Angel Tsai-Hsuan Chung

Angel Tsai-Hsuan Chung, Botong Zhang, Ling-Chieh Kung, Hamsa Bastani, Osbert Bastani, Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning.

Abstract: Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely focus on GenAI chatbot tutors that reactively answer student questions. We hypothesize that the efficacy of GenAI chatbot tutors can be substantially improved by proactively guiding student learning. To test this, we design a novel tutoring platform that tightly integrates a carefully-designed GenAI chatbot with a reinforcement learning algorithm for sequencing practice problems. Critically, this algorithm leverages rich signals from student-chatbot interactions to adaptively select practice problems of an appropriate difficulty level. In partnership with the Taipei City Government and American Institute in Taiwan, we deployed our tutoring platform in conjunction with a five-month course to teach Python to students across ten high schools. We randomized students between a fixed practice problem sequence and our adaptive sequencing algorithm. We find that adaptive sequencing increased unassisted final exam performance by 0.15 standard deviations (equivalent to 6-9 months of schooling by some estimates); mediation analysis suggests that gains were driven by increased engagement. Our work provides large-scale field evidence that student-chatbot interactions provide valuable signals for proactively optimizing and personalizing student learning.

Angel Tsai-Hsuan Chung, Jatu Abdulai, Patrick Bayoh, Lawrence Sandi, Francis Smart, Hamsa Bastani, Osbert Bastani (2026), Improving Access to Essential Medicines via Decision-Aware Machine Learning, Nature (Research Article) . 10.1038/s41586-026-10433-7

Abstract: A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques. We propose a novel decision-aware machine learning framework for essential medicines allocation, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the Sierra Leone national government, we performed a staggered, nationwide deployment of our system as a decision support tool. Our econometric evaluation finds an estimated 19% increase in consumption of allocated products in treated districts, demonstrating its efficacy at improving access to essential medicines. Our tool was subsequently scaled nationwide, covering an estimated 2 million women and children under five. Our work demonstrates how machine learning methods can improve efficiency at very low cost in resource-constrained global health settings.

Hamsa Bastani, Osbert Bastani, Angel Tsai-Hsuan Chung, “Optimizing Health Supply Chains in LMICs with Machine Learning: A Case Study in Sierra Leone”. In Responsible and Sustainable Operations: The New Frontier, edited by Tang, Christopher S. (Switzerland: Springer Nature, 2024), pp. 187-202

Abstract: This chapter overviews the challenges in pharmaceutical supply chains (PSCs) in Low- and Middle-Income Countries (LMICs), with a focus on Sierra Leone. Furthermore, it describes how traditional supply chain optimization strategies can be used to improve performance of PSCs in Sierra Leone. Finally, it describes the significant potential for using machine learning in this framework for effective demand forecasting. We highlight challenges such as limited data availability, the need to ensure equitable distribution, as well as the potential for transfer learning to address some of these challenges.

Angel Tsai-Hsuan Chung, Jimmy Qin, Ping-Cheng Lin, Hamsa Bastani, The Impact of Human-AI Collaboration in Outpatient Care: Evidence from Somaliland.

Abstract: Healthcare documentation is one of the major operational burdens in resource-constrained health systems, often competing directly with patient care. This paper evaluates a human-AI collaborative documentation tool implemented at the largest public hospital in Somaliland. Physicians enter a clinical note draft, from which AI generates a polished clinical note that physicians may directly submit, edit then submit, or abandon AI note and submit their own draft. Exploiting the staggered rollout of this tool across hospital departments, we estimate intent-to-treat (ITT) effects using a Callaway and Sant’Anna (2021) difference-in-differences design. Our results show that AI significantly reduces service time by 34% and reduces service time dispersions across encounters. These findings suggest that AI has the potential to increase effective service capacity and standardize care delivery. We find no evidence that these gains come at the expense of clinical care quality. In particular, provider surveys indicate greater patient interaction and clinical note quality improves substantially. However, AI-enabled faster service do not necessarily improve patient access and downstream operational benefits, for example, patient waiting time does not decrease significantly. We show that this is because physicians strategically respond to the reduced service time by shortening total working time and by starting clinical day later. Furthermore, we find significant improvement on physicians’ independently written notes (without AI) post-treatment, suggesting that improvement does not necessarily require adoption; exposure to AI (but without adoption) can be of great value as well.