Accelerate your research and gain new insights with IEEE Xplore AI
Now available as an optional subscription upgrade for select subscribers.
The IEEE Xplore AI Research Suite provides a new set of AI-powered features that enhance a user’s experience with IEEE Xplore. These features allow users to easily search and discover articles of interest from IEEE and other STEM publishers, accelerate their understanding of individual papers, and gain new insights. This solution offers powerful research tools that enable natural language queries, an AI-powered search that delivers relevant and accurate results with time-saving summary overviews, AI-generated topic and article summaries, and in-context definitions to help researchers grasp new concepts faster.
By combining these powerful features, the IEEE Xplore AI Research Suite transforms the research experience to help researchers deepen understanding, quickly assimilate new ideas, and accelerate research efforts.
The IEEE Xplore AI Research Suite provides:
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Time Savings and Increased Comprehension: Discover new insights faster with IEEE Xplore AI. Quickly understand more about your topic with the IEEE AI Overview and AI Article Summaries.
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Trusted Content: The only AI research assistant that allows users to search both the full-text and metadata of IEEE’s peer-reviewed journal articles and conference papers.
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Comprehensive Coverage: AI Search also provides access to an extensive range of STEM metadata from journal articles and conference papers. The selection is expertly curated to include scholarly articles in STEM, without the typical noise in other tools.
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Verifiable Results: AI overviews reference the source documents to ensure you can quickly and easily verify and trust the answers provide
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User-Friendly Interface: Allows users to input freeform semantic queries while also providing smart keyword suggestions to refine search terms.
For more information about getting access to these features for your organization, contact us.
Features | FAQs | Use Cases | Resources | Get Access
IEEE Research Navigator
Powered by IEEE Xplore AI
IEEE Research Navigator provides an advanced query tool for journal articles and conference papers published by IEEE as well as a database of other publishers focused on STEM-related fields of study—all in one place.
This AI search feature provides a unified interface for various types of searches—from asking a natural language query to entering a string of text—offering users a seamless experience. The AI search understands the meaning and intent behind queries to deliver relevant and accurate results. AI overviews provide an easily digestible summary of the results to provide a quick, time-saving overview of a topic with links to sources for further exploration.
Additionally, AI-generated article summaries deliver a concise synopsis of the article to deepen understanding, grasp new concepts faster, and accelerate research efforts.
Users can freely ask a research question, paste in text snippets, type a few sentences, and then query the top-matching articles based on the content, intent, and meaning of the users’ input.
IEEE Reading Lens
Powered by IEEE Xplore AI
IEEE Reading Lens provides an enhanced view of an article to help researchers learn new concepts quickly and effectively. The IEEE Xplore AI model finds key terms, highlights them, and provides in-context definitions on each term, and more.
These features help improve the reading experience by presenting supporting content for additional insights without having to leave the article, enhancing understanding while making the research process faster and easier.
Powered by IEEE Xplore AI, the annotated article provides concise technical information about the highlighted terms without needing external web browsing while reading the articles.
Frequently Asked Questions and IEEE AI Principles
Third party tools receive limited information – they will not receive any information about users or institutions – queries are agnostic of users' attributes and completely decoupled. Any data used by a third-party tool is broken down, filtered, blended with our prompt/engine design, then combined with the relevant data from our corpus. No data is stored in or used to train third-party tools, as these tools have a zero retention policy.
IEEE may occasionally review the searches and results for the purposes of assessing the quality of responses, but the queries themselves and the associated user data that entered the query will not be used for AI training
IEEE Xplore AI was developed in-house by IEEE. It leverages a variety of external software, including pre-built Large Language Models (LLMs) that are then fine-tuned by IEEE, along with proprietary software and algorithms that have been appended to these components.
Our dedicated data science team trains and fine-tunes our own instance of an LLM, optimizing the engine for IEEE and other scientific content and domain-specific terms, and continually tests and makes adjustments to ensure relevance and quality of results.
It also uses AI Inference through Retrieval Augmented Generation to make the tool authoritative, citing sources and grounding the outputs in up-to-date information.
The IEEE Xplore AI has been designed with extremely strict instructions to limit the search retrieval process to only query the IEEE peer-reviewed full text or the expertly curated set of STEM-related metadata and abstracts from OpenAlex. Following this stringent criteria, the AI engine’s response must match the intent of your query and will abstain from providing a response if the research results do not retrieve any STEM-related abstracts that are relevant to the user’s query.
If the IEEE Xplore AI is unable to find relevant scientific content from these strictly defined databases, it will inform the user. In addition, the AI Overviews reference and link to the source documents to ensure users can quickly and easily verify and trust the answers provided
Finally, our dedicated data science team continually tests and fine-tunes the IEEE Xplore AI to ensure relevance and quality of results.
