Research

Research Interests

AI-assisted/AI-enhanced/AI-powered research methods
Applications of AI (including GenAI and LLMs), machine learning, and data-adaptive methods in social and behavioral sciences
Psychometrics(including measurement), multilevel and latent variable models
Missing data and Bayesian data analysis

Publications & Preprints

Methods, AI, Psychometrics, and Research Software

Luo, N., & Ji, F. (2025). Generative adversarial networks for high-dimensional item factor analysis: A deep adversarial learning algorithm. Psychometrika, 90(5), 1765–1788. https://doi.org/10.1017/psy.2025.10059

Ji, F., Lee, J., & Rabe-Hesketh, S. (2025). Valid standard errors for Bayesian quantile regression with clustered and independent data. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986251379738

Han, Y., Wang, P., Ji, F., & Liu, H. (2026). A sequential response model with growth parameters for process data in complex problem solving. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986251403007

Luo, N., Han, Y., He, J., Zhang, X., & Ji, F. (2026). Fitting Bayesian item response theory models using deep learning computational frameworks. Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986261439301

He, X., Zhang, X., Wang, C., & Ji, F. (2026). Clarifying the conceptual landscape in AI literacy measurement: A large language model–based approach. PsyArXiv preprint. https://doi.org/10.31234/osf.io/hz4ak_v2

Zhou, B., Luo, N., & Ji, F. (2025). Federated item response models: A gradient-driven privacy-preserving framework for distributed psychometric estimation. arXiv:2506.21744. https://doi.org/10.48550/arXiv.2506.21744

Zhang, Y., Qi, J., He, X., Feng, Z., & Ji, F. (2026). The Generative Artificial Intelligence Literacy Scale (GAILS): Development, validation, and measurement invariance across sex and occupational status groups. PsyArXiv preprint. https://doi.org/10.31234/osf.io/bg6pq_v1

He, X., Wang, C., & Ji, F. (2026). A generator-aligner pipeline for LLM-based situational judgment test generation. In Artificial Intelligence in Education (pp. 361–369). Springer. https://doi.org/10.1007/978-3-032-29760-0_40

Yang, X., Zhang, X., Hu, H., & Ji, F. (2026). Beyond accuracy: Evaluating strategy diversity in LLM mathematical reasoning. arXiv:2605.09292. https://doi.org/10.48550/arXiv.2605.09292

Xu, W., & Ji, F. (2026). Reinforcement learning measurement model. arXiv:2605.09305. https://doi.org/10.48550/arXiv.2605.09305

Li, Z., & Ji, F. (2026). Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments. arXiv:2604.02458. https://doi.org/10.48550/arXiv.2604.02458

Wang, J., Zhang, Y., Borhi, M., Wei, W., Li, H., Hémono, R., Giorgio, J., Su, J., Ashe, K., Zheng, Z., Deardorff, J., Ji, F., & Zhang, X. (2026). Where do adolescent digital twins succeed and fail? A multi-layer validation of survey-anchored generative agents with an LLM backbone. Research Square preprint. https://doi.org/10.21203/rs.3.rs-8823969/v1

Zhang, Y., Luo, N., Kim, H., Li, L., Gao, L., Han, J., Chen, S., Zhang, X., He, J., & Ji, F. (2026). Large language models (LLMs) for evidence synthesis: An exploratory evaluation and a new approach for automated data extraction and validation. PsyArXiv preprint. https://doi.org/10.31234/osf.io/udysp_v2

Luo, N., & Ji, F. (2026). Robust standard errors for Bayesian posterior functionals via the infinitesimal jackknife. arXiv:2604.03398. https://doi.org/10.48550/arXiv.2604.03398

Luo, N., & Ji, F. (2026). avbirt: A Python package for flexible item factor analysis with adversarial variational Bayes. Applied Psychological Measurement. https://doi.org/10.1177/01466216261471170

Yuan, Y., Zhou, B., Qi, J., Luo, N., & Ji, F. (2026). BKT: A Bayesian knowledge tracing package for the R environment. Behavior Research Methods, 58(5), 134. https://doi.org/10.3758/s13428-026-02955-9

