Research & Discovery
These tools use artificial intelligence to enhance the research process by helping users locate, analyze, and connect scholarly information more efficiently.
They can identify related studies, visualize citation networks, generate statistical insights, and recommend appropriate journals for publication.
Use them to broaden search strategies and uncover new connections within the scholarly landscape. Always verify results through authoritative databases and critically evaluate sources for accuracy, bias, and reliability.
Research Discovery and Search Tools
Perplexity.ai
A conversational AI search engine
Perplexity combines publicly available web data with large language models (LLMs) to generate concise, sourced answers to user queries. It retrieves and summarizes current web content, displaying inline citations that link to original sources.
The tool emphasizes transparency by showing references, but users should verify results for accuracy, completeness, and bias.
Semantic Scholar
Free, nonprofit AI search engine for scholarly literature
Developed by the Allen Institute for AI, Semantic Scholar uses artificial intelligence to surface relevant, high-quality research. It includes features such as topic summaries, citation highlights, and author influence metrics.
As an open, academically focused alternative to commercial AI search tools, it offers students and faculty an ethical and accessible option for exploring scholarly work. Its growing AI capabilities make it a strong teaching example for understanding AI-assisted discovery within trusted research environments.
OpenAlex
Open database of scholarly metadata powering AI-driven tools
OpenAlex provides open data on scholarly works, authors, and institutions that power many AI-driven discovery systems, including Litmaps and Research Rabbit. Launched in 2022, it represents a major advancement in open infrastructure for academic research.
Including OpenAlex illustrates how AI systems source their data and how transparency and equity in access shape future scholarly communication. For librarians and advanced researchers, it offers a foundation for exploring the data ethics and metadata structures behind AI research tools.
Literature Mapping and Analysis Tools
Research Rabbit
Citation mapping and literature discovery tool
Research Rabbit helps researchers visualize connections between papers, authors, and research topics. It uses machine learning to identify similar works and recommend related studies, supporting literature review development and exploration of scholarly networks.
Research Rabbit has not disclosed information about its underlying models or bias mitigation practices.
Litmaps
Citation-mapping and trend-visualization platform
Litmaps uses citation data and machine learning to visualize how ideas and research topics evolve over time. It helps researchers identify influential papers, emerging themes, and gaps within the scholarly conversation.
Complementing tools like Research Rabbit, Litmaps emphasizes chronological connections and thematic development. Increasingly added to university library guides in 2024–2025, it supports graduate-level research and instruction focused on literature mapping and discovery strategies.
Statista (Research AI)
Data-driven question and visualization tool
This feature enables users to ask data-focused questions in natural language and receive concise summaries and visualizations based on Statista’s verified reports and statistics. It uses large language models (LLMs) to interpret queries and produce responses drawn solely from Statista’s internal database, not external sources.
Transparency or bias documentation has not been provided.
Available through the SJSU Library
AI-Enhanced Reference and Publication Tools
Taylor and Francis (Journal Suggester)
AI-assisted journal recommendation tool
This tool helps authors identify suitable Taylor & Francis journals for their manuscripts. It uses natural language processing (NLP) to analyze a paper’s title and abstract, comparing the content to published articles across Taylor & Francis journals to generate recommendations.
Details about training data or bias mitigation methods have not been made public.
Available through the SJSU Library
Oxford English Dictionary
Natural language search enhancement (Pilot)
This pilot tool allows users to pose questions in natural language, which it translates into advanced dictionary queries to retrieve relevant OED entries. The AI Search Assistant does not generate new definitions but reformulates user input to improve search precision.
Oxford University Press has not disclosed details about the model or training data used in this pilot.
Available through the SJSU Library
Elicit
AI-assisted literature-review and evidence synthesis tool
Elicit helps users locate and summarize scholarly papers in response to research questions. Rather than generating new text, it extracts key details such as study aims and methods, making it a reliable entry point for evidence-based exploration.
Increasingly adopted in higher education since 2023, Elicit is useful for faculty and graduate students conducting systematic or scoping reviews. Its emphasis on transparency and verifiable sources aligns closely with library values of responsible research and information literacy.