This National Science Foundation (NSF) award under the Computer and Information Science and Engineering (CFDA 47.070) program provides $124,108 to Cornell University to research symbolic learning with neural language models for artificial intelligence (AI) systems. The project aims to develop new AI methods for learning symbolic knowledge represented as computer code, combining statistical approaches, large language models like ChatGPT, and program synthesis. The goal is to create AI systems that can learn more abstract forms of knowledge from fewer examples and describe their knowledge in human-understandable ways. This 5-year project will involve student researchers, particularly from underrepresented groups, and inform new AI-focused graduate and undergraduate courses at Cornell. No subawards are planned for this award.