June E-Resources Roundup
The four AIRAs that we will be introducing in this Roundup are:
- EBSCO Natural Language Search
- Statista Research AI
- JSTOR AI Research Tool
- Washington Post Ask The Post AI
EBSCO
Provider: EBSCO
Name of AIRA: EBSCO Natural Language Search [name change coming July 7, 2026 to AI-assisted search; see Additional Information]
Status: Enabled, included in subscription
URL: https://about.ebsco.com/artificial-intelligence/products/natural-language-search
Additional Information: Message from EBSCO: To more clearly communicate how the feature works and the value it provides, we are renaming “Natural Language Search” to “AI-assisted search.” This change is intended to improve clarity, usability and adoption, while the underlying capabilities remain the same. Visit our page on EBSCO Connect for more information on this feature.
Brief Summary:
- Natural Language Search applies AI techniques to parse user queries into keywords and noun phrases. This approach aims to capture contextual elements and better represent user intent.
- Once a query is parsed, it is passed to the EBSCO search engine, where it is processed using the same relevancy ranking and specialized search functions used in traditional search.
- Natural Language Search can be configured as the primary search mode or used alongside traditional search methods.
- This approach is designed to support users who may be less familiar with advanced search techniques by reducing the need to construct complex queries.
Statista
Provider: Statista
Name of AIRA: Research AI
Status: Enabled, included in subscription
URL: https://www.statista.com/research-ai/
Additional Information: Must create an account and log in to use Research AI.
Brief Summary:
Research AI is a conversational interface developed by Statista for accessing and synthesizing content within its data platform. For each user query, the system interprets intent, retrieves relevant Statista content, and generates a response that includes source citations to support traceability of figures and claims.
Responses are structured to present information clearly, typically combining narrative summaries with inline citations. Depending on the query, outputs may also include brief summaries (such as TL;DR sections), key data points, bullet-point lists, tables for comparisons or timelines, and calculations (e.g., year-over-year change, compound annual growth rate, or market share). The system may incorporate external reference data where applicable, such as population figures from the United Nations or exchange rates from the European Central Bank for currency conversions. It also maintains context across interactions to support iterative refinement of queries.
The system draws on Statista’s curated content, including statistical datasets and their associated metadata. Additional contextual information may be incorporated from related Topic, Report, and Industry pages. Infographics are represented through their underlying data rather than as standalone visuals. Content from Market Insights is included to provide high-level summaries that complement quantitative data.
JSTOR
Provider: JSTOR
Name of AIRA: AI Research Tool
Status: Enabled, included in subscription
URL: https://about.jstor.org/products/jstor-platform/features-and-tools/research-tool/
Additional Information: Accessing JSTOR’s AI-enabled features requires a personal account to support transparency and shared accountability for how the tool is used. Logging in helps JSTOR manage user permissions effectively, safeguarding user data and privacy while allowing for tailored access based on institutional affiliations and user preferences. This approach helps balance transparency, academic integrity, and user support.
Brief Summary:
The AI Research Tool is integrated into content pages for journal articles, book chapters, and research reports on JSTOR, as well as within search workflows. It is designed to support research activities beyond standard keyword matching in the following ways:
- Assess content relevance:
The tool highlights key points and central arguments to help users evaluate whether a text is relevant to their research. - Support deeper exploration:
It identifies related topics and materials across the JSTOR corpus, enabling broader discovery of relevant scholarship. - Enable conversational interaction:
Users can conduct searches using natural language or pose questions about the content they are reading.
Currently, the tool works with journal articles, book chapters, and research reports available on JSTOR. It does not support images, audio, video, or text-based primary sources at this time.
Washington Post
Provider: Washington Post
Name of AIRA: Ask The Post AI
Status: Enabled, included in subscription
URL: https://www.washingtonpost.com/ask-the-post-ai/
Additional Information: Users must create an account with their VCU email address in order to access.
Brief Summary:
Ask The Post AI is a system that answers reader questions by pulling from the publication’s own news articles dating back to 2016. It lets our readers ask questions about the news of the day, get additional context and background on a topic they care about, and stay on top of what matters to them. All grounded in our published reporting. It identifies relevant articles, then uses a large language model to generate a summary‑based response grounded in that reporting. If the tool cannot interpret a question or lacks relevant material, it may return a pre‑written answer. Readers can access it on a dedicated page, within articles, or through the site’s search feature, and they can ask follow‑up questions in a conversational format. If you have questions about this tool, please see our FAQ and AI policy or contact customer care.