Explore the vast range of titles available.

One of the biggest challenges for EFL (English as Foreign Language) students to learn English is the lack of practicing environments. Although language researchers have attempted to conduct flipped classrooms to increase the practicing time in class, EFL students generally have difficulties interacting with peers and teachers in English in class. The advancement of Artificial Intelligence (AI) provides an opportunity to address this problem. With AI technologies, computer systems, in particular in the form of AI chatbots, are able to identify the meanings of users' statements and make responses accordingly. In the research design, AI-based chatbots were employed in the in-class and out-of-class activities for facilitating the students' speaking performance and interactions during the learning process in a university flipped English speaking classroom. The experimental results show that the mind map-guided AI chatbot approach (MM-AI) promoted the students' English speaking performances more than did the conventional AI chatbot approach (C-AI). Moreover, the MM-AI also promoted the students' learning performance and organized the interaction between the robots and humans more than the C-AI did. The findings could be a valuable reference for language educators and researchers who intend to conduct AI-supported flipped classrooms in language learning.

Early-stage ideation is a critical step in the design process. Mind maps are a popular tool for generating design concepts and in general for hierarchically organizing design insights. We explore an application for high-level concept synthesis in early stage design, which is typically difficult due to the broad space of options in early stages (e.g., as compared to parametric automation tools which are typically applicable in concept refinement stages or detail design). However, developing a useful mind map often demands a considerable time investment from a diverse design team. To facilitate the process of creating mind maps, we present an approach to crowdsourcing both concepts and binning of said concepts, using a mix of human evaluators and machine learning. The resulting computer-aided mind map has a significantly higher average concept novelty, and no significant difference in average feasibility (quantity can be set independently) as manually generated mind maps, includes distinct concepts, and reduces cost in terms of the designers’ time. This approach has the potential to make early-stage ideation faster, scalable and parallelizable, while creating alternative approaches to searching for a breadth and diversity of ideas. Emerging research explores the use of machine learning and other advanced computational techniques to amplify the mind mapping process. This work demonstrates the use of the both the EM-SVD, and HDBSCAN algorithms in an inferential clustering approach to reduce the number of one-to-one comparisons required in forming clusters of concepts. Crowdsourced human effort assists the process for both concept generation and clustering in the mind map. This process provides a viable approach to augment ideation methods, reduces the workload on a design team, and thus provides an efficient and useful machine learning based clustering approach.

Speaking skills are vital because they enable learners to express their ideas, opinions, and understanding of the material presented by the teacher. Furthermore, ChatGPT shows significant benefits to support students’ English-speaking learning. To find out the impact of using ChatGPT-aided mind maps on students’ English-speaking performance, a classroom action research study was conducted with 30 second-year English major students at a university in Vietnam during the first semester of the 2025-2026 academic year. The data collection tools included a pre-test, a post-test, and a questionnaire consisting of 24 Likert scale items and three open-ended questions. Quantitative data were analyzed using IBM SPSS Statistics (Version 25.0), including descriptive statistics and paired sample t-tests. The result shows that (1) the ChatGPT-aided mind maps help students enhance their English performance in grammar, vocabulary, pronunciation, fluency, and discourse management. (2) Students have positive perceptions of using ChatGPT-aided mind maps for preparing and organizing ideas for speaking skills. This study demonstrated that ChatGPT-aided mind maps have beneficial impacts on students' English-speaking skills.

Critical thinking skills are very important to have for students given the rapid distribution of information. To promote the critical thinking skills of the student, it could be done by including critical thinking learning in a formal learning environment. However, for the purpose of increasing this skill could be achieved, it required the motivation to learn. This study aimed to increase critical thinking skills and students' motivation through the implementation of an inquiry mind map tool. The study used a quasi-experimental group with a non-equivalent control-group pretest-posttest. The experimental class used an inquiry learning assisted inquiry mind map tool, whereas the control class requires that conventional learning. The sample consisted of 206 students from different schools and different genders. Data on critical thinking skills used essay tests referred to indicators from Sari in 2019. Motivation data learning taken from instrument motivation toward science learning from Tuan, Chin & Shied in 2005. Answers analyzed using ANCOVA. The research findings show that there were differences in the ability to think critically and students' learning motivation using an inquiry mind map tool. The results also examined no difference between school and gender on critical thinking skills and learning motivation. The results of the study show that the inquiry mind map tool was an impact on increasing critical thinking skills and learning motivation. It was recommended to be able to develop more other learning tools and learning model to improve critical thinking skills. Some im-plication was also provided in the article.

Mind mapping is a powerful technique that is often used for teaching declarative knowledge, but seldom implemented to record procedural knowledge. The present study focused on the latter. During a 12-week public presentation course, self-developed mind mapping software was utilized as a learning tool and an instrument to collect and analyze user behavior logs while summarizing and revising procedural knowledge. The participants were 53 working adults. They were divided into successful and unsuccessful mapping profiles based on their improvement. The pre- and post-tests on presentation skills, lag sequential analysis on log data, and interviews suggested that participants showing successful mapping behavior prioritized readability and ease of navigation of their maps. Their counterparts with unsuccessful mapping behavior tended to overload their maps and overuse highlighting. The discovery of actions and behavior patterns during the creation and revision of mind maps corresponding to successful/unsuccessful mind mapping profiles provides important suggestions to enhance existing digital mind mapping tools and to diagnose students who are falling behind. The implementation of mind mapping for procedural learning expands the area of mind mapping research and enlarges our understanding of teaching procedural knowledge.

