Emotion-Aware Tutors for English Language Learners: Real-Time Adaptation of Instruction Based on Affective States
Year: 2025
Type: article
Abstract: This study investigated the manner of employing emotional awareness in AI tutoring systems to support dimensions of second language acquisition (SLA) that address cognitive aspects of learning as well as emotional dimensions of learning. A quasi-experimental design established two experimental groups, each consisting of 120 intermediate-level English learners, who were randomly assigned to either the emotion-aware AI tutor or the conventional AI tutor. The academic success of the participants was assessed through vocabulary retention and speaking fluency measures, while a self-reported confidence from student’s measure assessed learner confidence. The self-reports of students using the emotion-aware AI tutor indicated significant progress on vocabulary retention, speaking fluency, and confidence, as learning increased. Qualitative evaluations of the emotion-aware AI tutor gave feedback that indicated the AI tutor's emotional support motivated learners and reduced their anxiety, suggesting the importance of an AI tutor's emotional support in the learning process, when involved. The findings of this study contribute to the ongoing development of emotion-aware artificial intelligence (AI). The study presented a feasible AI system model that addresses cognitive learning, supports learners emotionally, and bolsters their emotional well-being, with minimal cognitive progress made by learners in SLA.
Source: Journal of Posthumanism
Authors: Amal AlNatour, Dima Hijazi, Gurol Yokus
Institutions: Yarmouk University, Sinop University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4414252104
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