Tengteng Ma

I am an assistant professor in the School of Information Systems, Muma College of Business, University of South Florida. I earned my Ph.D. degree in Management Information Systems from University of Illinois Chicago.

I study how generative AI changes user behavior on digital platforms, and I build interpretable AI models, from deep learning architectures to large language models, that assess whether AI systems are treating users appropriately.

CV  /  Google Scholar

Projects

As more people turn to AI companions like Replika for friendship and even romance, a pressing question is who keeps those conversations safe. We built the first public collection of real companion chats labeled for safety risks and tested whether 20 leading AI models can serve as watchdogs. They catch obvious harm well, but they miss subtle manipulation and often mistake harmless role-play for danger.

Ask a chatbot a question and you might get an answer that sounds right but misses what you actually needed. We design a 'sensible' generative AI system that first figures out what the user is really after, then shapes its response to match, and we show how it works in online education and healthcare.

With endless videos competing for attention, platforms are betting that a quick AI-written summary will help viewers decide what to watch. Studying Bilibili's rollout of these summaries, we find they actually get people talking more, especially on longer videos and on videos people watch to learn something rather than just for fun.

We explore the relationship between passive and active engagement activities when one of the engagement channels is removed. By collecting a large-scale dataset from Youtube, we empirically examine the reinforcement and substitution effects between the two types of engagement behaviors.

We investigate the impact of inserting commercial endorsements in videos on influencers' popularity and reputation from an omni-dimensional perspective. Leveraging face recognition techniques, we further investigate how this effect can be moderated if content generators demonstrate stronger endorsement by showing their face.

We empirically examine how group size affects viewers' real-time engagement on online synchronous platforms and how moderators affect this relationship. The findings indicate a congestion effect of increasing group size viewer engagement in the synchronous communication setting and suggest the beneficial role of content moderators.

For brands on social media, guessing which post will spark a like, comment, or share can feel like trial and error. We design a new artificial intelligence model aims to replace that guesswork with sharper prediction and clearer insight into why people engage.

We systematically evaluate evidence from controlled studies of interventions using virtual humans on their effectiveness in health-related outcomes. The design and implementation characteristics of these systems are also examined.