Fostering AI Literacy: A Survey of Student Perceptions and Effective Practices in K-12 Machine Learning

This research addresses the integration of Machine Learning (ML) into K-12 education, which is increasingly recognized as essential for preparing students for the AI-driven world. However, the current landscape of ML implementation and its impact in K-12 classrooms remain areas of active investigation. In recent years, the integration of ML techniques into K-12 computing education has become increasingly prevalent, reflecting the growing importance of AI-driven and data-driven approaches across computing fields. This study addresses the gaps in understanding how intuition and agency develop in the context of ML systems within K-12 education, while also examining the emerging trajectories, challenges, and opportunities in teaching ML in K-12 education. Motivated by the need to assess the effectiveness and challenges of ML integration in K-12 settings, this survey-based study aims to provide insights into pedagogical approaches, tools utilized, and teachers' perceptions of ML in education. Drawing upon the importance of early ML exposure in developing computational thinking and fostering data literacy, this study seeks to address gaps such as age-appropriate content, teacher preparedness, and ethical considerations. By systematically reviewing existing research and surveying educators, this study aims to offer valuable insights into effective practices, identify tools available, and highlight areas for further research on the responsible implementation of ML in K-12 settings. Furthermore, this study emphasizes the importance of understanding gender-based differences in ML education. It briefly presents gender-based results obtained from surveys and data analysis, suggesting the need for tailored approaches to ML education that address potential gender-based barriers and biases. Additionally, the study discusses the paradigm shift required to successfully integrate ML into K-12 computing curricula, advocating for embracing ML as a fundamental component of future computing education. Ultimately, this study aims to inform computing educators and policymakers about the challenges and opportunities associated with ML education in K-12 settings, laying the groundwork for fostering ML literacy and computational thinking skills among K-12 students.