AFFECTIVE COMPUTING IN MODERN ART EDUCATION - Jagarajan, Manikandan; Narayana, Ananta; Sood, Saksham; Upadhyay, Abhishek; Datar, Dinesh Shravan; Munjal, Kalpana (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Affective computing Affective computing, or more strictly speaking, its recognition and interpretation of human emotions, has emerged as an influential model in the redesigning of art education. This paper will examine how emotion-sensitive technologies can be applied to contemporary art schools to improve creativity, participation, and self-directed learning. Conventional pedagogy of art tend to focus on technical achievement and abstract exploration but does not acknowledge the emotional relations that is the foundation of art. With the introduction of affective computing (recognition of emotions based on facial expressions, tonal, and physiological elements), the educator will be able to more effectively understand the emotional state of the learners and incorporate this knowledge in the instructional approach. The study applies multimodal data gathering in academic arts, including emotion, performance and interaction records to come up with affect-adaptive systems. Emotion classification methods based on machine learning, including CNNs, LSTMs, and multimodal fusion networks, are applied, whereas the performance of the systems is measured by such metrics as accuracy, F1-score, and emotional congruence index. The suggested affect-aware art education platform gives real time emotional feedback, adaptive learning and the use of digital art tools in order to facilitate expressive development. The experimental applications show that emotion-augmented assignments and feedback loops can enhance motivation and quality of the work done by learners.