In addition to the most pressing issues of the day, scholars at the Freeman Spogli Institute for International Studies focus their research on many regions of the world, from Beijing to Brazil.
Research Spotlight
The Ripple Effects of China’s College Expansion on American Universities
Researchers at SCCEI trace how China’s unprecedented expansion of higher education has impacted U.S. graduate education and local economies surrounding college towns.
While Nayib Bukele's style of authoritarianism may have some successes on paper, Beatriz Magaloni and Alberto Diaz-Cayeros argue that the regime is headed for a reckoning.
Time for Iran to Make a No-enrichment Nuclear Deal
The time has come for Iran’s leaders to reconsider their past intransigent, deceptive posture and instead pursue a nuclear power program that will benefit the Iranian people, write Abbas Milani and Siegfried Hecker.
The emergence of artificial intelligence (AI) has heightened interest in personalizing computer assisted learning (CAL) programs to tailor their instruction to individual students. Despite the proliferation of AI-driven CAL programs, evidence for their effectiveness remains limited. We present findings from a field experiment in rural China examining whether an AI-driven personalized CAL program improves student achievement. We randomly assign 8647 students from 315 primary school classes to one of three treatment arms: (i) AI-CAL, (ii) non-personalized CAL (active control), and (iii) non-CAL educational activities (pure control). Results indicate that AI-CAL does not significantly improve achievement, with estimates precise enough to rule out non-trivial positive effects. This finding holds across the difficulty of assessment items and across the baseline achievement of students. If anything, AI-CAL might be less effective than non-personalized CAL for students in the middle tercile of baseline achievement. These findings call for caution when scaling AI-driven educational technologies.
International Journal for Equity in Health,
October 14, 2026
Background: Early motherhood is a critical yet vulnerable life stage shaped by intersecting individual, familial, societal and structural challenges. Existing research often overlooks the multiple everyday stressors mothers experience across ecological domains. This study explores the lived experiences of mothers in urban China, using an ecological framework to examine the multi-level challenges they face during the first three years after childbirth.
Methods: In-depth interviews were conducted with 50 mothers of children aged 0–3 years recruited from nine community and township health centers and one childcare program for a migrant community in Shanghai, China, during well-child visits. Interviews were guided by an ecological framework and analyzed using thematic analysis.
Results: Challenges clustered across ecological levels, including postpartum physical health problems, caregiving burden, limited family and social support, work–childcare tension and constrained services, and information and financial constraints. These challenges manifested as common themes that negatively affected maternal wellbeing and the child nurturing environment.
Conclusions: These findings highlight that the challenges mothers experience extend beyond the individual level to family and institutional contexts, underscoring the need for coordinated interventions from individual support to family- and system-level services.
This study examines whether and how caregiver mental health symptoms influence the impacts of a center-based parenting intervention on child development.
METHODS
We conducted a clustered randomized controlled trial across 100 rural villages in China between 2016 and 2019. Villages were randomly assigned to a treatment group that received weekly 1-on-1 parenting sessions through parenting centers or to a pure comparison group. A total of 2554 caregiver–child dyads with children aged 6 to 24 months were surveyed at baseline, and 2044 completed the endline survey after 1 year (follow-up rate: 80.0%). Child development was assessed using the Bayley Scales of Infant Development, Third Edition. Caregiver mental health was measured using the Depression Anxiety and Stress Scale-21. Ordinary least squares regressions with interaction terms were used for analysis.
RESULTS
The intervention significantly improved cognitive (β = 0.17 SD; 95% CI 0.045–0.29) and language development (β = 0.10 SD; 95% CI 0.00–0.21) among children whose caregivers did not exhibit depressive symptoms at baseline. Positive cognitive effects were also observed among children of caregivers with low anxiety (β = 0.13 SD; 95% CI 0.00–0.26) or low stress (β = 0.15 SD; 95% CI 0.03–0.27). In contrast, no significant effects were detected across developmental domains among children whose caregivers exhibited mental health symptoms. Heterogeneous impacts were driven by differences in stimulating parenting practices rather than program attendance or parenting beliefs.
CONCLUSIONS The intervention primarily benefited children whose caregivers had no baseline mental health symptoms, suggesting caregiver mental health moderates intervention effects.
Generative artificial intelligence (AI) systems now summarize health-related search results, answer medical questions and offer guidance people once sought from clinicians. These systems bring real benefits, including plain-language explanations of medical information, around-the-clock availability and expanded access for people facing language or literacy barriers. They also carry new risks: inaccurate guidance can harm people at scale, and malicious actors can now generate personalized health misinformation at negligible cost. In this Perspective, we argue that these risks are largely invisible to the institutions responsible for protecting public health. When AI guidance causes harm, no record exists outside the platform, no channel allows users to report it and no independent researcher can measure the consequences. We trace these invisible risks across two settings: incidental exposure online and active seeking through search engines and chatbots. Minimizing harm from AI-generated health information requires making it observable. We therefore offer recommendations that aim to improve transparency, mitigate harm at the point of delivery and assign accountability, ranging from voluntary platform measures to regulatory ones.
Maternal mental health issues are pervasive in rural China, yet community-based interventions are scarce. To address this gap, we developed the Thinking Healthy Extended Program, a nonspecialist-led intervention designed to promote mental health among female caregivers of children under the age of three, including mothers and grandmothers, in resource-constrained areas of China. Drawing on an adapted Stages of Implementation Science framework, this study describes the Exploration and Installation phases of a four-phase intervention development process. During the Exploration phase, we assessed caregivers’ mental health needs and preferences for intervention delivery through in-depth interviews. The Installation phase comprised three stages: developing the overall curriculum structure, identifying and validating curriculum topics, and integrating these topics into a comprehensive curriculum based on feedback from interviews, focus group discussions, and cognitive walkthroughs. The development process took place from 2022 to 2024 in a prefecture in southwestern China and involved 73 caregivers of children aged 6-36 months. The resulting 24-session curriculum integrates simplified cognitive reframing techniques, behavioral activation, caregiving skills, and peer support. This study demonstrates the feasibility of a systematic and participatory approach to developing a mental health intervention for low-resource populations.
Chapter in "De Gruyter Handbook of Cities, Infrastructure and International Relations," edited by Michele Acuto, Simon Curtis, Ian Klaus, and Gaea Morales
British Journal of Political Science,
September 22, 2026
Theories of democratic diffusion predict that exposure to liberal democracies fosters support for political liberalism, while recent studies on Chinese overseas students suggest studying abroad generates backlash against liberal values. Using a unique four-year longitudinal dataset of nearly 1,000 Chinese college students in China and the United States, we examine how studying abroad shapes students’ ideology, attitudes, and identity. Our findings challenge both perspectives. Students who study in the United States are already more politically liberal, market-oriented, and cosmopolitan, and less nationalistic than their peers upon departure, underscoring the importance of selection. Ideological preferences remain remarkably stable throughout college, regardless of location. However, support for China’s political system and affective identification with Chinese national identity declines among students in both countries from 2022 to 2023, with a greater decline among students in the United States. These results show that selection, home-country shocks, and host-country context jointly shape overseas students’ political attitudes.
A new report from Stanford's Tech Impact and Policy Center is offering one of the first cross-sector attempts to make sense of a technology moving faster than the research meant to understand it: AI companions.
Proceedings of Machine Learning Research,
September 2, 2026
By proposing a correspondence between prominent algorithmic harm and social drivers of health (SDOH) frameworks, we show that a range of algorithmic harms ultimately impact human health through SDOH factors, especially structural factors.
