Emotional Connection and Communication in Teaching with Artificial Intelligence: Challenges and Solutions

Authors

  • Somayyeh Ebrahimi Koushk Mahdi * Department of Education, Imam Reza International University, Mashhad, Iran. https://orcid.org/0000-0003-4536-8266
  • Samaneh Esfidani Department of Applied Mathematics, Tabadkan Education Teacher, Mashhad, Iran.

https://doi.org/10.48314/jidcm.v1i2.70

Abstract

The aim and focus of this paper is to examine the challenges and solutions of two-way communication and emotional communication in teaching with Artificial Intelligence (AI). This research used a qualitative approach and an analytical-interpretative method of data. The community of documents related to the topic of teaching challenges in cyberspace, AI, and research on two fundamental elements, emotional connection, which were purposefully selected from reputable domestic and foreign databases. The findings in the challenges section indicate two extraordinary elements in teaching and learning that are influenced by cyberspace and AI: motivation, strengthening social skills, and understanding students' real feelings after the event. In the solutions section, the use of blended learning, artificial platforms and software that allow for two-way interaction, the use of new tools and technologies in the field of AI, which will transfer feelings and emotions, motivate, strengthen social skills, and to some extent create various challenges and communication. The results of this study can help professors, teachers, administrators, and education activists to get the most productivity of cyberspace and AI in teaching by reducing risks and increasing opportunities.

Keywords:

Teaching and learning, Artificial intelligence, Cyberspace, Two-way interaction, Emotional connection

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Published

2025-06-23

How to Cite

Ebrahimi Koushk Mahdi, S. ., & Esfidani, S. . (2025). Emotional Connection and Communication in Teaching with Artificial Intelligence: Challenges and Solutions. Journal of Intelligent Decision and Computational Modelling, 1(2), 148-157. https://doi.org/10.48314/jidcm.v1i2.70