New Funded Research at CAAI · Fall 2026

The Chicago Booth School of Business is an ecosystem that runs on alumni funding, faculty involvement, and student support. Together they drive discovery across new research areas. Following earlier rounds of research funding, the 2026-27 academic year brings a new set of funded projects from eleven faculty members.

From children’s textbooks and school yearbooks to acute respiratory failure and the frontiers of mathematics, take a look at how the Center for Applied Artificial Intelligence is supporting breakthroughs in business, economics, education, and medicine, in keeping with our mission to drive social benefit through innovation.

New Projects

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AI & Behavioral Science Eric J. Gleacher Distinguished Service Professor of Behavioral Science and Marketing Ayelet Fishbach received funding for four proposals, which she will allocate across the projects at her own discretion. The first asks what children’s textbooks teach about waiting. Fishbach and her team are coding about 2,700 lessons from 338 primary school language textbooks across 33 countries, roughly 7 million words. They then compare how waiting is portrayed with country-level measures of patience, saving, and preventive health behavior. The second tests whether the motivational advice LLMs give reflects lay intuition or the findings of motivation science. The team focuses on cases where the two diverge. The third, with Phuong Le of the Roman Family Center for Decision Research and UChicago MA student Xiaoyu Li, compares weight-loss strategies discussed on Reddit’s r/loseit with advice from AI models and with motivation research. The fourth examines whether LLMs that end responses with open-ended offers of more help leave users less satisfied and less likely to return, because the work never feels finished.

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AI & Finance Assistant Professor of Accounting and IBM Faculty Fellow Charles Downing is testing whether the effort a large reasoning model puts into a task can measure how hard financial information is for humans to process. His team feeds 10-Ks and earnings calls to these models, assigns fixed analysis tasks such as calculating core earnings, and tracks how much reasoning each document requires. Early evidence shows that greater reasoning effort lines up with more economically complex filings. Anomaly-strategy returns are also stronger among filings that are harder for a model to process, and price formation after earnings calls is slower for harder transcripts. Next, Downing will test how the findings hold across open-source models and probe the potential for memorization to bias results.

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AI & Education Christopher Campos, Associate Professor of Economics and Centel Foundation/Robert P. Reuss Faculty Scholar, asks how teacher-facing AI affects teacher productivity and student outcomes. Using national procurement data, he identifies school districts that bought district-wide contracts with K-12 AI platforms such as MagicSchool, SchoolAI, Brisk, and Diffit. His early findings show little change in the year of adoption. By the following spring, though, student achievement is about 0.04 standard deviations higher overall and 0.06 higher in mathematics. Campos is now surveying principals and teachers to learn how the tools are used, whether they save time, expand the tasks teachers take on, or complement other school investments. The work offers some of the first large-scale evidence on how generative AI is reshaping teachers’ work.

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AI & International Finance Why does so little capital flow from rich to poor countries? Emanuele Colonnelli, Joseph L. Gidwitz Professor of Finance and Entrepreneurship, is building a new dataset from 1.6 million sell-side analyst reports spanning 1984 to 2025 to find out. A multi-stage LLM pipeline, paired with document-layout and OCR models, identifies the barriers analysts cite, separates real reasoning from boilerplate, and extracts the price targets, discount rates, and valuation multiples tied to them. This lets Colonnelli trace how perceptions of political instability, currency risk, and governance enter valuations. The pipeline has already processed 488,000 reports, and CAAI funding will help scale it to the full corpus and create an auditable measurement system.

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AI & Entrepreneurship Assistant Professor of Accounting and James S. Kemper Faculty Scholar Marcela Aguilar is extending a randomized controlled trial in Honduras, run with the International Labour Organization and the Chamber of Commerce of Tegucigalpa. The trial compares two WhatsApp-based AI mentors, which differ in how they use accounting knowledge, against a traditional human-led business training program. Her analysis draws on survey and business outcomes and on more than 30,000 entrepreneur and AI messages. With CAAI support, Aguilar will add a hybrid model that pairs AI mentorship with human support and give entrepreneurs access to the mentor for a substantially longer period. The extensions will show when humans complement AI advice and how engagement and business practices change over time.

