There is growing literature on computerized dynamic assessment (C-DA) wherein individual items are accompanied by mediating prompts, but its effectiveness at fine-grained levels across time has not been explored sufficiently. This study constructed a computerized listening dynamic assessment (CLDA) system, where mediation was informed by an attribute-based mediation model (AMM) that established the relationship between the listening items and their underlying cognitive attributes. One hundred and twelve low-level university learners participated in the study, with the experimental group using the AMM-informed CLDA system (hereafter the CLDA group) and the control group (CG) using a non-dynamic assessment. Results indicated that the CLDA group significantly outperformed the CG in the post- and transfer- tests at both the test and attribute levels, and mediation was more effective for items of low and medium difficulty levels than those of high difficulty levels. Questionnaire and interview data indicated that most students perceived the CLDA system positively. The study demonstrates the advantages of AMM-informed C-DA in fine-grained diagnosis and tailored mediation. At the same time, it helps advance the validation pursuit of future mediation development.