Segev Shlomov

Papers

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About

Segev Shlomov is a researcher at the intersection of artificial intelligence, robotics, and human-computer interaction, with a focus on how intelligent systems can augment human skills and automate complex processes. His work spans three key areas: fine motor skill acquisition, conversational robotic process automation (RPA), and prescriptive process monitoring. In his most-cited paper, "Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition" (2024, 9 citations), Shlomov investigates how AI-driven virtual instructors can teach handwriting and other delicate motor tasks, addressing the inconsistency and time demands of traditional methods. This work bridges robotics and education, offering scalable solutions for skill development. He further advances automation in "Recommending Next Best Skill in Conversational Robotic Process Automation" (2022, 5 citations) and "Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration" (2022, 3 citations), where he integrates goal-driven chatbots with RPA to recommend optimal actions and monitor business processes in real time. By combining conversational AI with process automation, Shlomov contributes to more adaptive, intelligent systems that enhance both learning and operational efficiency. His research is particularly relevant for students and practitioners interested in AI-driven education, human-robot collaboration, and next-generation business automation.