Analysis of Generative AI Adoption in Self-Directed Learning Student at SKB Sidoarjo

Abstract

This research analyzes the adoption of Generative Artificial Intelligence (Generative AI) in self-directed learning among Kejar Paket B students at Sanggar Kegiatan Belajar (SKB) Sidoarjo. Employing a qualitative descriptive-analytic approach, data was collected through in-depth interviews, participant observation, and document analysis to explore the experiences of students, tutors, and policymakers. The findings indicate that Generative AI plays a significant role in enhancing accessibility to materials, learning flexibility, and student motivation through interactive explanations and instant feedback. However, the adoption of this technology faces complex challenges, such as student dependency on AI outputs, tutor resistance due to limited technical-pedagogical training, and the risk of content inaccuracies. The findings also reveal tutors' strategies in limiting the use of technology and the role of policymakers through tiered training and flexible regulations.

Theoretically, this research reinforces the relevance of constructivist theory and the Diffusion of Innovations Theory, emphasizing the importance of technology alignment with local needs, learning scaffolding, and a balance between innovation and human interaction. Practical implications include recommendations for comprehensive training for tutors, development of adaptive curriculum-based guidelines, periodic evaluation to minimize risks, and multidisciplinary collaboration in formulating holistic policies. This research contributes to understanding the dynamics of AI adoption in non-formal education with limited infrastructure and diverse student backgrounds, while offering inclusive solutions to maximize the potential of technology without sacrificing pedagogical values.