Saved in:
Bibliographic Details
Main Author: N.R. Nazarova
Format: Recurso digital
Language:
Published: Zenodo 2025
Subjects:
Online Access:https://doi.org/10.5281/zenodo.15285999
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866901900637503488
author N.R. Nazarova
author_facet N.R. Nazarova
contents <p><em><span>The addition of artificial intelligence (AI) to the training of primary education teachers has the potential to revolutionize pedagogical practice and increase educational outcomes. This research explores innovative approaches to improving AI programs in teacher training programs, emphasizing personalized learning experiences, data-driven decision-making, and adaptive teaching strategies. Using AI technologies such as machine learning algorithms and natural language processing, teacher training can be adapted to meet the needs of an individual intern, providing a deeper understanding of educational theories and practices. The study explores the current landscape of artificial intelligence in Teacher Education, identifies the challenges faced by teachers when applying these technologies, and provides a framework for effective implementation. Through a combination of qualitative and quantitative analysis, this study highlights the best experiences in introducing AI tools into curricula and provides recommendations for stakeholders in educational policy and teacher training programs. Ultimately, this work is aimed at improving the skills of primary education teachers, equipping them with the skills necessary to act in an increasingly digital learning environment, while promoting student engagement and success.</span></em></p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_15285999
institution Zenodo
language
publishDate 2025
publisher Zenodo
record_format zenodo
spellingShingle IMPROVING ARTIFICIAL INTELLIGENCE IN THE TRAINING OF PRIMARY EDUCATION TEACHERS
N.R. Nazarova
artificial intelligence (AI), teacher training, primary education, educational technology, personalized education, curriculum development, professional development, adaptive education systems, data analysis in education, educational robotics, machine learning programs, class management tools, interactive learning environment, pedagogical strategies.
<p><em><span>The addition of artificial intelligence (AI) to the training of primary education teachers has the potential to revolutionize pedagogical practice and increase educational outcomes. This research explores innovative approaches to improving AI programs in teacher training programs, emphasizing personalized learning experiences, data-driven decision-making, and adaptive teaching strategies. Using AI technologies such as machine learning algorithms and natural language processing, teacher training can be adapted to meet the needs of an individual intern, providing a deeper understanding of educational theories and practices. The study explores the current landscape of artificial intelligence in Teacher Education, identifies the challenges faced by teachers when applying these technologies, and provides a framework for effective implementation. Through a combination of qualitative and quantitative analysis, this study highlights the best experiences in introducing AI tools into curricula and provides recommendations for stakeholders in educational policy and teacher training programs. Ultimately, this work is aimed at improving the skills of primary education teachers, equipping them with the skills necessary to act in an increasingly digital learning environment, while promoting student engagement and success.</span></em></p>
title IMPROVING ARTIFICIAL INTELLIGENCE IN THE TRAINING OF PRIMARY EDUCATION TEACHERS
topic artificial intelligence (AI), teacher training, primary education, educational technology, personalized education, curriculum development, professional development, adaptive education systems, data analysis in education, educational robotics, machine learning programs, class management tools, interactive learning environment, pedagogical strategies.
url https://doi.org/10.5281/zenodo.15285999