In the morning, before you even open your eyes, your wearable device has already checked your vitals. By the time you brush your teeth, it has scanned your sleep patterns, flagged a slight irregularity, and adjusted your health plan. As you take your first sip of coffee, it’s already predicted your risks for the week ahead.

Georgia Tech researchers warn that this version of AI healthcare imagines a patient who is "affluent, able-bodied, tech-savvy, and always available." Those who don’t fit that mold, they argue, risk becoming invisible in the healthcare system.

The Ideal Future

In their study, published in the Proceedings of the ACM Conference on Human Factors in Computing Systems, the researchers analyzed 21 AI-driven health tools, ranging from fertility apps and wearable devices to diagnostic platforms and chatbots. They used sociological theory to understand the vision of the future these tools promote — and the patients they leave out.

“These systems envision care that is seamless, automatic, and always on,” said Catherine Wieczorek, a Ph.D. student in human-centered computing in the School of Interactive Computing and lead author of the study. “But they also flatten the messy realities of illness, disability, and socioeconomic complexity.”

Four Futures, One Narrow Lens

During their analysis, the researchers discovered four recurring narratives in AI-powered healthcare:

1. Care that never sleeps. Devices track your heart rate, glucose levels, and fertility signals — all in real time. You are always being watched, because that’s framed as “care.”
2. Efficiency as empathy. AI is faster, more objective, and more accurate. Unlike humans, it doesn’t get tired or biased. This pitch downplays the value of human judgment and connection.
3. Prevention as perfection. A world where illness is avoided through early detection if you have the right sensors, the right app, and the right lifestyle.
4. The optimized body. You’re not just healthy, you’re high-performing. The tech isn’t just treating you; it’s upgrading you.

“It’s like healthcare is becoming a productivity tool,” Wieczorek said. “You’re not just a patient anymore. You’re a project.”

Not Just a Tool, But a Teammate

This study also points to a critical transformation in which AI is no longer just a diagnostic tool; it’s a decision-maker. Described by the researchers as “both an agent and a gatekeeper,” AI now plays an active role in how care is delivered.

In some cases, AI systems are even named and personified, like Chloe, an IVF decision-support tool. “Chloe equips clinicians with the power of AI to work better and faster,” its promotional materials state. By framing AI this way — as a collaborator rather than just software — these systems subtly redefine who, or what, gets to be treated.

“When you give AI names, personalities, or decision-making roles, you’re doing more than programming. You’re shifting accountability and agency. That has consequences,” said Shaowen Bardzell, chair of Georgia Tech’s School of Interactive Computing and co-author of the study.

“It blurs the boundaries,” Wieczorek noted. “When AI takes on these roles, it’s reshaping how decisions are made and who holds authority in care.”

Calculated Care

Many AI tools promise early detection, hyper-efficiency, and optimized outcomes. But the study found that these systems risk sidelining patients with chronic illness, disabilities, or complex medical needs — the very people who rely most on healthcare.

“These technologies are selling worldviews,” Wieczorek explained. “They’re quietly defining who healthcare is for, and who it isn’t.”

By prioritizing predictive algorithms and automation, AI can strip away the context and humanity that real-world care requires. 

“Algorithms don’t see nuance. It’s difficult for a model to understand how a patient might be juggling multiple diagnoses or understand what it means to manage illness, while also navigating other important concerns like financial insecurity or caregiving. They are predetermined inputs and outputs,” Wieczorek said. “While these systems claim to streamline care, they are also encoding assumptions about who matters and how care should work. And when those assumptions go unchallenged, the most vulnerable patients are often the ones left out.” 

AI for ALL

The researchers argue that future AI systems must be developed in collaboration with those who don’t fit in the vision of a “perfect patient.” 

“Innovation without ethics risks reinforcing existing inequalities. It’s about better tech and better outcomes for real people,” Bardzell said. “We’re not anti-innovation. But technological progress isn’t just about what we can do. It’s about what we should do — and for whom.”

Wieczorek and Bardzell aren’t trying to stop AI from entering healthcare. They’re asking AI developers to understand who they’re really serving.

 

Funding:
This work was supported by the National Science Foundation (Grant #2418059). 

 

A new version of Georgia Tech’s virtual teaching assistant, Jill Watson, has demonstrated that artificial intelligence can significantly improve the online classroom experience. Developed by the Design Intelligence Laboratory (DILab) and the U.S. National Science Foundation AI Institute for Adult Learning and Online Education (AI-ALOE), the latest version of Jill Watson integrates OpenAI’s ChatGPT and is outperforming OpenAI’s own assistant in real-world educational settings.

Jill Watson not only answers student questions with high accuracy. It also improves teaching presence and correlates with better academic performance. Researchers believe this is the first documented instance of a chatbot enhancing teaching presence in online learning for adult students.

How Jill Watson Shaped Intelligent Teaching Assistants

First introduced in 2016 using IBM’s Watson platform, Jill Watson was the first AI-powered teaching assistant deployed in real classes. It began by responding to student questions on discussion forums like Piazza using course syllabi and a curated knowledge base of past Q&As. Widely covered by major media outlets including The Chronicle of Higher Education, The Wall Street Journal, and The New York Times, the original Jill pioneered new territory in AI-supported learning.

