Results of a set of experiments found that individuals learning about a topic from large language model summaries develop shallower knowledge compared to when they learn through standard web search. Individuals who learned from large language models felt less invested in forming their advice, and created advice that was sparser and less original compared to advice based on learning through web search. The research was published in PNAS Nexus. Large language models (LLMs) are artificial intelligence systems designed to interpret and generate human language by learning statistical patterns from vast collections of text. They are typically based on deep learning architectures, which allow them to process context and relationships between words over long passages. The most popular large language models today include those developed by OpenAI (GPT series used in ChatGPT), Google (Gemini), Anthropic (Claude), and Meta (LLaMA). The development of large language models has progressed rapidly over the last decade due to advances in computing power, the availability of large datasets, and improvements in training algorithms. Early models focused mainly on simple text prediction, …