PREDICTIVE MODELS FOR CREATIVE TALENT IDENTIFICATION - Behera, Dr. Bhagyalaxmi; Singh, Priyadarshani; Patil, Shashikant; Mahajan, Sulabh; Jain, Romil; Khurana, Divya S (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Creative talent is a multidimensional human ability that cannot be evaluated fully by traditional assessments based solely on portfolios, standardised tasks or subjective evaluation. In this paper, a multimodal predictive architecture is proposed that fuses textual, visual and behavioural data to predict creative potential more accurately, at a scale and with fairness that is achievable to human standards. In particular, it uses language models based on transformers, vision transformers, and behavioral encoders at the sequence level to learn latent representations of creativity (e.g., in the form of story divergence, in visual novelty, and in terms of exploratory interaction patterns). An attention-based feature integration factorizes modality-specific representations into a single representation, but the attention structure makes it possible to adaptively weight modalities to individual creative styles, instead of forcing creators to express themselves through a single process. Experimental results show that the multimodal model is able to perform much better than unimodal baselines, with both high accuracy, high correlation with expert scores, and robust cross-trials. Furthermore, the analysis of its interpretability suggests that the decisions made by the model align well with the human-recognized creative features, verifying its interpretability and its ethical suitability. This research draws attention to the possibility of AI-driven multimodal systems to improve talent identification of creative people, to provide a more inclusive and data-informed approach for applications in education, recruitment, and the creative industries.