Using ITHAKA’s Product Tracker to Prepare for Generative AI

The ITHAKA Product Tracker can be an excellent learning tool

Posted on August 4, 2025 in Blog Posts

Librarians are all looking for ways to prepare for how generative artificial intelligence (GAI) might change our jobs and our users’ research habits. However, the number of new GAI tools can be overwhelming. Happily, ITHAKA, the nonprofit behind JSTOR, has put together a GAI “Product Tracker.” It lists tools that are either widely used in higher ed or intended for a higher ed audience and tracks tools under development. The Tracker is formatted as a table, and it includes basic information about each tool, including fields for Purchasing Model, Description, Key Features, Pros, Limitations, and  Comments. The Tracker can be accessed as a web page or through a Google document. The size of the list might still feel overwhelming, but by providing clear categories and real examples of GAI tools, the Tracker helps give structure to the chaos. 

In this post, I will suggest a list of simple, active ways that librarians can use the Product Tracker to help better prepare for the growth of GAI. This is how I’ve used the Tracker, and I wish to share my insights!

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Learn about the Tracker and about GAI

The Product Tracker and its associated report are great resources that can help librarians learn about specific GAI tools and give a feel for broad trends in AI. Simply by looking through a sampling of tool entries, you can begin to become familiar with which types of tools will best meet the needs of various patrons. 

The classifications in the Tracker don’t just serve to describe existing tools; they can help us conceptualize new tools and fit them into the existing landscape. When we read about yet another new product, we can use the Tracker’s categories to help us think about what the new tool does and how it might affect higher ed. The Tracker sorts products into eight categories: General Purpose Tools, Discovery Tools, Teaching & Learning Tools, Research Workflow Tools, Writing Tools, Coding Tools, Image Generation Tools, and Other. General purpose tools, discovery tools, and research workflow tools are most likely to immediately affect library activities. Try just reading through the Tracker’s short descriptions of tools in each of these categories to learn about specific tasks that can be done with GAI in each of the groupings. 

Librarians should pay particular attention to tools that vendors that already provide resources to your library are developing. Search the Tracker to find them, even if your school doesn’t subscribe to GAI tools currently. If you occasionally review the Tracker, you are less likely to be caught unaware by a new product. It’s important to become familiar with what publishers are offering to make sure that we are ready to make decisions about subscription add-ons and provide reference services if databases fully integrate GAI. Elsevier and Clarivate are leaders among the major publishers, each offering multiple GAI tools to schools at certain subscription tiers. However, EBSCO, Statista, Oxford UP, and JSTOR are all producing tools that will change our databases. Skim the listing for vendors you recognize and follow up by searching for reviews, instructional resources, or vendor information on specific tools that look relevant to your work. 

Test and try

You can also use the Product Tracker to find and try both popular and less common GAI tools. Trying out different tools is one of the best ways to learn what GAI can do for your users. Even if you have already tested ChatGPT or looked at Google Gemini results, exploring specialized tools will provide a more complete sense of the wide variety of tasks that GAI is being used for. The Tracker includes a “purchasing model” field that will let you select free tools or tools with free trials in each category. 

Fully open projects are also worth a special look, as you can decide if you want to incorporate them into your reference and teaching right away. For example, Ai2 Scholar QA is an open deep search tool specifically built to assist the processes of creating literature reviews. It also provides basic explanations for how it produces its results. Actual results may be similar to those of Undermind, a major proprietary tool that offers only limited free searches. Also try comparing Ai2 Scholar QA’s scholarly-literature-focused output to that of Google’s Gemini Deep Research. Deep Research allows a limited number of free reports, and in my testing, I found that it includes more general web sources while focusing on longer, narrative answers. Using the free tools can prepare you for understanding the usage of the paywalled ones. 

As you explore tools, practice applying basic information literacy skills in this new environment. Compare not just the functionality of similar products, but their results as well. Use the fact-checking strategies as you would on any information resource on GAI outputs. Rely on lateral reading, check sources to see if they actually support the statements they are cited for, and draw on contextual knowledge to check for hallucinations and biases. Librarians know valuable strategies for identifying reliable content, and we need to learn how to tweak them to work with GAI results. Some GAI products cite sources and others don’t; practice fact-checking both. Start with topics you know well to spot hallucinations easily, but don’t just stick to familiar searches. We all need practice searching unfamiliar subjects since reference desk patrons will always find research topics that surprise us. 

Evaluate and share

While you explore, use your professional knowledge to begin judging the tools. The Tracker doesn’t pass value judgments, but librarians need to think about what the best tools are for different use cases. In an academic library, we have a duty to help patrons find both the right information resources and the right tools that they can use to find more information resources on their own. For some queries, GAI may already be the best way to identify information and prioritize the relevancy of articles. We can’t abdicate our responsibility to provide patrons the best assistance we can. Whether we personally are early or late adopters of GAI, enthusiastic or skeptical, we must meet users where they are and be prepared to answer their questions. We can all be knowledgeable, even if some of us may favor a slow pace of GAI adoption.

You know your users best and will be able to identify which tools from the Product Tracker will be of the most use to your community of researchers. However, there is a growing discourse about how to evaluate GAI tools, which can help. Librarians have created GAI-tailored product assessment frameworks, compared products head-to-head, and defined adoption frameworks that can guide librarians when implementing AI. Standards for vendor usage statistics reporting are still developing, and librarians should take care to note what reporting will be provided when evaluating new products. 

Finally, we must share what we learn! Librarians have unique information literacy skills that position us to make meaningful contributions to the discussion on how GAI should be used in higher education. If we want to have a “seat at the table,” we must establish ourselves as knowledgeable experts on GAI now, while norms are developing. Teach about GAI in your one-shot classes, consider using it during reference when appropriate, and speak knowledgeably to faculty about how literature reviews are changing. Use the ITHKA Product Tracker to develop the confidence to share your opinions on GAI.

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