AI mental health chatbots, Adolescents’ social media usage and well-being, Meta-analysis methods
AI Mental Health Chatbot
Can we AI everything? An increasing number of people are using AI chatbots as their friends, partners, and therapists. While AI chatbots can beat human counsellors in availability and cost, generative AI technology is a black-box with many cases of failed safety guardrails. This line of work investigates the efficiency of AI mental health chatbots while exploring the ethics behind the design of these tools.
Representative papers:
Zhang, Q., Zhang, R., Xiong, Y., Sui, Y., Tong, C., & Lin, F. (2025). Generative AI mental health chatbots as therapeutic tools: A systematic review and meta-analysis of their role in minimizing mental health issues. Journal of Medical Internet Research.
Students’ Social Media Usage and Well-being
Social media’s growing popularity among teenagers has sparked considerable concerns among teachers and parents. They see it as potentially harmful to children’s mental health, a perspective that highlights the complexity of its impact. This tension motivated me to navigate the mechanism behind students’ social media usage and their well-being. I am eager to answer the question: How can we help adolescents harness benefits from social media usage? Therefore, I focus my dissertation research on thoroughly investigating the psychological impacts of social media usage.
Social media is now a common feature in adolescents’ lives, and its pros and cons are frequently discussed. However, existing research on young people’ use of social media tends to focus on the associated risks. As a result, experts often limit their recommendations to simply cutting back on social media usage, rather than exploring more innovative solutions. My research agenda aims to uncover the underlying principles and themes that make social media effective in promoting students’ social support and psychological well-being. The central question I am dedicating my career to is: Is social media good or bad for adolescents? How can we navigate adolescents to reap the benefits and minimize risks while using these platforms?
Working papers (dissertation papers):
Zhang, Q., Wang, X., Tian, XR., & Gehlbach, H. (2024). Social support at your fingertips: Exploring the correlation between students’ social media usage and social support through meta-analysis. OSF Preprint.
Zhang, Q., Huang, Z., Sui, Y., Lin, F., Guan, H., Li, L., Wang, K., & Amanda J. Neitzel (2025). Social-media-based mental health interventions: A meta-analysis of randomized controlled trials. Journal of Medical Internet Research.
Zhang, Q., & Gehlbach, H. (2024).Stepping into adolescents’ digital shoes: An intervention for parental understanding of children’s privacy needs in social media monitoring.
Automated Systematic Review and Meta-analysis Method Development
Systematic reviews and meta-analyses are vital instruments for researchers to understand broad trends in a field and synthesize evidence on the effectiveness of interventions in addressing specific issues. The quality of a systematic review depends critically on having comprehensively surveyed all relevant literature on the review topic as well as using the most advanced and reliable tools to reduce human errors.
My line of work in meta-analyses focuses on methodological development and giving concrete information to assist systematic reviewers using the most suitable instead of the most convenient tools. The development of Paperfetcher has also reformed the current literature searching practices. This line of projects will continue contributing to helping us understand the differences between different AI-enhanced searching, screening, and coding tools.
Representative papers:
Zhang, Q., & Pallath, A. (2022) Paperfetcher: A tool to automate handsearching and citation searching in systematic reviews. Research Synthesis Methods, 14(2), 323-335.
Zhang, Q., & Neitzel, A. J. (2023). Choosing the right tool for the job: Screening tools for systematic reviews in education. Journal of Research on Educational Effectiveness.
Qiyang Zhang, Ph.D. 