This blog has been authored by Samuel Isiko, doctoral student in the program of Educational Research and Evaluation at Ohio University, Dr. Krisanna Machtmes, Associate Professor at Ohio University, and Doreen Otieno, doctoral student in the program of Educational Research and Evaluation at Ohio University. In this blog, we are excited to share with you our experiences and perspectives on using AI to transform the teaching of evaluation.
Artificial Intelligence (AI) is significantly impacting many disciplines inclusive of education, particularly teaching methodologies and evaluation. We recognize the contributions of Relevancy of Artificial Intelligence in Education: A Conceptual Review (2023); they indicate that integrating AI applications in teaching can streamline and enhance student learning, teaching techniques, content, and student assessment process, and offer tailored feedback to cater to individual student needs. Drawing from our experiences and insights with AI-driven technologies, educators can analyze students’ personal learning patterns and adjust the curriculum and pace to better suit individual preferences, thereby enhancing teaching effectiveness. As emerging evaluators, we advocate for the integration of AI due to its transformative potential in revolutionizing evaluation teaching. AI’s ability to adapt teaching strategies, personalize learning experiences, and boost student engagement and practical applications makes it a powerful tool for advancing the teaching of evaluation.
Value Addition for Utilizing AI
- One of the key benefits we highlight is AI’s ability to enhance simulations, which are crucial tools in the teaching of evaluation. These simulations offer a dynamic, interactive, and realistic approach to assessing and improving teaching practices and student learning outcomes. We firmly believe that incorporating AI-driven simulations and real-world case studies provides valuable hands-on experience in evaluation.
- Integrating AI-based packages in teaching makes the learning process more engaging and motivating (e.g. Intelligent Tutoring Systems). Our experience reveals that they increase student engagement by making learning more interactive and immersive. Engaged students are more likely to participate actively and invest in their learning.
- Our experiences with AI in evaluation indicate that they provide a robust analysis of large datasets to uncover trends and insights that might be missed with traditional methods, which leads to more informed decision-making. This is why we believe that it is time to integrate AI in the teaching of evaluation because it provides a platform that introduces students to advanced data analytics techniques and software that can help in analyzing and interpreting evaluation data effectively.
- Artificial Intelligence provides opportunities to conduct virtual site visits, access large data, and project reports within a classroom setting without necessarily going to the fields. This empowers students to apply evaluation techniques in real educational settings, such as internships or collaborative projects within and outside of schools.
Call for Action
Revolutionizing the teaching of evaluation calls for embracing innovative, AI-driven, and interactive methods to teach evaluators to conduct and apply evaluations in such an era. By integrating cutting-edge tools, personalized learning experiences, and practical applications, we aim to significantly enhance the effectiveness and relevance of teaching evaluation. We urge educators and institutions to adopt these transformative approaches to better prepare future evaluators and improve educational outcomes.
Rad Resource
We recognized the work of Relevancy of Artificial Intelligence in Education: A Conceptual Review (2023), who provide a comprehensive understanding of how integration of AI applications revolutionizes teaching. This resource is highly recommended for instructors, researchers, and evaluation professionals seeking to leverage AI to enhance the teaching of evaluation and gain deeper insights into improving student learning outcomes.
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