## 5 Discussion  
Current educational situations necessitate developing and implementing alternative strategies to replace the previous functions of learning evaluation settings due to COVID-19. The integration of artificially intelligent components into educational systems greatly helps to efficiently provide meaningful information for evaluation and learning [39; 40; 41]. On the one hand, adaptive testing technology offers an efficient and reliable personalized evaluation system. On the other hand, formative assessment systems provide information about the knowledge, skills, and abilities (KSAs) that a student may have obtained, their learning progression over time, and diagnostic feedback relative to their instruction and curriculum. The combination of adaptive testing technology and a formative assessment system is a promising means for effectively generating data that provide a teacher insight into where their students are struggling collectively and where particular students might need more help in accordance with the curriculum. While substantive theory and task design aspects in online formative assessments have been investigated, few analytic systems that allow for adaptively implementing formative assessments have been proposed [42; 43]. The linking of an adaptive algorithm as a psychometric method with an online formative assessment system allows the assessment to be efficient, accurate, and personalized [44; 45]. This study developed CAT for an online formative assessment system, called CAFT (computerized adaptive formative testing). CAFT is an evaluation technology that integrates seamless artificial intelligence elements into education for remotely delivering a customized diagnostic learning service. CAFT takes advantage of the adaptive functions of CAT and the statistical estimation method from DBNs. The adaptive functionality of CAT allows for adaptively selecting an item for a test and selecting a test for a sequential test aligned with a curriculum. In addition, DBNs estimates the real-time change of a learner's ability across multiple tests. Therefore, a combination of CAT as an adaptive selection method and DBNs as a real-time estimation method is the core ingenuity of CAFT as an integration of artificial intelligence into an e-learning system. This paper addressed the detailed specification and functions of CAFT. The performance of CAFT was examined using a simulation and application studies. The results showed that CAFT is a reliable and efficient testing system. However, the current system has a few limitations. First, the system does not contain detailed functions related to item development. A valid adaptive formative assessment using CAT should coherently consider substantive concepts, item design, and statistical analysis. The connection among substantive theories, item design, and analytic methods provides information about how students are progressing and where they are having difficulties solving items. This information is useful in the adaptive selection of items, assignments, or alternative instructional approaches in terms of a learner's level. If the item can be generated by various item features linking the substantive theories in a discipline, assessment can be more targeted and adaptive. Since the change of just one of the item features can require students to use different KSAs, more diagnostic information can be obtained using exquisite item design. Second, while there is a possibility of using CAFT for distinguishing experts from novices in perception, procedures, acquisition, and learning progression, more research into a valid psychometric model must be conducted. Third, a connection between adaptive learning and adaptive evaluation provides a more comprehensive and meaningful learning management system. A systematic connection between an adaptive learning system from CAFT and adaptive learning will be the topic of future study. Figure 10: Level change of the third learner in CAFT.