Han, Y., Ji, F., Chen, Y., Gan, K., & Liu, H. (2026). Analyzing group differences and measurement fairness in process data: A sequential response model with covariates. Methodology, 22(1), 1–26. https://doi.org/10.5964/meth.16999

Han, Y., Luo, X., Ji, F., & Kong, X. (2026). Multi-agent LLMs for occupational profiling: Psychometric validation on 1,636 Chinese occupations. Behavioral Sciences, 16(7), 1064. https://doi.org/10.3390/bs16071064

Xiao, Y., Zhao, S., Zhou, Q., Zhang, Y., Han, J., Ji, F., & He, J. (2026). Can ChatGPT rate body-related images like humans? Effects of temperature and few-shot prompting on valence and arousal judgments. Body Image, 57, 102097. https://doi.org/10.1016/j.bodyim.2026.102097

Ji, F., Zhang, Y., Luo, N., Yan, T., Barnhart, W. R., Cui, S., Zhang, J., Gui, C., Zhou, J., Nagata, J. M., & He, J. (2026). Predicting probable eating disorders in Chinese adolescents using longitudinal data: A comparison between traditional machine learning and modern deep learning approaches. PsyArXiv preprint. https://doi.org/10.31234/osf.io/hrp37_v1

Ma, J., Zhang, X., He, G., Han, Y., Ge, T., & Ji, F. (2025). From structural equation modeling to targeted learning: A tutorial introduction to targeted maximum likelihood estimation for SEM researchers. arXiv:2511.01040. https://doi.org/10.48550/arXiv.2511.01040

Zhou, B., Yuan, Y., Luo, N., Qi, J., & Ji, F. (2025). FedIRT: An R package and Shiny app for estimating federated item response theory models. Journal of Open Source Software, 10(113), 7869. https://doi.org/10.21105/joss.07869

Han, Y., Ji, F., & Jiang, Z. (2025). Two-stage polytomous attribute estimation for cognitive diagnostic models: Overcoming computational challenges in large-scale assessments with many polytomous attributes. Humanities and Social Sciences Communications, 12(1), 716. https://doi.org/10.1057/s41599-025-04959-w

Han, Y., Ji, F., Wang, P., & Liu, H. (2025). Assessing multiple abilities through process data in computer-based assessments: The multidimensional sequential response model (MSRM). Behavior Research Methods, 57(5), 152. https://doi.org/10.3758/s13428-025-02658-7

Wang, S., Barnhart, W. R., Li, Y., Gaggiano, C. M., Jiang, Z., Wu, S., Nagata, J. M., Ji, F., & He, J. (2025). Validation of the Muscularity Bias Internalization Scale in Chinese transgender and gender-diverse adults. Body Image, 52, 101857. https://doi.org/10.1016/j.bodyim.2025.101857

Zhang, J., Cui, S., Xu, Y., Cui, T., Barnhart, W. R., Ji, F., Nagata, J. M., & He, J. (2025). Introducing diagnostic classification modeling as an unsupervised method for screening probable eating disorders. Assessment, 32(3), 405–416. https://doi.org/10.1177/10731911241247483

Yin, P., Ji, F., & Blum, A. M. (2025). Exploring Chinese international students’ attitudes toward anti-China rhetoric: Development and initial validation of an item response theory-based measure. Journal of Diversity in Higher Education, 18(3), 240–252. https://doi.org/10.1037/dhe0000513

Kim, H., Qi, J., Feng, Z., Zhang, X., Han, Y., He, J., & Ji, F. (accepted). Can large language models (LLMs) be trusted for power analysis? An empirical evaluation. Journal of Behavioral Data Science. https://doi.org/10.31234/osf.io/32mkv

Yan, T., Zhang, Y., Han, J., Liu, Z., Barnhart, W. R., Sun, S., Zhou, J., Ji, F., & He, J. (2024). Evaluating the performance of GPT-assisted identification and classification of eating disorders with text-based Chinese social media data. JMIR Preprints. https://doi.org/10.2196/preprints.58054

