INTELLIGENT ASSESSMENT SYSTEMS IN DIGITAL ART EDUCATION - Rashmi, Prachi; Sargam, Sadhana; BS, Babitha; Kandhari, Harsimrat; Agrawal, Sarita; Nanda, Simranjeet; Raikwar, Rajesh (ShodhKosh: Journal of Visual and Performing Arts, 2025)
During educational assessment, the Artificial Intelligence (AI) has become the new way of assessing creativity and skill, specifically in the teaching of digital art. This article discusses the development and application of Intelligent Assessment Systems (IAS) that have the capability of assessing artistic outputs objectively and holistically. Conventional approaches to art assessment are more subjective and thus hard to scale and be consistent. IAS offers a machine learning-based but customizable method of evaluating digital artworks by integrating both machine learning models and creative evaluation systems. The research question is based on how AI algorithms could explore the artistic parameters, including composition, color harmony, creativity and technical skill without contradicting the purposes of educators. Data were collected in digital art classrooms to train and validate the system through the use of a mixed-methods approach, that is, surveys, case studies, and experimental testing. The research design is based on the already developed educational assessment theories, artificial intelligence, and innovative cognition models in order to provide balanced measures of evaluation. Convolutional neural networks and feature extraction were created into a prototype system to evaluate visual artworks. Findings of pilot applications prove that intelligent assessment improves feedback quality and minimizes evaluation bias and provides personalized learning. The results point at the possibility of AI to enhance, as opposed to substitute, human teachers by providing objective information that will supplement expert feedback.