Automated Graphic Divergent Thinking Assessment: A Multimodal Machine Learning Approach.

This study proposes a multimodal deep learning model for automated scoring of image-based divergent thinking tests, integrating visual and semantic features to improve assessment objectivity and efficiency. Utilizing 708 Chinese high school students' responses from validated tests, we developed a system combining pretrained ResNet50 (image features) and GloVe (text embeddings), fused through a fully connected neural network with MSE loss and Adam optimization. The training set (603 images, triple-rated consensus scores) showed strong alignment with human scores (Pearson r = 0.810). Validation on 100 images demonstrated generalization capacity (r = 0.561), while participant-level analysis achieved 0.602 correlation with total human scores. Results indicate multimodal integration effectively captures divergent thinking dimensions, enabling simultaneous evaluation of novelty, fluency, and flexibility. This approach reduces manual scoring subjectivity, streamlines assessment processes, and maintains cost-effectiveness while preserving psychometric rigor. The findings advance automated cognitive evaluation methodologies by demonstrating the complementary value of visual-textual feature fusion in creativity assessment.