Lai, M. H. C., Zhang, Y., & Ji, F. (2024). Estimating between-cluster effects in multilevel modeling: An empirical Bayes cluster means approach with finite population corrections. Multivariate Behavioral Research, 59(3), 584–598. https://doi.org/10.1080/00273171.2024.2307034

Ji, F., Sun, H., Barnhart, W. R., Cui, T., Cui, S., Zhang, J., & He, J. (2024). Psychometric network analysis of the Intuitive Eating Scale-2 in Chinese general adults. Journal of Clinical Psychology, 80(5), 1098–1114. https://doi.org/10.1002/jclp.23657

Ernst, A. F., Timmerman, M. E., Ji, F., Jeronimus, B. F., & Albers, C. J. (2024). Multilevel mixture vector-autoregressive modeling. Psychological Methods, 29(1), 137–154. https://doi.org/10.1037/met0000551

Chen, Y., Wang, S., Barnhart, W. R., Song, J., Cui, S., Ji, F., & He, J. (2024). Translation and validation of a Chinese version of the Appearance Schemas Inventory-Revised in Chinese adults. Body Image, 48, 101671. https://doi.org/10.1016/j.bodyim.2023.101671

Ji, F., Rabe-Hesketh, S., & Skrondal, A. (2023). Diagnosing and handling common violations of missing at random (MAR). Psychometrika, 88(4), 1123–1143. https://doi.org/10.1007/s11336-022-09896-0

Schneider, W. J., & Ji, F. (2023). Detecting unusual score patterns in the context of relevant predictors. Journal of Pediatric Neuropsychology, 9(1), 1–17. https://doi.org/10.1007/s40817-022-00137-x

Han, Y., Liu, H., & Ji, F. (2022). A sequential response model for analyzing process data on technology-based problem-solving tasks. Multivariate Behavioral Research, 57(6), 960–977. https://doi.org/10.1080/00273171.2021.1932403

Ji, F., Xiao, X., Amanmyradova, A., & Rabe-Hesketh, S. (2022). Bayesian structural equation modeling using blavaan. Stan Case Studies, Volume 10. https://mc-stan.org/learn-stan/case-studies/sem.html

Ji, F., Amanmyradova, A., & Rabe-Hesketh, S. (2021). Bayesian latent class models and handling of label-switching. Stan Case Studies, Volume 8. https://mc-stan.org/learn-stan/case-studies/Latent_class_case_study.html

He, J., Ji, F., Zhang, X., & Fan, X. (2019). Psychometric properties and gender invariance of the simplified Chinese version of Night Eating Questionnaire in a large sample of mainland Chinese college students. Eating and Weight Disorders—Studies on Anorexia, Bulimia and Obesity, 24(1), 57–66. https://doi.org/10.1007/s40519-018-0553-7

Lee, J., Sim, N., Ji, F., & Rabe-Hesketh, S. (2018). Multilevel linear models using rstanarm. Stan Case Studies, Volume 5. https://mc-stan.org/learn-stan/case-studies/tutorial_rstanarm.html

Luo, N., & Ji, F. (2026). avbirt: Importance-weighted adversarial variational Bayes for item factor analysis (Version 0.2.0). Python package. https://pypi.org/project/avbirt/

Ji, F., & Zhou, B. (2025). missalpha: Find range of Cronbach alpha with a dataset including missing data (Version 0.2.0). R package. https://doi.org/10.32614/CRAN.package.missalpha

Yuan, Y., Zhou, B., & Ji, F. (2025). BKT: Bayesian knowledge tracing in R (Version 0.1.0). R package. https://doi.org/10.32614/CRAN.package.BKT

Ji, F., Lee, J., & Rabe-Hesketh, S. (2025). IJSE: Infinitesimal jackknife standard errors for “brms” models (Version 0.1.2). R package. https://doi.org/10.32614/CRAN.package.IJSE

