The field of AI-driven educational and clinical research is rapidly evolving, with a focus on developing innovative methods and applications to improve learning outcomes and healthcare services. One of the key directions is the integration of large language models (LLMs) into educational and clinical settings, which has the potential to enhance teaching, learning, and patient care. However, this also raises important questions about the ethical implications, bias, and transparency of these models. Researchers are exploring the use of LLMs to generate adaptive, context-aware survey questions, simulate patient-health educator dialogues, and develop conversational assistants for health applications. Another area of focus is the development of computational models of inclusive pedagogy, which can help to create more effective and adaptive learning environments. Overall, the field is moving towards a more nuanced understanding of the potential benefits and limitations of AI-driven approaches in education and healthcare. Noteworthy papers include: Methodological Foundations for AI-Driven Survey Question Generation, which introduces a methodological framework for using generative AI in educational survey research. A Computational Model of Inclusive Pedagogy, which presents a computational model that integrates contextual insights on human education into a testable framework. Natural Language Generation in Healthcare, which provides a comprehensive review of NLG methods and applications in the medical domain.