This page highlights AI-powered tools available through the USF Libraries’ subscribed databases and platforms. Many academic vendors have integrated artificial intelligence into their search, discovery, and writing environments to support research efficiency, citation assistance, and content summarization. Tools such as these are designed to enhance—not replace—your own academic work.
While USF Libraries subscribes to numerous resources that now include AI features, we do not independently develop or deploy AI models without careful review, testing, and oversight. The LINK AI ChatBot and the Primo AI Research Assistant are examples of AI integrations developed or implemented with substantial evaluation and transparency, and they are covered in more detail on their respective pages.
Scopus AI is a generative AI-powered enhancement of the established Scopus platform—Elsevier’s curated abstract and citation database. It offers researchers an advanced, intuitive way to explore scholarly topics via natural language queries, moving beyond traditional keyword-based searching.
Advantages:
Makes complex academic discovery faster and more intuitive.
High transparency through source citations and confidence indicators.
Minimizes hallucinations via RAG Fusion and strict prompt engineering.
Very helpful for early-career or interdisciplinary researchers navigating unfamiliar fields.
Considerations:
Currently relies on abstracts—not full text—so nuance may be limited.
Summaries are tools for exploration, not for citation—users should cite original documents.
Predominantly English-language supported, though multilingual input is being assessed.
JSTOR AI is a generative AI-powered enhancement built directly into the JSTOR platform. It allows researchers to ask natural-language questions about scholarly works and receive concise, source-linked summaries. By working at the document level, JSTOR AI helps readers quickly identify key arguments, methods, and evidence within an article or chapter.
Advantages:
Provides clear, source-linked summaries to help evaluate relevance.
Supports natural-language queries for easier exploration of complex works.
Enhances teaching prep by surfacing main arguments and methodologies.
Transparent grounding in the document being viewed, reducing risk of error.
Considerations:
Operates only on the item being viewed—not the full JSTOR corpus.
Summaries are for exploration and teaching support, not citation.
Still in beta development, so features may evolve or expand.