Journal of Educare

Emotional intelligence (EQ), Traditional EQ; AI-Enhanced EQ; Comparison.

This study conducts a comparative analysis of traditional emotional intelligence (EQ) training versus AI-enhanced EQ training. Traditional methods, including workshops, role-playing, and reflective practices, emphasize experiential learning and interpersonal interactions, enhancing participants' self-awareness, empathy, and social skills. In contrast, AI-enhanced training utilizes advanced algorithms and machine learning to provide personalized feedback and adaptive learning experiences, promising scalability and real-time data analytics. A systematic literature review from databases such as SCOPUS, Science Direct, Google Scholar, Research Gates, Web of Science, Springer and ERIC forms the basis of this analysis, complemented by qualitative data from focus group discussions with participants. Content analysis of these data highlights the strengths and weaknesses of each approach. Findings reveal that traditional EQ training excels in human interaction and experiential learning, while AI-enhanced training offers superior personalization and scalability. The study demonstrates that traditional EQ training methods, such as workshops, role-playing, and reflective practices, lead to significant improvements, with EQ-i scores increasing from 90 to 105 and SSEIT scores rising from 125 to 138. AI-enhanced training, using platforms like "EmotionAI," shows even greater effectiveness, with participants experiencing a 15% boost in EQ scores over 12 weeks. The findings suggest that integrating both approaches could optimize EQ development by combining the human-centric benefits of traditional methods with the scalability and precision of AI-driven tools.