01899nas a2200229   4500000000100000000000100001008004100002260001200043653002800055653001700083653001300100100003300113700002100146700002000167700003100187245011500218856008000333300001000413490000600423520122600429022001401655       2020                            d  c12/202010aArtificial Intelligence10ae-assessment10aAdoption1 aJosé Carlos Sánchez-Prieto1 aJuan Cruz-Benito1 aRoberto Therón1 aFrancisco García-Peñalvo00aAssessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students  uhttps://www.ijimai.org/journal/sites/default/files/2020-11/ijimai_6_4_8.pdf  a80-860 v63 aIn recent years, the use of more and more technology in education has been a trend. The shift of traditional learning procedures into more online and tech-ish approaches has contributed to a context that can favor integrating Artificial-Intelligence-based or algorithm-based assessment of learning. Even more, with the current acceleration because of the COVID-19 pandemic, more and more learning processes are becoming online and are incorporating technologies related to automatize assessment or help instructors in the process. While we are in an initial stage of that integration, it is the moment to reflect on the students' perceptions of being assessed by a non-conscious software entity like a machine learning model or any other artificial intelligence application. As a result of the paper, we present a TAM-based model and a ready-to-use instrument based on five aspects concerning understanding technology adoption like the AI-based assessment on education. These aspects are perceived usefulness, perceived ease of use, attitude towards use, behavioral intention, and actual use. The paper's outcomes can be relevant to the research community since there is a lack of this kind of proposal in the literature.  a1989-1660