Advancing Multimodal Vision-Language Models in Assessment

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This presentation explores Multimodal Vision-Language Models, focusing on contrastive pretraining mechanisms, modular adapters, and advancements in instruction-tuning. It evaluates risks, offers critical analysis, and outlines recommendations for modular designs. Additionally, it addresses governance and regulatory approaches to guide future developments in AI assessment within education.