AI FOR MANAGING DIGITAL LEARNING PORTFOLIOS IN ART
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6906Keywords:
AI-Enabled Assessment, Digital Art Portfolios, Creativity Analytics, Computer Vision in Art Education, NLP-Based Reflection AnalysisAbstract [English]
The growing pace of digital art activity and education of creativity requires strong systems that can store, measure, and analyze different multimodal artefacts of learning. The pedagogy of traditional portfolio based learning is constrained by manual tracking, subjectivity, and the amount of student-generated material in sketches, digital art, design version, studio comments, and multimedia stories. The paper describes an AI-based system of managing digital learning portfolios in art that provides a scalable and data-driven system that proposes an alternative to traditional assessment processes which is pedagogically aligned. The suggested system combines machine learning models of tracking developmental trends, natural language processing (NLP) modules of analysing reflective statements and computer vision methods of interpreting visual artworks. These elements combined allow automated tagging, mapping of creativity progression, analytics of skill-growth, and semantic processing of the inputs of visual and textual data. There are three layers in the architecture: multimodal data ingestion layer which gathers heterogeneous artefacts, a feature extraction module which generates semantic, stylistic, and behavioural indicators and an intelligence layer which applies classification, clustering, scoring, and recommendation algorithms to assist educators and learners. Digital art academies and undergraduate creative programs exemplified in case studies depict the benefits of AI-induced portfolio intelligence on improving assessment accuracy, decreasing the workload of educators, and offering actionable learners to use as personalised learning patterns. Findings indicate that there are dramatic gains in the quality analysis of the reflective facet, consistency in artistic analysis, and tracking of creative development over time.
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Copyright (c) 2025 Nipun Setia, Shantha Shalini K, Dr. Shailesh Shivaji Deore, Sushmitha S. S, Ms. Palak Patel, Ganesh Korwar
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