Adaptive Narrative Game for Personalized Learning In recent years, engineering educators have begun to focus more on new learning methods that allow students to better understand and practice their subject matter. However, these approaches are not ideal for every student, and can be costly or time consuming to educators and institutions. Instead, a more individualized approach to learning is needed. This paper addresses this challenge through a narrative game system. Building on top of a previously created narrative game, the system utilizes metacognitive strategies (Roadmap, What I KnowWhat I Want to KnowWhat I Have Solved, and ThinkAloudShareSolve) and a random forest machine learning model to model a studentu0027s learning process. Based on data collected from the student, including emotional state, time, and errors in solutions, the system can estimate the current stage in the learning process. The student can then be offered prompts or hints to guide learning in a positive and productive direction, dynamically correcting misconceptions and allowing the student to learn at hisher own pace in a stressfree environment.

AITutor: Generating Tailored Remedial Questions and Answers Based on Cognitive Diagnostic Assessment Developing AIpowered intelligent tutoring systems to facilitate adaptive learning has been a hot research topic due to that these systems can provide personalized learning guidance by taking individual learneru0027s status and needs into consideration to improve their learning performance. Based on this perspective, this paper proposes a novel personalized adaptive tutoring system named AITutor, which not only incorporates the basic functions of general tutoring systems that providing adaptive course learning to learners, but also can generate tailored remedial questions and answers based on cognitive diagnostic assessment. This unique and tailored tutoring service will potentially help learners master the deficient knowledge points in a much shorter time. This paper presents the preliminary research and development in implementing such an AIpowered tutor. First, it gives the system design with the function description of main modules and the operating procedure. Second, it identifies four main research problems behind this system and proposes approaches to solve these problems in developing the AITutor.