Educational technologies play a critical role in the European action plan to strengthen digital literacy skills by 2027 and solve Grand Challenges related to work and education by 2030, as the global community shifts increasingly into the Digital Age. Four technology types are examined: face-to-face, online, hybrid or blended, and video learning. Policy makers who wish to weigh user preferences and acceptance of educational technology types will find they may better address, for example, the transition from school to hybrid work, as well as mitigate the shortage of teaching hours within The Netherlands. Acceptance is operationalized as ‘behavioral intention’ to use a technology type. However, models of acceptance cannot determine whether students and schools would successfully adopt these forms of learning (or technology types) after implementation, regardless of their reliability in measuring factors like BI/ATT, PE, or PEOU. Limited research has been conducted locally and nationally; literature case studies typically measure one or two learning forms. Moreover, theory suggests that, if a (learning) technology is accepted, then successful adoption is likely. Therefore, I aim to explore “To what extent do Dutch students of middle and higher education, ages 16-25, prefer and will theoretically accept different educational technology types?” This study employs a quantitative survey to rank learning preferences and assess its acceptance via a modified Technology Acceptance Model (TAM). This study intends to propose an emergence preference model (EPM) composed of a modified technology acceptance model (mTAM) to challenge the theoretical concept of preference as a robust measure to supply statistical information in support of acceptance studies. The model was validated with PLS-SEM, and the results were delimited by the modified TAM. The results showed secondary school students prefer Face-to-face learning while university students’ preference is well distributed. The reasons for these technological preferences were comprehensive and well explained by chosen acceptance factors. The preference of student’s technology for each acceptance factor was key to showing this relation. Therefore, user preference and user acceptance indeed provide comprehensive knowledge to decision makers who consider weighing acceptance.