The aim of this paper is to identify the basic elements that must be taken into account when constituting the complete process of crisis management in an organisation. This study explains the following: the identification of the basic elements; the sequence of the basic elements' relationships in the creation of crisis management; the reason for their importance in this process; terms; and the person/team responsible for their determination. The identification of the elements is based on a mind map. The logic progress of each action is presented in the network. Detailed graphical and tabular representations of the verbal accompaniment have been used to highlight the diversity of the activities and skills required when creating crisis management in an organisation. Thus, the elements presented and their relationships are a tool for managers. Their practical usefulness has been confirmed in several applications in different organisations.

Background Teaching and learning Community-Based Medical Education (CBME) requires the active engagement of students in various activities to cover planned curricular content. CBME being multifaceted involves careful application and formation of links when attending to community health problems and public health issues. Students often depend on factual recall rather than ‘engaging in’, to counteract the broad and comprehensive nature of CBME. This study was conducted to assess the effectiveness of Visual mapping techniques as a learning tool in a CBME program for the subject Community Medicine and thereby assist medical undergraduate students in overcoming identified learning challenges. Methodology An interventional study was conducted where medical undergraduates were randomly assigned to two different groups (each group = 30). After sensitization, a broad theme was taught to both the groups as a part of community-based teaching (CBT), each week for a month. The students in the intervention group were given the assignment to draw visual maps using the mind mapping & concept mapping techniques, after each CBT session, while the control group had Question-Answer session with built-in discussion (Conventional method) by an equally qualified, experienced faculty with no mapping assignments. A surprise written examination was conducted on the topics taught, and scores of both the groups were compared. Feedback was obtained from the intervention group. Results Mean score of the examination by the intervention group (29.85 ± 3.22) was significantly higher than and that of the control group (23.06 ± 4.09) (t = 7.14 and p  < 0.05). The students shared that the assignment of drawing mind and concept maps for topics taught helped in attempting questions of the written examination by facilitating easy recall of the information learned. It aided to frame the answers to descriptive questions in a structured way with the use of keywords. However, identifying the concepts and establishing relationship between them was slightly challenging. Conclusion ‘Visual mapping’ in the form of Mind and Concept mapping was found to be an effective learning tool for multifaceted CBME especially in promoting meaningful learning and facilitating rational thinking by the medical undergraduates.

Objective We analyzed the impact of mind mapping on the critical thinking ability of clinical nursing students and its use as a teaching application. This study provides reference information for clinical teaching. Methods We selected 64 nursing students using convenience sampling. Participants received basic knowledge training of mind mapping in three sessions during the intervention. Questionnaires on critical thinking ability were designed by the researchers, adopting the Chinese version of the Critical Thinking Disposition Inventory. Data collected using the questionnaires included learning strategy function and clinical skill improvement with mind mapping, as well as students’ degree of adaptability to mind mapping. Participants’ critical thinking ability before and after the intervention was analyzed using a paired t-test. Results The critical thinking inclination of nursing students was significantly improved after intervention compared with that before the intervention (t = −0.74). The four dimensions of open-mindedness, inquisitiveness, cognitive maturity, and systematicity among nursing students after the intervention were also significantly improved compared with before the intervention. Conclusion Mind mapping is conducive to improving the critical thinking ability of clinical nursing students.

Artificial Intelligence Generated Content (AIGC) technology has become an important driver of educational innovation with its content creation capability and personalized learning experience. In this paper, taking the teaching of information technology in preschool education as an example, a method of generating mind maps based on teaching videos is proposed to be used in teaching design. For the task of teaching resource recommendation, a DB-CGAT model is proposed, which combines the teaching resource knowledge graph context processing method with the dual behavior aggregation method. Yelp 2018, Amazon-Book, and CoLR datasets are used for recommendation performance experiments. In comparison with six mainstream baseline methods, the DB-CGAT model can achieve better performance in most cases. When τ = 0.3, DB-CGAT model has the best Recall@20 recommendation performance.

High-grade gliomas, particularly glioblastoma (MeSH:Glioblastoma), are among the most aggressive and lethal central nervous system tumors, necessitating advanced diagnostic and prognostic strategies. This systematic review and epistemic meta-analysis explore the integration of Artificial Intelligence (AI) and Radiomics Inter-field (AIRI) to enhance predictive modeling for tumor progression. A comprehensive literature search identified 19 high-quality studies, which were analyzed to evaluate radiomic features and machine learning models in predicting overall survival (OS) and progression-free survival (PFS). Key findings highlight the predictive strength of specific MRI-derived radiomic features such as log-filter and Gabor textures and the superior performance of Support Vector Machines (SVM) and Random Forest (RF) models, achieving high accuracy and AUC scores (e.g., 98% AUC and 98.7% accuracy for OS). This research demonstrates the current state of the AIRI field and shows that current articles report their results with different performance indicators and metrics, making outcomes heterogenous and hard to integrate knowledge. Additionally, it was explored that today some articles use biased methodologies. This study proposes a structured AIRI development roadmap and guidelines, to avoid bias and make results comparable, emphasizing standardized feature extraction and AI model training to improve reproducibility across clinical settings. By advancing precision medicine, AIRI integration has the potential to refine clinical decision-making and enhance patient outcomes.