Applications of Artificial Emotional Intelligence Models in Human Learning
Keywords:
Artificial emotional intelligence, human learning, human–machine interaction, affective learning, affective computingAbstract
This study aimed to systematically review and analyze the role of artificial emotional intelligence (AEI) models in enhancing human learning and emotional human–machine interaction in educational contexts. This qualitative systematic review employed thematic analysis. Data were collected through an extensive search of major academic databases, including Scopus, Web of Science, IEEE Xplore, and Google Scholar, covering publications from 2015 to 2025. From an initial pool of 45 studies, 12 articles meeting the inclusion criteria were selected and analyzed using NVivo 14 software. Following Braun and Clarke’s (2006) thematic analysis procedure, open, axial, and selective coding were performed to identify main themes and subthemes related to AEI applications in learning. Three main themes emerged: (1) emotional interaction between humans and machines in learning, (2) computational and technical models of artificial emotional intelligence in education, and (3) educational and cognitive outcomes of AEI implementation. The findings revealed that emotion-aware learning systems improve self-regulation, intrinsic motivation, and engagement while reducing learning anxiety. They also foster social presence and empathy among learners. However, key challenges include ethical concerns over emotional data privacy, algorithmic bias, and cultural generalizability of affect recognition models. Integrating artificial emotional intelligence into education promotes more personalized, empathic, and adaptive learning experiences. Nonetheless, sustainable implementation requires human-centered design, robust ethical frameworks, and sensitivity to cultural and contextual diversity in emotional expression.
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