As our society and technology continue to evolve rapidly, there is a growing interests by adults in taking an active role to continue to learn on their own as hey age. One of the most common lifelong learning scenarios is gaining knowledege from an algorithm-mediated information environment, such as using the internet to explore unfamiliar topics or interact with conversational agents to adopt new health behaviors. Contemporary algorithm-mediated information retrieval (IR) systems, including search engines and conversational agents, have limitations when it comes to adequately supporting users with complex information needs, particularly those related to learning-oriented search tasks. Finding information does not necessarily lead to effective learning or deep comprehension. Moreover, IR algorithms often neglect users needs or intentions for the search - one search does not fit all. Measures of intent and algorithms need to be tuned to individual users or risk misaligning with the learning goals of users. Therefore, developing IR systems for learning requires understanding how individuals monitor, assess, and regulate their learning progress and what factors shape their judgments to persist in or disengage from learning. To facilitate the translation of research into instructional and outreach practices, the project will collaborate with the Osher Lifelong Learning Institution and the National Multiple Sclerosis Society to co-design educational games and webinars that foster