Evaluating the Impact of Artificial Intelligence-Based Learning Methods on Students' Motivation and Academic Achievement - Rizkyana Wahyu Laras Pertiwi; Laeli Umi Kulsum; Isnaini Amirotun Hanifah (International Journal of Post Axial: Futuristic Teaching and Learning, 2024)
This research explores the transformative potential of artificial intelligence (AI) in elementary education, focusing on its impact on student motivation, academic achievement, and overall learning experiences. Through a comprehensive examination of existing literature, empirical evidence, and qualitative interviews with educators and administrators, this study sheds light on the benefits and challenges of AI integration in educational settings. The findings highlight a positive correlation between AI-based learning methods and student motivation levels, suggesting that technology can engage students in meaningful ways by fostering curiosity, autonomy, and intrinsic motivation in their learning journey. Moreover, significant improvements in students' academic achievement following exposure to AI-enhanced instruction underscore the efficacy of these innovative approaches in facilitating deeper conceptual understanding and mastery of academic content. However, addressing challenges such as equity, inclusion, and ethical considerations requires a multifaceted approach involving ongoing professional development, investments in infrastructure, and the establishment of ethical safeguards. Further research is needed to explore the nuanced dynamics of AI integration in education, including its impact on teacher-student interactions, classroom dynamics, and the broader socio-cultural context. Overall, interdisciplinary collaborations and a commitment to ethical innovation are essential for harnessing the transformative power of AI to create more equitable, inclusive, and effective learning environments for all student.