Subsequent iterations addressed early biases in the training data and transitioned to more flexible platforms like Google’s BERT in 2019, allowing Jill to work across learning management systems such as EdStem and Canvas. With the rise of generative AI, the latest version now uses ChatGPT to engage in extended, context-rich dialogue with students using information drawn directly from courseware, textbooks, video transcripts, and more.

Future of Personalized, AI-Powered Learning

Designed around the Community of Inquiry (CoI) framework, Jill Watson aims to enhance “teaching presence,” one of three key factors in effective online learning, alongside cognitive and social presence. Teaching presence includes both the design of course materials and facilitation of instruction. Jill supports this by providing accurate, personalized answers while reinforcing the structure and goals of the course.

The system architecture includes a preprocessed knowledge base, a MongoDB-powered memory for storing conversation history, and a pipeline that classifies questions, retrieves contextually relevant content, and moderates responses. Jill is built to avoid generating harmful content and only responds when sufficient verified course material is available.

Field-Tested in Georgia and Beyond

In Fall 2023, Jill Watson was deployed in Georgia Tech’s Online Master of Science in Computer Science (OMSCS) artificial intelligence course, serving over 600 students, and in an English course at Wiregrass Georgia Technical College (WGTC), part of the Technical College System of Georgia (TCSG).

A controlled A/B experiment in the OMSCS course allowed researchers to compare outcomes between students with and without access to Jill Watson, even though all students could use ChatGPT. The findings are striking:

• Jill Watson’s accuracy on synthetic test sets ranged from 75% to 97%, depending on the content source. It consistently outperformed OpenAI’s Assistant, which scored around 30%.
• Students with access to Jill Watson showed stronger perceptions of teaching presence, particularly in course design and organization, as well as higher social presence.
• Academic performance also improved slightly: students with Jill saw more A grades (66% vs. 62%) and fewer C grades (3% vs. 7%).

A Smarter, Safer Chatbot

While Jill Watson uses ChatGPT for natural language generation, it restricts outputs to validated course material and verifies each response using textual entailment. According to a study by Taneja et al. (2024), Jill not only delivers more accurate answers than OpenAI’s Assistant but also avoids producing confusing or harmful content at significantly lower rates.

Compared to OpenAI’s Assistant, Jill Watson (ChatGPT) not only achieves higher accuracy but also produces confusing or harmful content at significantly lower rates. Jill Watson answers correctly 78.7% of the time, with only 2.7% of its errors categorized as harmful and 54.0% as confusing. In contrast, OpenAI’s Assistant demonstrates a much lower accuracy of 30.7%, with harmful failures occurring 14.4% of the time and confusing failures rising to 69.2%. Additionally, Jill Watson has a lower retrieval failure rate of 43.2%, compared to 68.3% for the OpenAI Assistant.

What’s Next for Jill

The team plans to expand testing across introductory computing courses at Georgia Tech and technical colleges. They also aim to explore Jill Watson’s potential to improve cognitive presence, particularly critical thinking and concept application. Although quantitative results for cognitive presence are still inconclusive, anecdotal feedback from students has been positive. One OMSCS student wrote:

“The Jill Watson upgrade is a leap forward. With persistent prompting I managed to coax it from explicit knowledge to tacit knowledge. Kudos to the team!”

The researchers also expect Jill to reduce instructional workload by handling routine questions and enabling more focus on complex student needs.

Additionally, AI-ALOE is collaborating with the publishing company John Wiley & Sons, Inc., to develop a Jill Watson virtual teaching assistant for one of their courses, with the instructor and university chosen by Wiley. If successful, this initiative could potentially scale to hundreds or even thousands of classes across the country and around the world, transforming the way students interact with course content and receive support.

A Georgia Tech-Led Collaboration

The Jill Watson project is supported by Georgia Tech, the US National Science Foundation’s AI-ALOE Institute (Grants #2112523 and #2247790), and the Bill & Melinda Gates Foundation.

Core team members are Saptrishi Basu, Jihou Chen, Jake Finnegan, Isaac Lo, JunSoo Park, Ahamad Shapiro and Karan Taneja, under the direction of professor Ashok Goel and Sandeep Kakar. The team works under Beyond Question LLC, an AI-based educational technology startup.

Juba Ziani, assistant professor in Georgia Tech’s H. Milton Stewart School of Industrial and Systems Engineering, has been named the 2025 recipient of the MIF Early Career Award from INFORMS. The purpose of the MIF Early Career Award is to recognize outstanding contributions to the theory or practice of OR/MS and service made by active members of MIF. The award recognizes exceptional researchers who have shown promise at the beginning of their academic or industrial career.

As part of the recognition, Ziani has been invited to present his work in the MIF Early Career Award session at the 2025 INFORMS Annual Meeting in Atlanta. His talk, titled “Towards Inclusive and Human-Centered AI: Research and Service at the Intersection of Algorithms and Society,” will take place on Monday, October 27, 2025.

