Robot-assisted speaking practice (RASP): Learner affection, engagement, outcomes, and their interplay

Robots are increasingly utilised in language education to physically accompany learners, simulate natural communication through verbal and non-verbal cues, and thereby engage students in various academic tasks, demonstrating potential in improving learner affection, engagement, and language proficiency. So far, research on using robots specifically for developing English as a Foreign Language (EFL) speaking skill, along with associated learner affection and engagement, remains limited, despite the significance of developing EFL speaking skills and setting-specific exploration of learner affection and engagement. To address this gap, we employed a NAO robot to assist 49 Chinese undergraduate students in EFL speaking practice over eight sessions, examining their affection, engagement, outcomes, and the interplay among these factors within the robot-assisted speaking practice (RASP). Data collection included interviews and pre- and post-questionnaires and speaking tests. Findings indicated positive learner affection, deep engagement, and satisfactory EFL speaking proficiency outcomes compared to the baselines. While learner affection significantly enhanced engagement with RASP, neither affection nor engagement had a statistically significant impact on EFL speaking outcomes. Based on these results, we identified the advantages of robots over alternative technologies, peers, and teachers as the interlocutor in assisting speaking practice among adult learners, thereby enriching the robot-assisted language learning framework. We recommend implementing RASP to foster undergraduate EFL speaking development, particularly with a high-quality robot voice and speech recognition system.