Document Type : Original Article

Researchers

• narges saedi ¹
• Esmaeil Zarei zavaraki ²
• Hamidreza Maghami ³
• Hasan Rashidi ⁴
• Esmaeil Sadipour ⁵

¹ psychology and educational science faculty, Allametabatae'i university, Tehran, Iran.,

² Faculty of Psychology and Educational Sciences, Tehran, Iran.

³ faculty of Psychology and Educational science, Tehran, Iran.

⁴ Faculty of Statistics and Computer, Tabatabai University, Tehran, Iran.

⁵ Department of Psychology, Faculty of Psychology and Educational Science, Allameh Tabataba'i University, Tehran, Iran.

Ministerial Ethics Committee

Problem Statement:

The rapid technological transformations and the widespread challenges in teachers’ professional performance in Iran—particularly the inefficiency of in-service training programs and the limited use of innovative instructional methods—have led to reduced student learning quality. Under these circumstances, AI-based collaborative learning environments, as a modern approach, can address existing gaps in teacher retraining, professional interaction, and performance improvement.



Importance and Necessity:

The expansion of artificial intelligence has enabled the development of collaborative and personalized learning environments that can help overcome the major challenges in teachers’ professional development, including theoretical and low-impact training and the lack of meaningful professional interaction. Considering the critical role of teachers in achieving 21st-century skills and improving student learning quality, leveraging such environments to enhance teachers’ professional competencies is an essential necessity.



Objectives and Hypotheses:

This study aims to design and validate an AI-based collaborative learning environment and examine its effect on improving the performance of teachers in the upper grades of elementary school. The main hypothesis of the study is that the intelligent environment will significantly improve teachers’ performance.



Research Method:

Using an exploratory mixed-methods approach, the study first identifies the components of an AI-based collaborative learning environment through qualitative content analysis and expert validation. In the quantitative phase, the effectiveness of the designed environment is examined using a quasi-experimental pretest–posttest design with ANCOVA. The study population includes elementary school teachers, and the measurement tool is the official teacher performance evaluation form issued by the Ministry of Education.