In his presentation, Ziani will highlight how his research redefines fairness in algorithmic decision-making, treating it not simply as a technical requirement but as a property shaped by broader socio-economic contexts. His work leverages methods from computer science, operations research, and economics to study both immediate and long-term disparities and to evaluate the societal impacts of algorithm-driven systems.

“This award is a recognition not only of my research but also of the importance of building inclusive structures that support the next generation of researchers,” Ziani said.

Beyond research, Ziani has dedicated his career to supporting emerging scholars in the field. He has spearheaded initiatives such as ISyE-MS&E-IOE Rising Stars Workshop, in conjunction with Stanford University Management Science and Engineering and University of Michigan Industrial and Operations Engineering, and has served as Doctoral Consortium Chair for the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) for the past four years.

For more information on 2025 INFORMS Annual Meeting, please visit the INFORMS website.

The Georgia Tech Research Institute (GTRI) is proud to announce the appointment of Kenneth W. Allen, Ph.D., as its new Chief Technology Officer (CTO).
 

Allen, who assumed this position Aug. 15, provides executive leadership for the Office of the CTO (OCTO), including stewardship of GTRI’s independent research and development (IRAD) portfolio with an annual operating budget of nearly $25 million. He will also serve on the GTRI Executive Council (EC) where he will collaborate with other EC members on the development and oversight of GTRI’s technology strategy.
 

“I am thrilled to welcome Ken as GTRI’s new CTO,” said Tommer Ender, Interim Director of GTRI and Senior Vice President for the Georgia Institute of Technology (Georgia Tech). “Ken is a visionary leader with deep technical expertise, an innovative mindset, and a strategic perspective that will support GTRI in fulfilling its core mission to enhance Georgia’s economic development, serve national security, improve the human condition and educate future technology leaders.”  
 

Over his career, Allen has served in a variety of leadership roles within GTRI’s Advanced Concepts Laboratory (ACL) as a Project Director, Chief Engineer for the Electromagnetics Division, Division Chief for the Signals and Systems Division in an interim capacity, and as a Laboratory Chief Scientist. 
 

Read more in the GTRI newsroom

 

There’s good news and bad news in the world of global health research.

The good news is that the gap between what health scientists are studying and the actual worldwide disease burden has narrowed since 1999. That’s according to a new study from a global team of health policy researchers, including Georgia Tech’s Cassidy R. Sugimoto.

The bad news? The study shows the improvement was mostly accidental, and things are likely to start getting worse again — especially if the U.S. follows through on plans to cut global health research funding.

“Our current situation is going to lead to an increasing imbalance between the diseases burdening the world and the research that is produced,” said Sugimoto, the Tom and Marie Patton Chair in the Jimmy and Rosalynn Carter School of Public Policy.

To read more about the paper, published Aug. 27, 2025, in Nature Medicine, read our full story at https://iac.gatech.edu/featured-news/2025/08/global-health-research-gap-study.

Daniel Yue, assistant professor of IT Management at the Scheller College of Business, has been awarded the prestigious Best Dissertation Award by the Technology and Innovation Management Division of the Academy of Management. The recognition celebrates the most impactful doctoral research in the field of business and innovation.

Yue’s dissertation, developed during his Ph.D. at Harvard Business School, explores a paradox at the heart of the AI industry: why do firms openly share their innovations, like scientific knowledge, software, and models, despite the apparent lack of direct financial return? His work sheds light on the strategic and economic mechanisms that drive this openness, offering new frameworks for understanding how firms contribute to and benefit from shared technological progress.

“We typically think of firms as trying to capture value from their innovations,” Yue explained. “But in AI, we see companies freely publishing research and releasing open-source software. My dissertation investigates why this happens and what firms gain from it.”

Read More

Georgia Tech researchers have designed the first benchmark that tests how well existing AI tools can interpret advice from YouTube financial influencers, also known as finfluencers.

Lead author Michael Galarnyk, Ph.D. Machine Learning ’28, joined lead authors Veer Kejriwal, B.S. Computer Science ’25, and Agam Shah, Ph.D. Machine Learning ’26, along with co-authors Yash Bhardwaj, École Polytechnique, M.S. Trustworthy and Responsible AI ‘27; Nicholas Meyer, B.S. Electrical and Computer Engineering ’22 and Quantitative and Computational Finance ’24; Anand Krishnan, Stanford University, B.S. Computer Science ‘27; and, Sudheer Chava, Alton M. Costley Chair and professor of Finance at Georgia Tech.

Aptly named VideoConviction, the multimodal benchmark included hundreds of video clips. Experts labelled each clip with the influencer’s recommendation (buy, sell, or hold) and how strongly the influencer seemed to believe in their advice, based on tone, delivery, and facial expressions. The goal? To see how accurately AI can pick up on both the message and the conviction behind it.

“Our work shows that financial reasoning remains a challenge for even the most advanced models,” said Michael Galarnyk, lead author. “Multimodal inputs bring some improvement, but performance often breaks down on harder tasks that require distinguishing between casual discussion and meaningful analysis. Understanding where these models fail is a first step toward building systems that can reason more reliably in high stakes domains.”

Read More