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AI & Supply Chains Ozan Candogan is studying agentic supply chains through the lens of information design. His project develops autonomous agents and simulates their interactions across a range of supply chain settings. It looks at how agents acquire, transform, transmit, and act on information, and at the distortions they create or pass along. The results will show how agent behavior shapes information quality and decision-making across networks of interdependent decision-makers.

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AI & Operations Research Rad Niazadeh continues his work on how frontier AI models can help operations research theorists. With the Center’s initial support, his team built an end-to-end agentic pipeline that extracts open problems from papers, verifies that they are open, and runs a solver and adversarial-verifier loop to attempt solutions. A separate Lean 4 pipeline produces machine-checked certificates, and eight solutions have been formalized so far. The team’s public database catalogs 159 open problems, 45 with solution write-ups and 112 with partial progress. A collaboration with Google DeepMind’s AlphaEvolve team provides an external benchmark. Next, the project opens the pipeline so researchers can run it on their own problems.

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AI & History John P. Gould Professor of Economics and Neubauer Family Faculty Fellow Richard Hornbeck is turning school yearbooks into data to ask whether desegregation changed not only who attended school together but also how students took part in school life. His team will extend its pipeline to 1,894 California high school yearbooks from 1960 to 1992, covering 116 schools in eight districts that desegregated between 1970 and 1985. Funding for Gemini credits will support multimodal analysis that reads photographs together with captions and headings, distinguishing a posed portrait from students participating together. Because the period includes the passage of Title IX in 1972, the data can show changes in interactions across both gender and racial lines. Early comparisons against hand-coded data are promising.

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AI & Medicine Acute respiratory failure affects more than 3 million US patients each year, and up to 30% receive incorrect initial treatment. Sarah Jabbour, Assistant Professor of Applied AI and Fishman Faculty Fellow, is studying whether multimodal large language models can move beyond narrow predictive tools to diagnose patients and recommend treatment and management plans. She is building a cohort of patients hospitalized with the condition at Chicago Medicine, linked to imaging, structured data, and physical examination notes. Two clinicians annotate each case, with a third breaking ties. Her research asks how accurately these models diagnose, and how AI should be integrated at the bedside to get the most from human-AI teams.

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AI & Human Oversight As AI grows more accurate, careful human verification matters in ever-rarer cases, which makes it hard to sustain. In an ICML 2026 paper, Shuangning Li shows that incentives based on observed accuracy become increasingly costly as error rates approach zero. Her proposed fix is sentinel auditing, which mixes in tasks on which the AI is known to be unreliable and rewards successful correction. Li, Assistant Professor of Econometrics and Statistics and Charles E. Merrill Faculty Scholar, will now test the mechanism in AI-assisted grading. She will build a controlled shadow-grading environment and measure how review time, rubric engagement, and error correction change as the assistant improves. The findings will inform oversight design in education, auditing, compliance, and content moderation.

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AI & Marketing Thomas Wiemann, Assistant Professor of Marketing and Beatrice Foods Co. Faculty Scholar, is using AI chatbot conversations to estimate consumer demand. His project with Joshua Higbee (OSU), “Estimating Demand from Chat,” will use Comscore data that links product-search conversations to the same panelists’ later visits and purchases. Clickstream data shows which pages consumers open but not what they were trying to learn. A shopping conversation records what they asked, what they already believed, and what the chatbot told them. The team will map conversation turns to products and attributes, and later clicks and purchases to the final choice. The work lays the groundwork for understanding how advertising embedded in LLM responses may change consumer decisions.

Research at CAAI

The projects funded by the Center for Applied AI represent more than academic curiosity. They reflect CAAI’s mission to harness AI for meaningful social benefit. As the Center expands its reach and research capabilities, keep an eye out for new opportunities in research, engagement, and collaboration. Stay connected to a community dedicated to the future of AI-driven discovery, and explore our growing collection of research by Booth faculty.