The proposed research paper discusses the development of an Intelligent Mentoring System based on AI and multimodal data (sketches, digital paintings, and written reflections) to evaluate artistic development and prescribe specific learning intervention in art learners. The system uses a hybrid approach that involves the use of Convolutional Neural Networks (CNNs) to analyze visual artwork and Natural Language Processing (NLP) in analyzing learner feedback and descriptions. Reinforcement learning is a dynamically adaptive framework that uses mentoring policies according to individual learning paths and maximizes engagement and creative development. Moreover, the explainable AI (XAI) components provide the evaluation transparency so that learners can get the feedback reasons and art improvement indicators. The architecture upholds a human-in-the-loop paradigm, in which skilled artists work alongside AI advisors to improve criteria of evaluation, to be both aesthetic and delicate, as well as technical. The study focuses on the pedagogical and psychological dimensions of mentorship instead of considering the affective state recognition, which helps to modify emotional support in the creative process. This smart mentoring system seeks to balance the traditional forms of art mentorship and AI-customization with the view of promoting independent creativity, self-reflection, and long-term artistic development among learners within the formal and informal learning settings.