Designing a Large Language Model Architecture for Shared Decision Making and Informed Intentions

This study investigates a novel, agentic Large Language Model (LLM) architecture designed to facilitate Shared Decision-Making (SDM) in healthcare. While patients increasingly use standard LLMs for health information, these models often struggle with multi-turn conversations and fail to adapt to varying reading levels, disadvantaging vulnerable groups. By utilizing an agentic state-machine, this project aims to overcome common LLM deficits-such as context loss and uncritical agreement-to ensure clinically accurate, participatory patient conversations. The study evaluates whether this architecture improves informed decision-making compared to standard LLMs, particularly for patients with low health literacy.

Inclusion Criteria: * Biological sex: Female. * Age: 40 to 70 years old. * Country of residence: United States (US) or United Kingdom (UK). * Language: Native English speaker (English as first language). * Registered and verified user on the academic research platform Prolific. * Able to read, understand, and provide informed consent digitally. Exclusion Criteria: * Individuals who do not meet the automated pre-screening criteria on the Prolific platform. * Inability to use or access a computer, smartphone, or internet browser required to complete the digital study. Note: Quotas will be enforced during recruitment to ensure a 50/50 stratified split between participants with and without university entrance qualifications to guarantee variance in educational backgrounds. Once a quota is filled, further participants matching that educational profile will be excluded. \-