Exploring School Needs and Barriers in Adopting a Deep Learning-Based Adaptive Evaluation Model - Ode Zulaeha; Yetni Marlina (Lembaga Penerbitan dan Publikasi Ilmiah Program Pascasarjana IAI Sunan Giri Ponorogo, 2025)
This research explores the needs and obstacles of schools in adopting a deep learning-based adaptive evaluation model. The research focuses on understanding infrastructure readiness, teachers' digital literacy, and the perception of principals, teachers, and curriculum developers towards the artificial intelligence-based evaluation system. The research method used is an exploratory qualitative approach with a multi-case study design. The study subjects included five secondary schools in Ternate City with different characteristics (public-private status and geographical context). Participants consisted of school principals, teachers, information technology staff, and curriculum developers. Data was collected through in-depth interviews, focus group discussions (FGDs), field observations, and document analysis. Data analysis was carried out with a thematic model through the stages of data reduction, presentation, and verification by triangulating sources and methods to maintain the validity of the findings. The study results show that most schools are still in the early stages of integrating learning technologies. The main obstacles found include the limitations of digital infrastructure (computers and internet networks), low technological literacy of teachers, resistance to change, and the absence of clear regulations on the use of AI in educational evaluation. On the other hand, schools identify three main needs to support adopting adaptive evaluation: providing adequate digital infrastructure, continuous technopedagogical training, and evaluation platforms that are simple and appropriate to the school context.