Mohit Chandra

humanfactors.jmir.org User Perceptions of a Large Language Model–Based Chatbot for Cognitive Reappraisal of Stress: Mixed... Background: Cognitive reappraisal is a widely studied emotion regulation strategy that helps individuals reinterpret stressful situations in ways that reduce their emotional impact. Digital mental...

Ananya Bhattacharjee Verified account @Ananyabha · Oct 23

I am happy to share a new paper in @JMIR_HF, where we explored the feasibility of an LLM-based conversational agent for cognitive reappraisal of stress.

Paper: humanfactors.jmir.org/2026/1/e89736

In this mixed-methods study with 100 employees, we examined a single-session intervention that used an LLM to guide participants through a structured reflection exercise. We observed reductions in self-reported situational stress and improvements in stress mindset following the interaction.

Participants appreciated the structured questions, opportunities to reflect on their situations, and nonjudgmental interaction. Their feedback also revealed some design challenges, including balancing conversational flexibility with intervention structure and mixed reactions to AI-generated empathy.

Grateful to my collaborators Jina Suh, @mohit__30, and @javherriv.

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thamar | Verified account @thamar_solorio · Oct 16, 2025

Super excited and grateful that we received a Google Academic Research award to advance our efforts in Loneliness understanding and even more excited of this new collaboration with @munmun10, @monojitchou, @mohit__30 and @as3eem.
@mbzuai #lonelinessresearch
#NLProc
linkedin.com/posts/mbzuai_g…

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Gaurav Verma @verma22gaurav · Sep 28, 2025

Excited to share our upcoming #AIES2025 paper on how large AI models can be responsibly applied in real-world vertical systems (like in healthcare and education). This also happens to be the the final paper of my PhD journey at Georgia Tech 🎓

Foundation models are powerful, but not plug-and-play solutions that immediately translate to user-facing impact. Our work introduces a framework that shows how to bridge from the models themselves to users' needs. Our work also underscores the healthy dynamism needed across the development and deployment layers: it calls on model developers to incorporate feedback from vertical deployments and it urges vertical-focused practitioners to recognize where their innovations could broaden into vertical-agnostic improvements. Ultimately, this coordinated and interdisciplinary effort will ensure that large AI models effectively deliver on users' needs.

Check out the paper for more details, and the webpage for an interactive illustration of the framework that is grounded in specific examples across different domains.

With an amazing team of co-authors at @GeorgiaTech @GTCSE @ICatGT : @_jiawei_zhou , @mohit__30 , @srijankedia , and @munmun10!

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