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About

Olov Engwall is a leading researcher in human-robot interaction and technology-enhanced language learning, with a particular focus on robot-assisted language learning (RALL) for adult second-language learners. His work explores how social robots can serve as conversation partners and tutors, examining the influence of robot behavior, interaction style, and role on learner engagement and outcomes. Among his most impactful contributions is a comprehensive analysis of how teaching strategy, robot role, and robot type shape RALL interactions, which has garnered 88 citations, alongside influential work on robot interaction styles for conversation practice (53 citations) and the use of robot gaze to mediate participation imbalances in group settings (57 citations). Engwall has pioneered the concept of the "robot language café," an innovative conversational practice format pairing learners with a robotic host. His research also advances the technical dimensions of RALL, including automatic detection of learner uncertainty and low engagement, backchannel modeling across cultures, and reducing reliance on Wizard-of-Oz setups in favor of fully autonomous systems. Through both design-oriented and empirical approaches, Engwall's work has meaningfully shaped how educational robots can be developed to support authentic, adaptive, and socially sensitive language learning experiences.