To utilize Semantic Scholar, users begin by employing its AI-powered search engine, which allows queries by keyword, author, conference, and more. Filters can be applied for publication venues, authors, or timeframes to refine results. Unlike conventional search, Semantic Scholar's AI algorithms understand the meaning and context of papers, delivering precise and contextualized findings. Once relevant papers are identified, users can leverage 'TLDR Summaries' to quickly grasp a paper's main objectives and results, assessing its relevance without reading the full abstract. For deeper engagement, the 'Semantic Reader' offers an augmented PDF experience, providing in-line citation cards with TLDRs and skimming highlights to make complex papers more accessible. Researchers can create a free account to personalize their experience by setting up 'Research Feeds' that use AI to recommend the latest relevant research based on their interests, ensuring they stay informed about emerging trends. Accounts also enable users to save papers, organize them into 'Libraries', and set up email 'Alerts' for new publications or citations. The platform also facilitates 'Citation and Research Network Analysis', allowing users to explore citation networks, track influential works, and visualize connections among researchers and institutions, complete with citation velocity scores. 'Entity Identification' provides quick overviews of key elements like authors and journals. For developers and institutions, Semantic Scholar promotes open access and offers programmatic access to its data via the Semantic Scholar Academic Graph (S2AG) RESTful API and downloadable datasets, supporting the development of scholarly applications. Furthermore, the platform ensures 'Interoperability' by allowing users to export bibliographic references in common formats like BibTeX for integration with tools such as Zotero or Mendeley.