Lingxin Hao

This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel four-stage reliance taxonomy was introduced to capture students' reliance patterns, distinguishing AI competence, relevance, adoption, and students' final answer correctness. Three findings emerged. First, students exhibited overall low reliance on AI and many of them could not effectively use AI for learning. Second, negative reliance patterns often persisted across interactions, highlighting students’ difficulty in effectively shifting strategies after unsuccessful initial experiences. Third, certain behavioral metrics strongly predicted AI reliance, highlighting potential behavioral mechanisms to explain AI adoption. The study's findings underline critical implications for ethical AI integration in education and the broader field. It emphasizes the need for enhanced onboarding processes to improve student's familiarity and effective use of AI tools. Furthermore, AI interfaces should be designed with reliance-calibration mechanisms to enhance appropriate reliance. Ultimately, this research advances understanding of AI reliance dynamics, providing foundational insights for ethically sound and cognitively enriching AI practices.

This study explores how college students interact with generative AI (ChatGPT-4) during educational quizzes, focusing on reliance and predictors of AI adoption. Conducted at the early stages of ChatGPT implementation, when students had limited familiarity with the tool, this field study analyzed 315 student-AI conversations during a brief, quiz-based scenario across various STEM courses. A novel four-stage reliance taxonomy was introduced to capture students' reliance patterns, distinguishing AI competence, relevance, adoption, and students' final answer correctness. Three findings emerged. First, students exhibited overall low reliance on AI and many of them could not effectively use AI for learning. Second, negative reliance patterns often persisted across interactions, highlighting students' difficulty in effectively shifting strategies after unsuccessful initial experiences. Third, certain behavioral metrics strongly predicted AI reliance, highlighting potential behavioral mechanisms to explain AI adoption. The study's findings underline critical implications for ethical AI integration in education and the broader field. It emphasizes the need for enhanced onboarding processes to improve student's familiarity and effective use of AI tools. Furthermore, AI interfaces should be designed with reliance-calibration mechanisms to enhance appropriate reliance. Ultimately, this research advances understanding of AI reliance dynamics, providing foundational insights for ethically sound and cognitively enriching AI practices.

In network inference applications, it is often desirable to detect community structure. Beyond mere adjacency matrices, many real-world networks also involve vertex covariates that carry key information about underlying block structure in graphs. To assess the effects of such covariates on block recovery, we present a comparative analysis of two model-based spectral algorithms for clustering vertices in stochastic blockmodel graphs with vertex covariates. The first algorithm uses only the adjacency matrix, and directly estimates the block assignments. The second algorithm incorporates both the adjacency matrix and the vertex covariates into the estimation of block assignments, and moreover quantifies the explicit impact of the vertex covariates on the resulting estimate of the block assignments. We employ Chernoff information to analytically compare the algorithms’ performance and derive the information-theoretic Chernoff ratio for certain models of interest. Analytic results and simulations suggest that the second algorithm is often preferred: one can better estimate the induced block assignments by first estimating the effect of vertex covariates. In addition, real data examples also indicate that the second algorithm has the advantage of revealing underlying block structure while considering observed vertex heterogeneity in real applications.

In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity. Beyond mere adjacency matrices, many real networks also involve vertex covariates that carry key information about underlying block structure in graphs. To assess the effects of such covariates on block recovery, we present a comparative analysis of two model-based spectral algorithms for clustering vertices in stochastic blockmodel graphs with vertex covariates. The first algorithm uses only the adjacency matrix, and directly estimates the block assignments. The second algorithm incorporates both the adjacency matrix and the vertex covariates into the estimation of block assignments, and moreover quantifies the explicit impact of the vertex covariates on the resulting estimate of the block assignments. We employ Chernoff information to analytically compare the algorithms' performance and derive the information-theoretic Chernoff ratio for certain models of interest. Analytic results and simulations suggest that the second algorithm is often preferred: we can often better estimate the induced block assignments by first estimating the effect of vertex covariates. In addition, real data examples also indicate that the second algorithm has the advantages of revealing underlying block structure and taking observed vertex heterogeneity into account in real applications. Our findings emphasize the importance of distinguishing between observed and unobserved factors that can affect block structure in graphs.

This article examines the effect of China’s 1999 acceleration of higher education expansion on when college graduates find their first skilled job. We use a natural experiment to test our hypotheses and exploit the unique education and work history data of a nationally representative survey, as well as estimate a causal inference model. We find that the 1999 education expansion caused a delay in the landing of a skilled job among graduates from technical colleges, while graduates from four-year colleges were not affected in job acquisition. We also find that family origins and individual social positions are significant determinants of who entered college both before and after the education expansion. These findings shed new light on the workings of early adulthood and on social inequality in China.

This paper seeks to identify the major sources of unequal cognitive development of middle school students in China’s current rural–urban migration era. Under a framework that integrates theories of social stratification, migration, and sociology of education, we utilize data on a nationally representative sample of 9th graders and a standardized cognitive test to provide valid population patterns and explanations of cognitive development inequality. Our regression decomposition helps disentangle the sources of group disparities between the predictor levels and the predictor effects. Our analysis reveals that the old order of inequality created by household registration (hukou) has been complicated by rural–urban migration. Inequality has increased for rural-hukou students, with the most dramatic differences occurring among children of migrants, depending upon the child’s migrant status. Our regression decomposition analysis results point to the effectiveness of the school learning environment as the chief source of cognitive development inequality. We discuss implications for policy interventions to foster and ensure an equal education for all students.

In this article, we provide a comprehensive examination of the spatial and career mobility of China's labor population. We integrate theories on stratification and social change and exploit the innovative design and measurement of the China Labor-force Dynamics Survey to minimize the undercoverage problem of the rural-urban migratory experience. Our analysis provides several fresh findings: (1) at-birth rural household registration (hukou) status leads to a greater probability of spatial mobility and career advancement than at-birth urban hukou status does; (2) education and gender differentiates rural-origin people, increasing the heterogeneity of urban labor and decreasing the heterogeneity of rural labor; (3) hukou policy relaxation favors later cohorts over earlier cohorts; and (4) among demographically comparable people, having experienced spatial mobility is correlated with having career advancement experience. Work organizations are found to be the arena where the two dimensions of mobility can happen jointly. Our findings provide a rich context for understanding the management and organization of Chinese labor.

Skilled immigration to the US has been multi‐channelled via legislation on permanent and temporary visa programmes. This paper argues that skilled immigrants were not disadvantaged during the Great Recession because of a new hedging mechanism, which starts with the federal legislation that admits skilled non‐immigrants, proceeds to vest authority in employers who perform rigorous screening and selection of temporary workers for future permanency, and ends with greater protection of those selected. To test this mechanism, this paper examines skilled immigrants' spatial mobility out of the country and their domestic labour market outcomes. The paper presents evidence from analysing repeated, nationally representative survey data of college graduates in the US using demographic techniques of intra‐cohort and inter‐cohort analyses. The major findings about the substantial cross‐border mobility and high levels of labour force participation among at‐entry temporary visa holders who later gained permanent residency provide strong evidence to support our proposed new hedging mechanism. Copyright © 2015 John Wiley & Sons, Ltd.