Benhur Ravuri

I am currently working as a Teaching Faculty II at the University of Wisconsin-Milwaukee. I graduated with my Ph.D. in Information at Florida State University, where I was advised by Dr. Marcia A. Mardis, who leads the FSU Information Institute.

I graduated from Karunya Institute of Technology (India) with a Bachelor's degree in Computer Science in 2018 and earned my Master's degree in Information Technology from Florida State University in 2020.

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Research

I am interested in research areas related to Generative Artificial Intelligence (GenAI), AI-powered chatbots, Information-seeking Behavior, Human-Computer Interaction (HCI), and AI in Education (AIED). My overarching goal is to advance the field by enhancing human-computer interactions and improving learning experiences through innovative AI applications.

Testing ChatGPT-4o-mini and Gemini 1.5 Flash on 84 authentic library reference questions, this study found that question complexity (READ level) was strongly linked to whether the chatbots pointed users toward further assistance. As complexity rose, ChatGPT supplied more resources but still gave incomplete answers, while Gemini’s completeness tracked question type, making it the more consistent of the two; the authors recommend that future work compare newer LLMs against human responses.

This study explores how librarians' use of clarifying questions to guide patrons parallels chatbot-user interactions, suggesting that applying Kuhlthau’s Information Search Process (ISP) can help improve AI-driven chatbots by enhancing prompt engineering, refining responses, and balancing human mediation in information-seeking.

A systematic review of 11 recent meta-analyses (774 articles) finds that AI adoption in university teaching is driven by personalized learning at scale, better formative feedback, and instructional efficiency, but held back by concerns about academic integrity, bias, inaccurate outputs, and data privacy. It also identifies a shift in the faculty role from content expert to “AI mediator,” guiding students toward critical and ethical AI use

This study explores how librarians' use of clarifying questions to guide patrons parallels chatbot-user interactions, suggesting that applying Kuhlthau’s Information Search Process (ISP) can help improve AI-driven chatbots by enhancing prompt engineering, refining responses, and balancing human mediation in information-seeking.

This study analyzed 247 Reddit posts on ChatGPT in education. Most users were neutral, but positive views outweighed negative ones. While ChatGPT helps students, it can also be biased or incorrect. Understanding user perceptions is important.

This study examines key ethical principles in AI for healthcare through thematic analysis. Using survey data from 36 AI experts (Pew Research Center, 2020), it identifies justice, privacy, bias, regulation gaps, risks, and interpretability as major concerns. To address these issues, the study proposes a utilitarian ethics-based framework for ethical AI design in healthcare.

This study examines ChatGPT’s impact on organizational management, highlighting its benefits and challenges. It offers insights to help organizations integrate ChatGPT effectively.

This paper examines AI research in higher education from 2019 to January 2023, analyzing 723 articles. After filtering for relevant terms, 113 articles were categorized into adaptive systems, assessment, profiling and prediction, and intelligent tutoring. The results show rapid AI development, with most applications focused on predicting student performance, though few tools are ready for classroom use.

Experience

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