Individual growth/change has long been an active topic in educational, psychological and behavioral studies. During the last decades, the assessment pattern of objectively quantifying the learning status and providing diagnostic feedback has been increasingly valued, aiming at promoting students’ learning, based on the idea of assessment for learning and the characteristics of cognitive diagnostic assessments (CDA). Although most of the existing studies on CDA mentioned that the main objective of CDA is to identify students’ strengths and weaknesses in their learning status and to provide guidance for targeted remedial teachings, still few studies have focused on and evaluated the effectiveness of such remedial teachings. The main reason is that the cross-sectional design is currently adopted by most CDAs. This means that only one assessment is done at a specific point in time. This issue may also reflect on the development of existing cognitive diagnosis models (CDMs) which is one of the core components of CDA. Although various CDMs have been suggested by previous researchers, most of them are only applicable to cross-sectional data analysis. The longitudinal data collected from the assessments throughout the learning process provides researchers the chance to develop learning models, that can be adapted to track individual growth over time and to evaluate remedial teachings’ effectiveness. Compared to the cross-sectional cognitive diagnostic assessment, the longitudinal cognitive diagnostic assessment is more helpful when aiming at promoting students' development. However, so far, longitudinal CDM is just a new rising topic in foreign countries, and still, in its infancy, there are many issues for further study. And the domestic research is almost blank. Therefore, in order to make domestic scholars systematically understanding the longitudinal CDM, several longitudinal CDMs are reviewed in this article. We first divided the existing longitudinal CDMs into two types according to the modeling logic: one is based on the latent transition analysis and another one is based on the higher-order latent structural model. Then, the theoretical basis and application scenarios of some representative models are introduced and explained one by one, including Li, Cohen, Bottge, & Templin (2016), Kaya and Leite (2017), Wang, Yang, Culpepper, and Douglas (2018), Huang (2017), and Zhan, Jiao, Liao, and Li (2019). Furthermore, a simple simulation study was conducted to present how to use the longitudinal CDM for data analysis and how to interpret corresponding diagnostic results, which is of certain reference value to practitioners. Finally, four future research topics are concluded, (a) systematic comparison between different longitudinal CDMs, which can provide theoretical suggestions for practitioners to choose suitable models; (b) incorporating process data or biometrical data into current longitudinal CDMs, which can provide theoretical support for using multivariate data to assist the evaluation of student development; (c) extending current longitudinal CDMs to handle polytomous attributes and probabilistic attributes, since polytomous attributes and probabilistic attributes can describe the growth of students in a more refined way than binary attribute; (d) compared with cross-sectional CDAs, the diagnostic accuracy and validity of longitudinal CDA that used to depict the learning or growth trajectories are more worthy of attention by researchers and practitioners. In addition to choose a suitable longitudinal CDM, many factors such as the quality of the longitudinal test itself, the setting of a cognitive model, students’ response attitude, cheating and missing data will also affect the accuracy and validity of the diagnostic results. The influence degree of these factors on the longitudinal diagnosis and the corresponding compensation or detection methods are also worth further discussion.