DESIGNING INTELLIGENT MENTORING SYSTEMS FOR ART LEARNERS

Authors

  • Suma N G Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
  • Dr. Deepti Professor, Department of Computer Science & Engineering(CSBS), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Sahil Khurana Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Ashu Katyal Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India
  • Dr. Tanmoy Parida Associate Professor, Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar, Odisha, India
  • Dr. Prashant Lahane Assistant Professor , School of Computer Engineering & Technology, MIT World Peace University, Pune, India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6748

Keywords:

Artificial Intelligence, Intelligent Mentoring System, Art Education, Personalized Learning, Visual Analysis, Natural Language Processing, Reinforcement Learning, Explainable AI, Creative Pedagogy, Affective Computing

Abstract [English]

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.

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Algorithmic Aesthetics and Artificial Intelligence in Visual Arts, Cultural Education, and Creative Practice

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Published

2025-12-16

How to Cite

Suma N G, Deepti, Khurana, S., Katyal, A., Parida, T., & Lahane, P. (2025). DESIGNING INTELLIGENT MENTORING SYSTEMS FOR ART LEARNERS. ShodhKosh: Journal of Visual and Performing Arts, 6(2s), 262–271. https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6748

Issue

Vol. 6 No. 2s (2025): SPECIAL ISSUE ON ALGORITHMIC AESTHETICS AND ARTIFICIAL INTELLIGENCE IN VISUAL ARTS, CULTURAL EDUCATION, AND CREATIVE PRACTICE

Section

Articles

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Copyright (c) 2025 Suma N G, Dr. Deepti, Sahil Khurana, Ashu Katyal, Dr. Tanmoy Parida, Dr. Prashant Lahane

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