Xialing Lin

Pennsylvania State University, University of Scranton

Papers

Total Citations

424

H-Index

About

Xialing Lin is a pioneering researcher at the intersection of human-machine communication, artificial intelligence, and instructional technology. Her work fundamentally explores how social identity theory and machine heuristics shape human-robot interactions, particularly in educational contexts. Lin's most influential contribution, "Evaluations of an artificial intelligence instructor's voice" (2018, 261 citations), demonstrates how social identity processes affect perceptions of AI instructors, establishing foundational knowledge for designing effective educational robots. Her seminal piece "I, teacher: using artificial intelligence (AI) and social robots in communication and instruction" (2018, 153 citations) argues compellingly that human-machine communication represents a new relational context demanding urgent scholarly attention, predicting teachers' evolution into overseers who curate personalized AI-driven instruction. Lin also advanced understanding of the machine heuristic through experimental work on robot news delivery (2019), revealing how suspicion levels influence credibility assessments of robotic information sources. Her research carries profound implications for the future of education, journalism, and human-technology relationships, establishing her as a leading voice in understanding how artificial agents reshape fundamental human communication dynamics.