A Meta-Study of the Evolutionary Transformative Academic Landscape by Artificial Intelligence and Machine Learning - Lainjo, Bongs; Tmouche, Hanan (International Journal of Education, Teaching, and Social Sciences, 2024)
This article titled “The Evolutionary Transformative Academic Landscape by Artificial Intelligence and Machine Learning (Meta-Study)" explores the profound impact of AI and ML on education, particularly in the context of remote learning and the COVID-19 pandemic. The study systematically reviews the literature on AI in higher education, aiming to understand its pedagogical advantages and ethical implications. The objectives include assessing digital transformation in classrooms, evaluating the effectiveness of AI and ML in enhancing learning outcomes, examining their role in personalized learning and identifying areas for improvement. The research questions focus on the contribution of AI and ML in digital classrooms, their effectiveness in enhancing learning outcomes, and their role in supporting individualized learning. The literature review delves into AI and ML's role in academia, their impact on teaching, learning, and research, and the ethical considerations of their application. The study employs a meta-systematic review approach, incorporating statistical analyses and addressing ethical concerns to ensure AI's effective and ethical utilization in education. This study's findings indicate a positive correlation between implementing AI and ML technologies and improving student engagement, academic performance, research productivity, and teacher satisfaction. It highlights the necessity of further research to optimize AI use in education, considering software quality, student learning styles, and teacher integration skills. The study contributes to understanding the transformative role of AI and ML in reshaping education and fostering a more advanced, dynamic academic environment.