Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning

Abstract: The integration of Artificial Intelligence (AI) in teaching has transformed conventional teacher training methods, offering AI-driven feedback systems, interactive simulations, and adaptive learning environments. This study explored the role of AI, particularly generative models like ChatGPT, in enhancing lesson delivery, instructional feedback, and teacher engagement in micro- teaching. AI-powered platforms provide real-time, systematic, and personalized feedback, analyzing verbal communication, lesson structuring, and classroom engagement techniques to improve teaching effectiveness. Additionally, AI-driven simulations enable pre-service teachers to practice classroom management, respond to diverse learning scenarios, and develop adaptive instructional strategies in a risk-free virtual environment. Despite these advancements, AI in micro-teaching presents significant challenges, including bias in AI-generated feedback, lack of emotional intelligence, data privacy concerns, and the potential over-reliance on automation. Research highlights that while AI offers consistency and efficiency, it lacks the depth of human evaluation, particularly in assessing creativity, socialization, student engagement, and emotional responsiveness. A hybrid feedback model that integrates AI-driven analytics with human mentoring is recommended to balance structured feedback with contextual and personalized insights. This literature review synthesizes theoretical frameworks, such as Constructivist Learning Theory, Feedback and Learning Theories, and the Artificial Intelligence in Education (AIED) Framework, to explain AI’s role in micro-teaching. Findings suggest that AI-enhanced micro-teaching can complement conservative evaluation methods, leading to a more engaging, individualized, and efficient teacher training experience. However, ethical considerations and responsible AI integration must be prioritized to ensure fair, unbiased, and effective use of AI in education. This study contributes to the ongoing discourse on AI’s impact in teacher education, offering insights into its potential, limitations, and future directions. Keywords: micro-teaching, Artificial intelligence, AI-generated feedback, AI-powered simulations, Hybrid AI-human evaluation. Title: Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning Author: Joanne Nabwire Lyanda, Salmon Oliech Owidi International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH) ISSN 2349-7831 Vol. 12, Issue 2, April 2025 - June 2025 Page No: 1-10 Paper Publications Website: www.paperpublications.org Published Date: 04-April-2025 DOI: https://doi.org/10.5281/zenodo.15130275 Paper Download Link (Source) https://www.paperpublications.org/upload/book/Integrating%20Artificial%20Intelligence-04042025-3.pdf

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-04
DOI
https://doi.org/10.5281/zenodo.15130274
Primary Topic
E-Learning and COVID-19
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning

Zenodo (CERN European Organization for Nuclear Research)
E-Learning and COVID-19
article

Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning

article en

Abstract

Abstract: The integration of Artificial Intelligence (AI) in teaching has transformed conventional teacher training methods, offering AI-driven feedback systems, interactive simulations, and adaptive learning environments. This study explored the role of AI, particularly generative models like ChatGPT, in enhancing lesson delivery, instructional feedback, and teacher engagement in micro- teaching. AI-powered platforms provide real-time, systematic, and personalized feedback, analyzing verbal communication, lesson structuring, and classroom engagement techniques to improve teaching effectiveness. Additionally, AI-driven simulations enable pre-service teachers to practice classroom management, respond to diverse learning scenarios, and develop adaptive instructional strategies in a risk-free virtual environment. Despite these advancements, AI in micro-teaching presents significant challenges, including bias in AI-generated feedback, lack of emotional intelligence, data privacy concerns, and the potential over-reliance on automation. Research highlights that while AI offers consistency and efficiency, it lacks the depth of human evaluation, particularly in assessing creativity, socialization, student engagement, and emotional responsiveness. A hybrid feedback model that integrates AI-driven analytics with human mentoring is recommended to balance structured feedback with contextual and personalized insights. This literature review synthesizes theoretical frameworks, such as Constructivist Learning Theory, Feedback and Learning Theories, and the Artificial Intelligence in Education (AIED) Framework, to explain AI’s role in micro-teaching. Findings suggest that AI-enhanced micro-teaching can complement conservative evaluation methods, leading to a more engaging, individualized, and efficient teacher training experience. However, ethical considerations and responsible AI integration must be prioritized to ensure fair, unbiased, and effective use of AI in education. This study contributes to the ongoing discourse on AI’s impact in teacher education, offering insights into its potential, limitations, and future directions. Keywords: micro-teaching, Artificial intelligence, AI-generated feedback, AI-powered simulations, Hybrid AI-human evaluation. Title: Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning Author: Joanne Nabwire Lyanda, Salmon Oliech Owidi International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH) ISSN 2349-7831 Vol. 12, Issue 2, April 2025 - June 2025 Page No: 1-10 Paper Publications Website: www.paperpublications.org Published Date: 04-April-2025 DOI: https://doi.org/10.5281/zenodo.15130275 Paper Download Link (Source) https://www.paperpublications.org/upload/book/Integrating%20Artificial%20Intelligence-04042025-3.pdf

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 51%
E-Learning and COVID-19
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