Date of Award

2024

Degree Type

Thesis

Degree Name

Master of Education (MEd)

First Advisor

Dr Wachira Nicholas

Department

Institute for Educational Development, East Africa

Abstract

This dissertation investigates the integration of Artificial Intelligence (AI) into science education at teacher training institutions, aiming to identify effective strategies for its implementation. The research focuses on the central question: How can AI be effectively integrated into science teacher training to enhance future science education practices?

Employing a Design-Based Implementation Research (DBIR) framework, the study fosters collaborative exploration and iterative development alongside science tutors, teachers, and administrators. This approach bridges the gap between academic research and practical classroom application, ensuring that any proposed AI integration strategies are both theoretically sound and readily adaptable to real-world educational settings.

The research's significance lies in its potential to transform science education through personalized learning experiences powered by AI. By equipping science tutors and teachers with the knowledge and tools necessary to seamlessly incorporate AI technologies into their teaching methodologies, the dissertation strives to empower educators and, ultimately, ignite a passion for science in their students.

To understand the current landscape and inform strategic recommendations, the study delves into several key areas. First, it identifies existing challenges in science education, such as the limited integration of Information and Communication Technologies (ICT) and AI, the scarcity of practical experiment opportunities, and high student-tutor ratios. Secondly, it explores the opportunities presented by AI integration, including personalized learning pathways, access to diverse resources, and real-time tutoring capabilities, all of which have the potential to boost student engagement and academic performance.

Through qualitative and quantitative data analysis encompassing insights from science teachers, tutors, and administrators, the research sheds light on the current state of educators' digital skills, the perceived opportunities and challenges associated with AI integration, and valuable feedback on potential implementation strategies. These findings pave the way for the development of concrete recommendations focused on three key areas:

Firstly, targeted professional development programs Equipping educators with the necessary skills and knowledge to confidently utilize AI tools in their teaching practices.

Secondly, infrastructure improvement: Ensuring schools have adequate technological resources and support systems to facilitate effective AI integration.

Lastly collaborative efforts: Fostering cooperation among educators, policymakers, researchers, and technology developers to optimize AI integration strategies and ensure their long-term sustainability.

The implications of this research extend beyond the immediate realm of teacher training institutions. By providing valuable insights and practical recommendations, it informs a wider audience, including policymakers seeking to guide educational advancements, researchers refining AI tools for educational purposes, and future educators envisioning a classroom where technological innovations enhance the learning experience and stimulate scientific curiosity. Ultimately, this dissertation serves as a catalyst for transformation, empowering educators to leverage the power of AI in creating a more engaging, effective, and inspiring science education environment for generations to come.

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Last Page

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