Using mobile learning (ML) has become exceedingly relevant in times of distant teaching. Although much is known about the factors affecting ML usage, less is known about the ML adoption process under constraints such as the COVID-19 pandemic. The aim of this exploratory case study was to gain insight into the ML adoption process using the lens of Rogers' Diffusion of Innovation Theory. Participants were in-service (32) and preservice (29) teachers who attended ML training. Data were collected using semi-structured interviews (20), focus groups (6), and participants' reflections (183) at three time points. Data underwent multilevel analysis (content and linguistic analysis), revealing 12 themes that denote the ML adoption process and demonstrated intergroup similarities and differences. The study provides theoretical insight into the ML adoption process under crisis and highlights the features that must be addressed to promote optimal ML adoption in teacher education in both routine and emergency conditions.