Together, these factors greatly reduce the risk of any hallucinations.
IEEE Xplore AI was developed with several safeguards to prevent risk that bias will impact any answers provided:
- IEEE Xplore AI has been designed with extremely strict instructions to limit the search retrieval process to only query the IEEE peer-reviewed full text or the expertly curated set of STEM-related metadata and abstracts. As a result, any biases that may be present in other sources on the open web cannot impact the results provided.
- In addition, it provides responses that match the purpose and intent of a user query to provide relevant results. As a result, other criteria such as the number of citations an article has received, the popularity of a journal or the reputation of an author do not impact the set of results retrieved. If the user does have a specific need to search based on factors such as article citation rate, article popularity, or works from a specific author, IEEE Xplore has other search tools and filters that the user can utilize that are not AI-powered.
- IEEE also has several feedback mechanisms in place that enable users to report any issues with responses.
- IEEE Xplore AI is equipped with guardrails to filter out ‘unsafe’ answers; these are typically responses that exacerbate prejudice, harm, or stereotypes against specific individuals or groups.
IEEE conducted extensive alpha and beta tests of the IEEE Xplore AI Research Suite from April 2025 through May 2026. During this time, over 200 customer accounts and more than 3,000 users tested the features and provided valuable feedback. We also collaborated with our own network of IEEE volunteer engineers and scientists, as well as the library and information professional community to ensure the features and results would be relevant and useful to researchers globally. All of this research and feedback helped inform decisions, helped shape the product and its features and functionality and optimize the accuracy and relevance of the result delivered.
Unlike general-purpose AI chatbots, the IEEE Xplore AI Research Suite was custom-built for researchers and optimized for IEEE content and other scientific publications to deliver the best possible results based on the highest quality data in the field of engineering and computing.
While we can’t speak to detailed specs of the foundational technology used in other AI tools, the research tools provided in the IEEE Xplore AI Research Suite were developed following industry best practices to prioritize data and security, ethical AI principles (fairness, transparency), and utilize high-quality data, continuous monitoring, and integrate human oversight to ensure reliability and relevancy of results.
In addition, it is the only AI solution available in the industry that leverages the full-text of quality, peer-reviewed journals and conferences from IEEE to fine-tune the LLM, providing an AI solution that is optimized for IEEE content and other scientific publications to deliver the best possible results based on the highest quality data in the field of engineering and computing. It is also the only solution that is fully integrated with IEEE Xplore, seamlessly aligning with the researcher’s current workflow and research process.
How the IEEE Xplore AI Research Suite Supports Research
A researcher applying for funding pastes a draft section of their draft application to find semantically similar research, helping them strengthen their case by citing aligned studies, distinguishing how their research is novel, is distinct from prior research, and can advance a particular field of study.
A software engineer is developing a low-power IoT sensor for smart home applications and needs to optimize power consumption. The engineer pastes a technical description of the device’s battery life constraints into the search bar. AI Search retrieves semantically similar articles discussing adaptive power-saving techniques, and the engineer finds recent studies on AI-powered dynamic voltage scaling that could be integrated into the product.
A robotics engineer is designing a drone for industrial inspections and needs to research navigation and obstacle avoidance methods. The engineer pastes a paragraph from the system design document describing the drone’s operating conditions (e.g., high winds, GPS-denied environments). AI Search retrieves articles discussing semantically similar challenges, such as vision-based navigation for drones in complex terrains. These results help the engineer identify novel deep learning-based path planning algorithms that could enhance drone efficiency.
A student inputs a section of their dissertation’s introduction regarding challenges in next-generation energy storage. AI Search analyzes the technical context of their problem statement to retrieve semantically similar articles that explore specific novel materials (like solid-state electrolytes or graphene composites). This allows the student to discover cutting-edge candidates for their research they may not have known to search for by name.
An engineer is reviewing multiple research papers to identify the most commonly used algorithms for a machine learning project. The engineer can select “Algorithms” from the Term Categories panel and find algorithm-related terms within the article without reading the entire document.
A graduate student is reviewing a dense paper on artificial intelligence but struggles with technical jargon. The student can view easy-to-understand explanations of complex terms directly in the text. They can also see content suggestions to explore additional learning resources.
A researcher working on a literature review for natural language processing (NLP) needs to track methods from multiple articles. By selecting the “Methods” category in the Reading Lens term categories panel, they can instantly highlight all “Methods” across the text, allowing them to skim for specific techniques without reading unrelated sections.
