Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
To expand and enhance the abundance of research in applied artificial intelligence at The University of Chicago Booth School of Business, the Center for Applied AI as acquired two large-scale datasets. Researchers can now access millions of LinkedIn job listings from Bright Data as well as millions of rental listings from Dwellsy. To facilitate efficient exploration of what the datasets offer, CAAI also launched companion chatbots that sift through the data using plain English instead of SQL.
Together, the new tools significantly broaden the questions Booth researchers can pursue across labor economics, housing markets, finance, urban policy, and the rapidly shifting intersection of AI and the workforce.
Supplied by Bright Data, the LinkedIn Job Listings dataset includes millions of postings scraped from publicly accessible LinkedIn job pages. The data captures the texture of the labor market in granular detail: job titles, employer attributes, required skills, posting and removal dates, locations, salary information, and the written content of job descriptions. For researchers studying labor demand, skill formation, and the impact of generative AI on the workforce, this is one of the most abundant, publicly observable sources available.
The Dwellsy Rental Listings dataset is provided by Dwellsy, a rental marketplace aiming to bring solutions to a dysfunctional rental data ecosystem. Through Dwellsy, property managers and landlords can list properties for free. Listings correspond directly with property management systems, ensuring that listings reflect real, currently available inventory rather than the duplicates, stale entries, and bait-and-switch posts characteristic of many rental platforms. The dataset features listings across the U.S., with detail on rent price, housing unit characteristics, location, and availability status.
New research possibilities are infinite and will certainly combine to be greater than the sum of their parts. With Booth ingenuity, potential research areas may include:
For researchers in finance, microeconomics, behavioral science, applied AI, and beyond, the datasets supply the empirical raw material for the kind of rigorous, and specific yet broadly applicable research characteristic of Chicago Booth. New studies facilitated by the datasets may open the door to interdisciplinary collaboration with researchers in public policy, sociology, and computer science whose questions can now be explored through shared data infrastructure.
Alongside the datasets, CAAI has launched chatbot interfaces designed to make the data immediately useful and more broadly accessible to researchers, regardless of their level of familiarity with SQL.
With the Dwellsy chatbot, a researcher can search a built-in data dictionary that explains every table and field. They can ask questions in plain-English—”What is the median rent for two-bedroom apartments in Chicago over the last six months?” The chatbot translates the question into SQL, runs it on the dataset, and replies with both the result and the generated query.
That last detail is intentional. By providing the SQL alongside the answer, the tool aims to create workflow of trust and refinement where researchers can clearly see the chatbot’s course of action, verify its interpretation, and iterate accordingly. This way, the chatbot serves as a starting point for exploration, not a black-box oracle.
CAAI’s mission is to place advanced AI tools in front of Booth researchers, and the launch of these two datasets and their accompanying chatbots is a concrete expression of that goal. By pairing rich, real-world data with interfaces that meet researchers where they are, CAAI is shortening the distance between a budding research idea and a first credible examination of empirical data.
The next iteration of working research papers from Booth may well begin with a plain-English question typed into a chatbot…and end with insights that would not have been possible to generate at this speed even a year ago.
Booth faculty, PhD students, and research staff interested in exploring the new datasets or requesting full access can contact the Center for Applied AI.
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