Understanding Primary Teachers’ Perspectives On AI-Supported Joyful Learning Classroom Practices

Authors

  • Dr. Ramesh K. Parua
  • Dr. Rubul Kalita
  • Dr. Jasmer Singh
  • Dr. Subrata Sen

Keywords:

Chest computed tomography, COVID-19, deep learning, lesion segmentation, pulmonary burden estimation, Transformer.

Abstract

Artificial Intelligence (AI) augments educational practices by providing more personalized, interactive, and creative learning. One of these strategies is joyful learning. Joyful learning concentrates on the learner's participation, interest, motivation, creativity, and the overall emotional state of the users. Therefore, incorporating joyful learning into AI may help teachers develop their primary classrooms in more engaging and learner-centered ways. The purpose of this study is to evaluate primary school teachers' opinions on the procurement of joyful learning practices and AI. This study will employ a descriptive survey research design. The sample will be 200 primary school teachers selected from government and private primary schools in Meghalaya, India. This study will consist of a researcher-developed questionnaire asking teachers to evaluate the usefulness, accessibility, and pedagogy of AI-based joyful learning. Along with this, participants will evaluate their own level of preparation, anticipated benefits, and perceived obstacles. The author will analyze the data by utilizing descriptive statistics, including frequency, percentage, mean, and standard deviation, and will employ other relevant inferential analyses when necessary. It will provide information on proper teacher assessment for incorporating AI and will provide proper foundation for other research focused on developing joyful and technology enhanced primary level school education.

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Published

2026-09-01

How to Cite

Parua, D. R. K., Kalita, D. R., Singh, D. J., & Sen, D. S. (2026). Understanding Primary Teachers’ Perspectives On AI-Supported Joyful Learning Classroom Practices. International Journal of Artificial Intelligence and Machine Learning, 6(3), 305–315. Retrieved from https://www.svedbergopen.com/index.php/ijaiml/article/view/1737