Short Course on Machine Learning and Emerging AI Technologies

About This Grant

Project Summary/Abstract The purpose of this project is to provide a train-the-trainer program in machine learning and emerging artificial intelligence (AI) technologies – such as generative AI and large language models – for early, mid-career, and senior scholars in the social and behavioral sciences. The rapid growth of available data, computing capacity, and powerful AI models has the potential to reshape how research is conducted in social science domains relevant to health, including population health, population dynamics, child and adolescent development, and health economics. Although machine learning and related methods have been used increasingly, their adoption in the social sciences is being outpaced by the development of emerging AI technologies, such as generative AI. Within the social sciences, pedagogy for emerging AI methods remains largely undeveloped, leaving a gap in formal training infrastructure. There is a pressing need for cutting-edge, scalable training programs that not only equip researchers to use these technologies but also establish replicable models for instruction across institutions grounded in novel pedagogical strategies. The proposed program consists of a series of 8-week, remote summer workshops designed to equip trainees to use machine learning and emerging AI technologies in their own research. The curriculum will be structured to prepare trainees to offer their own versions of the course, magnifying the program’s impact beyond its immediate participants. Trainees will not be expected to have prior programming experience. The course will build on our wealth of experience in developing remote data science training programs across the social and behavioral sciences. The course will begin with an introduction to the Python programming language, emphasizing a data science perspective. Participants will then explore advanced topics such as machine learning, large language models, and generative AI, with an emphasis on integrating these technologies into research workflows and building practical fluency in computational tools. Practical skills, such as use of the command line, version control, and cloud computing will also be covered. The workshops will take an active learning pedagogical approach, utilizing a flipped classroom that combines pre-recorded content with live sessions focused on coding exercises, applied examples, discussion, feedback, and formative assessment. Trainees will develop their own projects, with mentorship and consulting support from program staff, which will continue beyond the formal course period. Each course will also feature a slate of guest speakers discussing innovative health research that leverages machine learning to generate new knowledge. The program will culminate in an in-person conference where participants will meet, present their work, and build future collaborations. To facilitate trainees’ future use of these methods in both research and teaching, the project will openly disseminate all curricular and pedagogical materials, including syllabi, computational notebooks, videos, and lesson plans. Overall, this program will build a sustainable and resilient foundation for integrating emerging AI into social science research and teaching.