Zhou, B., & Ji, F. (2025). SEMsensitivity: SEM sensitivity analysis (Version 0.1.0). R package. https://doi.org/10.32614/CRAN.package.SEMsensitivity

Zhou, B., & Ji, F. (2024). FedIRT: Federated item response theory models (Version 1.1.0). R package. https://doi.org/10.32614/CRAN.package.FedIRT

Schneider, W. J., & Ji, F. (2024). unusualprofile: A method to interpret individual results of psychological test batteries using conditional Mahalanobis distance (Version 0.1.4). R package. https://doi.org/10.32614/CRAN.package.unusualprofile

Ji, F. (2020). Shiny apps for visualizing multilevel models: Intraclass correlation; random intercept models; random slope models; empirical Bayes prediction; level-2 endogeneity: RE versus FE estimators.

Ji, F. (2020). gIVCV: General instrumental variables with cross-validation for massive A/B test experiments (Version 0.1.0). R package. https://github.com/googleinterns/causal-analysis-iv/tree/master/gIVCV

Applied Research

Li, Z., Liu, Y., Wang, C., Tong, S., Peng, K., & Ji, F. (2026). LLM-generated personalized nudges for improving pro-environmental behavior: Field evidence from resource conservation. arXiv:2604.03881. https://doi.org/10.48550/arXiv.2604.03881

Wang, S., Wang, P., Barnhart, W. R., Sahlan, R. N., Nagata, J. M., Ji, F., & He, J. (2026). Adverse childhood experiences and body appreciation: The buffering role of posttraumatic growth. Body Image, 58, 102149. https://doi.org/10.1016/j.bodyim.2026.102149

Cheng, Y., Li, Y., Barnhart, W. R., Wang, P., Sahlan, R. N., Wu, S., Jiang, Z., Nagata, J. M., Ji, F., & He, J. (2026). Testing the tripartite influence model among Chinese transgender and gender-diverse adults by gender identity. Body Image, 58, 102106. https://doi.org/10.1016/j.bodyim.2026.102106

Gao, X., Zhang, J., Chen, Y., Wang, P., Barnhart, W. R., Yang, C., Ji, F., Yim, S. H., & He, J. (2026). Associations of negative, positive, and mixed body talk with self-objectification, negative affect, body appreciation, and disordered eating in Chinese young women: An ecological momentary assessment study. Body Image, 56, 102047. https://doi.org/10.1016/j.bodyim.2026.102047

Wang-Kildegaard, B., Irey, R., Ji, F., Gorno Tempini, M., & Cunningham, A. (2026). Differential impacts of morphology-focused and phonology-focused interventions on students with dyslexia: Evidence from latent regression analysis. Reading Psychology, 47(5), 743–768. https://doi.org/10.1080/02702711.2026.2614436

Wei, W., Shao, J., Lyu, R. Q., Hémono, R., Ma, X., Giorgio, J., Zheng, Z., Ji, F., Zhang, X., Katabaro, E., Mlowe, M., Sabasaba, A., Lister, C., Shabani, S., Njau, P., McCoy, S. I., & Wang, J. (2026). Enhanced language models for predicting and understanding HIV care disengagement: A case study in Tanzania. npj Digital Medicine, 9(1), 165. https://doi.org/10.1038/s41746-026-02349-3

Yang, S., Zhao, S., Barnhart, W. R., Wang, P., Cheng, Y., Tie, B., Ji, F., & He, J. (2026). Male self-reported eating disorders on Chinese social media: A computational analysis of Xiaohongshu posts and comments. Research Square preprint. https://doi.org/10.21203/rs.3.rs-9156420/v1

He, G., Ge, T., Ji, F., & Wu, C. (2026). From passive to active welfare: Poverty alleviation and perceived governance in rural China. International Journal of Social Welfare, 35(1), e70059. https://doi.org/10.1111/ijsw.70059

Qi, J., Zhao, H., Xiang, R., Zhang, Y., Zhang, X., Wang, C., Liu, J., & Ji, F. (2026). Understanding the interaction between generative AI and factual knowledge in supporting students’ problem-solving. Journal of Research on Technology in Education, 1–23. https://doi.org/10.1080/15391523.2025.2601067

