Pedagogical (Tutor or Tutoring) Models are an important element of Intelligent Tutoring Systems (ITS) and they can be described by sets of (tutoring) rules. The implementation of a Tutoring Model includes both the formal representation of the aforementioned rules and a mechanism able to interpret such representation and execute the rules. One of the most suitable approaches to formally represent pedagogical rules is to construct semantic web ontologies that are highly interoperable and can be integrated with other models in an ITS like the subject domain and the student model. However, the main drawback of semantic web-based approaches is that they require a considerable human effort to prepare and build relevant ontologies. This paper proposes a novel approach to maintain the benefits of the semantic web-based approach in representing pedagogical rules for an ITS, while overcoming its main drawback by employing a data mining technique to automatically extract rules from real-world tutoring sessions and represent them by means of Web Ontology Language (OWL).

This exploratory research conducted a thematic analysis of students’ experiences and utilization of AI tools by students in educational settings. We surveyed 87 undergraduates from two different educational courses at a comprehensive university in Western Canada. Nine integral themes that represent AI’s role in student learning and key issues with respect to AI have been identified. The study yielded three critical insights: the potential of AI to expand educational access for a diverse student body, the necessity for robust ethical frameworks to govern AI, and the benefits of personalized AI-driven support. Based on the results, a model is proposed along with recommendations for an optimal learning environment, where AI facilitates meaningful learning. We argue that integrating AI tools into learning has the potential to promote inclusivity and accessibility by making learning more accessible to diverse students. We also advocate for a shift in perception among educational stakeholders towards AI, calling for de-stigmatization of its use in education. Overall, our findings suggest that academic institutions should establish clear, empirical guidelines defining student conduct with respect to what is considered appropriate AI use.

A web-based, stage-matched message intervention was designed in order to see the effect on the physical activity of young Taiwanese women with regard to several variables. The intervention was guided by the Transtheoretical Model (TTM) and was evaluated using a pre–post-test control group design. One hundred thirty female freshmen, enrolled in a nursing class in a university in Taipei, completed the survey at three different points in time. They were assigned to three groups: an experimental group with stage-matched messages on the website, a generic group with non-stage-matched messages on the website, and a control group that was given only lectures but had no access to the website. Results indicated that the subjects in the stage-matched group improved most in terms of stage-of-exercise and amount of physical activity, followed by the generic group at immediate post-test; meanwhile, the control group had a tendency toward downward change. The effect did not persist, however, for the first two groups. A significantly higher level of exercise self-efficacy was found in the stage-matched group than in the other two groups immediately after the intervention. The results suggested that a website conveying theory-based messages can serve as a useful tool for improving young females' physical activity and exercise self-efficacy. Additionally, we need research that explores different degrees and dimensions of tailoring in order to find the optimal degree of audience segmentation.

This research involved the design of a task-based dialogue system and evaluation of its learning effectiveness. Dialogue training still heavily depends on human communication with instant feedback or correction. However, it is not possible to provide a personal tutor for every English learner. With the rapid development of information technology, digitized learning and voice communication is a possible solution. The goal of this research was to develop an innovative model to refine the task-based dialogue system, including natural language understanding, disassembly intention, and dialogue state tracking. To enable the dialogue system to find the corresponding sentence accurately, the dialogue system was designed with machine learning algorithms to allow users to communicate in a task-based fashion. Past research has pointed out that computer-assisted instruction has achieved remarkable results in language reading, writing, and listening. Therefore, the direction of the discussion is to use the task-oriented dialogue system as a speaking teaching assistant. To train the speaking ability, the proposed system provides a simulation environment with goal-oriented characteristics, allowing learners to continuously improve their language fluency in terms of speaking ability by simulating conversational situational exercises. To evaluate the possibility of replacing the traditional English speaking practice with the proposed system, a small English speaking class experiment was carried out to validate the effectiveness of the proposed system. Data of 28 students with three assigned tasks were collected and analyzed. The promising results of the collected students’ feedback confirm the positive perceptions toward the system regarding user interface, learning style, and the system’s effectiveness.

A number of Learning Management Systems (LMSs) exist on the market today. A subset of a LMS is the component in which student assessment is managed. In some forms of assessment, such as short-answer questions, the LMS is incapable of evaluating the students' responses and therefore human intervention is necessary. This study leverages the research conducted in recent Natural Language Processing studies to provide a fair, timely and accurate assessment of student short-answers based on the semantic meaning of those answers. A component-based system utilizing a Text Pre-Processing phase and a Word/Synonym Matching phase has been developed to automate the marking process. An evaluation plan is also made to verify the possibility of applying such computerized assessment system in practical situations as well as to reveal areas in which the system could be improved later.