The advanced generative artificial intelligence (AI) techniques, such as large language models and large multimodal models, are transforming many aspects of educational assessment. The integration of AI into education has the potential to revolutionize not only test development and evaluation but also the way students can learn. Over the past years, some successful adoptions of machine learning in this area are using natural language processing for automated scoring, or applying collaborative filtering to predict student responses. The rapid advances of large foundation models (e.g., ChatGPT, GPT-4, Llama, Gemini) demonstrate the potential of intelligent assessment with data-driven AI systems. These models could potentially benefit test construct identification, automatic item generation, multimodal item design, automated scoring, and assessment administration. Meanwhile, new research challenges arise in the intersection of AI and educational assessments. For instance, the explainability and accountability of current large foundations models are still inadequate to convince the stakeholders in the educational ecosystem, which limits the adoption of AI techniques in large-scale assessments. Also, it is still unclear whether the large foundation models are capable of assisting complex assessment tasks that involve creative thinking or high-order reasoning. Tackling these research challenges would require collaborative efforts from researchers and practitioners in both AI and educational assessment. This one-day workshop provides a forum for researchers from AI and educational assessment to review and discuss the recent advances of applying large foundation models for educational assessment.