Ding, J., Fan, Z., Liu, H., Slotta, J., Ji, F., & Hu, K. (2026). Understanding collaborative programming dynamics: The role of prior knowledge, engagement, and ICAP learning modes. International Journal of Computer-Supported Collaborative Learning, 21(1), 7–45. https://doi.org/10.1007/s11412-025-09457-4

Xiao, Y., Wang, P., Barnhart, W. R., Chen, C., Ji, F., Nagata, J. M., & He, J. (2026). Exploring prospective relationships of emotion regulation difficulties with eating disorder psychopathology and eating-related psychosocial impairment among older adults in China. European Eating Disorders Review, 34(1), 240–255. https://doi.org/10.1002/erv.70026

Wang, C., Wang, X., Sun, H., Chen, J., & Ji, F. (2026). Mind and mood in harmony: Synergizing cognitive efficiency and emotional engagement across different modalities of online learning resources. Interactive Learning Environments, 34(2), 734–757. https://doi.org/10.1080/10494820.2025.2508916

Cheng, Y., Chen, Y., Barnhart, W. R., Chen, C., Yim, S. H., Nagata, J. M., Ji, F., & He, J. (2025). Improving body image in female Chinese social media users with eating disorder symptoms: A randomized controlled trial of two online self-guided single-session interventions. Journal of Eating Disorders, 14(1), 37. https://doi.org/10.1186/s40337-025-01497-3

Zhang, Y., Luo, N., Zhang, X., Ji, F., & He, J. (2025). Identifying and characterizing eating disorder discourse on Chinese social media: A machine learning approach. Research Square preprint. https://doi.org/10.21203/rs.3.rs-7852043/v1

Gaggiano, C. M., Wang, S., Barnhart, W. R., Ji, F., & He, J. (2025). Explaining the associations between adverse childhood experiences and body dissatisfaction through the lens of appearance stigma. Body Image, 55, 101993. https://doi.org/10.1016/j.bodyim.2025.101993

He, J., & Ji, F. (2025). Artificial intelligence and social media for the detection of eating disorders. International Journal of Eating Disorders, 58(7), 1187–1190. https://doi.org/10.1002/eat.24438

Kong, T., Yang, Y., Ji, F., Liu, J., Liu, R., & Luo, L. (2025). Combined effects of prenatal ozone exposure and school/neighborhood environments on youth brain, cognition, and psychotic-like experiences. Journal of Child Psychology and Psychiatry, 66(10), 1551–1562. https://doi.org/10.1111/jcpp.14167

Pan, Z., Chen, Y., Barnhart, W. R., Cui, S., Chen, G., Ji, F., Nagata, J. M., Yim, S. H., & He, J. (2025). How self-compassion moderates the associations of body image with eating disorder psychopathology, eating-related psychosocial impairment, and psychological distress: A longitudinal study in Chinese adolescents. Body Image, 55, 101989. https://doi.org/10.1016/j.bodyim.2025.101989

He, J., Wang, Z., Chen, X., Barnhart, W. R., Pan, Z., Cui, S., Yim, S. H., Zhang, J., Chen, G., & Ji, F. (2025). Tripartite influence and social comparison theories for explaining eating disorder psychopathology in Chinese boys and girls: A longitudinal network perspective. Body Image, 54, 101952. https://doi.org/10.1016/j.bodyim.2025.101952

Ge, T., Lu, X., He, G., Ren, Y., & Ji, F. (2025). Job demands-control, job support, and depressive symptoms: Unraveling job support’s moderating mechanism among social workers in China. International Journal of Social Welfare, 34(1), e12714. https://doi.org/10.1111/ijsw.12714

Wang, Z., Cui, T., Barnhart, W. R., Ji, F., Nagata, J. M., & He, J. (2025). Early-life bullying victimization and perpetration and current disordered eating in Chinese men: An integrated theoretical model. Psychology of Men & Masculinities, 26(1), 150–167. https://doi.org/10.1037/men0000496

Yang, Y., Kong, T., Ji, F., Liu, R., & Luo, L. (2024). Associations among environmental unpredictability, changes in resting-state functional connectivity, and adolescent psychopathology in the ABCD study. Psychological Medicine, 54(15), 4119–4128. https://doi.org/10.1017/S0033291724001855

He, J., Zhang, Y., Liu, Z., Barnhart, W. R., Cui, S., Chen, S., Fu, Y., Ji, F., Nagata, J. M., & Sun, S. (2024). Exploring the self-perceived causes of eating disorders among Chinese social media users with self-reported eating disorders. Journal of Eating Disorders, 12(1), 201. https://doi.org/10.1186/s40337-024-01159-w

Zhang, J., Cui, S., Zickgraf, H. F., Barnhart, W. R., Xu, Y., Wang, Z., Ji, F., Chen, G., & He, J. (2024). A longitudinal network analysis of emotion regulation, interpersonal problems, and eating disorder psychopathology in Chinese adolescents. International Journal of Eating Disorders, 57(12), 2415–2426. https://doi.org/10.1002/eat.24292

Xu, Y., Song, J., Ren, Y., Barnhart, W. R., Dixit, U., Ji, F., Chen, C., & He, J. (2024). Negative emotional eating patterns in general Chinese adults: A replication and expansion study examining group differences in eating disorder symptomatology, psychosocial impairment, and emotion regulation difficulties. Eating Behaviors, 54, 101899. https://doi.org/10.1016/j.eatbeh.2024.101899

Barnhart, W. R., Cui, T., Cui, S., Sun, H., Xu, Y., Chen, G., Ji, F., & He, J. (2024). Exploring the reciprocal relationships between body image flexibility and body fat and muscularity dissatisfaction: An 18-month longitudinal study in Chinese adolescents. Body Image, 51, 101789. https://doi.org/10.1016/j.bodyim.2024.101789

Huang, Z., Wang, S., Lin, Y., Cui, T., Barnhart, W. R., Gaggiano, C. M., Ji, F., & He, J. (2024). The gratitude model of body appreciation and intuitive eating: Replication and extension of the model to explain intuitive eating facets among young adult women in China. Appetite, 203, 107672. https://doi.org/10.1016/j.appet.2024.107672

Hilbert, M., Thakur, A., Flores, P. M., Zhang, X., Bhan, J. Y., Bernhard, P., & Ji, F. (2024). 8–10% of algorithmic recommendations are “bad,” but… an exploratory risk-utility meta-analysis and its regulatory implications. International Journal of Information Management, 75, 102743. https://doi.org/10.1016/j.ijinfomgt.2023.102743

He, J., Wang, Z., Fu, Y., Wang, Y., Yi, S., Ji, F., & Nagata, J. M. (2024). Associations between screen use while eating and eating disorder symptomatology: Exploring the roles of mindfulness and intuitive eating. Appetite, 197, 107320. https://doi.org/10.1016/j.appet.2024.107320

Wang-Kildegaard, B., & Ji, F. (2024). Context synthesis accelerates vocabulary learning through reading: The implication of distributional semantic theory on second language vocabulary research. Applied Linguistics, 45(2), 287–307. https://doi.org/10.1093/applin/amad014

Barnhart, W. R., Cui, T., Cui, S., Ren, Y., Ji, F., & He, J. (2023). Exploring the prospective relationships between food addiction symptoms, weight bias internalization, and psychological distress in Chinese adolescents. International Journal of Eating Disorders, 56(12), 2304–2314. https://doi.org/10.1002/eat.24066

Liu, R., Phillips, J. J., Ji, F., Shi, D., & Bell, M. A. (2021). Temperamental shyness and anger/frustration in childhood: Normative development, individual differences, and the impacts of maternal intrusiveness and frontal electroencephalogram asymmetry. Child Development, 92(6), 2529–2545. https://doi.org/10.1111/cdev.13621