Search Results (48)

To correspond to the advancement of technology, programming has become an indispensable course in university curricula. However, students easily become confused by the rules governing program execution or by complex logical structures. Mastering program structure and logic often is a significant challenge for beginners, especially. Despite the availability of information on programming on various websites and tools, including generative artificial intelligence (AI), there is still a gap between conceptual understanding and practical application for beginners. They overlook important implementation details or struggle to grasp the flow of a program, making the mastery of program logic a persistent challenge. To address these issues, we have developed a system that dynamically generates process architecture diagrams. Users upload their code, and the system produces corresponding diagrams that decompose and execute the code line by line. Its visual representation allows users to observe the program’s execution and aids them in comprehending the sequence and operational flow of the code. By understanding the structure and logic of the program intuitively, this system supplements traditional teaching methods and AI-assisted question-and-answer tools. The experimental results demonstrated that students found the system helpful to track their learning progress (87%) and improved their understanding of program code (81%). Additionally, 84% of students reported that the system was easy to use, highlighting its user-friendliness. In terms of student interest, 83% of students agreed that the interactive elements made learning more engaging, indicating that the system positively contributed to dynamic and enjoyable learning. However, 63% of students reported an improvement in coding and were influenced by the complexity of the programming tasks assigned. Despite this, the overall satisfaction with the system developed in this study was high. Full article

Artificial intelligence (AI) has found applications across diverse sectors in recent years, significantly enhancing operational efficiencies and user experiences. Educational data mining (EDM) has emerged as a pivotal AI application to transform educational environments by optimizing learning processes and identifying at-risk students. This study leverages EDM within a Moroccan university (Hassan First, University Settat, Morocco) context to augment educational quality and improve learning. We introduce a novel “Hybrid approach” that synthesizes students’ historical academic records and their in-class behavioral data, provided by instructors, to predict student performance in initial coding courses. Utilizing a range of machine learning (ML) algorithms, our research applies multi-classification, data augmentation, and binary classification techniques to evaluate student outcomes effectively. The key performance metrics, accuracy, precision, recall, and F1-score, are calculated to assess the efficacy of classification. Our results highlight the long short-term memory (LSTM) algorithm’s robustness achieving the highest accuracy of 94% and an F1-score of 0.87 along with a support vector machine (SVM), indicating high efficacy in predicting student success at the onset of learning coding. Furthermore, the study proposes a comprehensive framework that can be integrated into learning management systems (LMSs) to accommodate generational shifts in student populations, evolving university pedagogies, and varied teaching methodologies. This framework aims to support educational institutions in adapting to changing educational dynamics while ensuring high-quality, tailored learning experiences for students. Full article

Blended learning has introduced a more accessible and flexible teaching environment in higher education. However, ensuring that content is inclusive, particularly for students with learning difficulties, remains a challenge. This paper explores how Moodle, a widely adopted learning management system (LMS), can support inclusive and adaptive learning based on Universal Design for Learning (UDL) principles. A 16-week descriptive exploratory study was conducted with 70 undergraduate students during a software engineering fundamentals course at Philadelphia University in Jordan. The research combined weekly iterative focus groups, teaching reflections, and interviews with 16 educators to identify and address inclusion barriers. The findings highlight that the students responded positively to features such as conditional activities, flexible quizzes, and multimodal content. A UDL-based framework was developed to guide the design of inclusive Moodle content, and it was validated by experienced educators. To our knowledge, this is the first UDL-based framework designed for Moodle in Middle Eastern computing and engineering education. The findings indicate that Moodle features, such as conditional activities and flexible deadlines, can facilitate inclusive practices, but adoption remains hindered by institutional and workload constraints. This study contributes a replicable design model for inclusive blended learning and emphasizes the need for structured training, intentional course planning, and technological support for implementing inclusivity in blended learning environments. Moreover, this study provides a novel weekly iterative focus group methodology, which enables continuous course refinement based on evolving students’ feedback. Future work will look into generalizing the research findings and transferring the findings to other contexts. It will also explore AI-driven adaptive learning pathways within LMS platforms. This is an empirical study grounded in weekly student focus groups, educator interviews, and reflective teaching practice, offering evidence-based insights on the application of UDL in a real-world higher education setting. Full article

Teaching social research methodology in university courses, whether qualitative or quantitative, presents significant challenges for both instructors and students. These challenges include the availability of empirical datasets, the illustration of data analysis techniques, the simulation of research report writing, and the facilitation of scenario-based learning. Emerging AI tools, such as ChatGPT-4, offer potential support in higher education, though their effectiveness depends on the context and their integration with traditional didactic methods. This article explores the potential of AI in teaching social research methodology, with a focus on its benefits, limits and ethical considerations. Furthermore, the paper presents a case study of AI application in teaching qualitative research techniques, specifically in the analysis of solicited documents. Generative AI shows the potential to improve the teaching of social research methodology by providing students with opportunities to engage in hands-on learning, interact with realistic datasets and refine their analytical and communication skills. The integration of AI in education should, however, be approached with a critical mindset, ensuring that AI tools serve as a means to sharpen (not replace) traditional methods of learning. Full article

In this study, an ICT course is redesigned with a blended learning concept. This course aims to teach an introduction to game technology in the following three main topics: ‘Introduction to Computer’, ‘Game software technology’, and ‘Game art technology’. Basic computer science concepts such as binary numbers, algebra, vectors, data structure, computer graphics, and artificial intelligence (AI) are introduced in this course. In the flipped classroom, insufficient preparation of students before class and an increased workload of students and teachers are the challenges to overcome. Active learning is carried out in the classroom, as it enhances students’ concentration in the classroom. The pre- and post-test was used to investigate the effects of in-class and out-of-class activities in this method. In this study, active learning was applied to flipped classrooms in this course, and its learning effects were compared with that of the traditional method. The learning outcomes of active learning were significantly improved. In-class activity had significant effects on the outcome quantitatively and qualitatively. The learning outcomes of out-of-class activities for which students were usually insufficiently prepared were also improved. Full article

Exploring the potential of text-to-image generation technology in Taiwanese vocational high school art courses, this study employs a conceptual framework of technology integration, creative thinking, and metacognitive abilities, focusing on its effects on teaching strategies as well as students’ digital art creation skills and cognitive and creative development. The study was conducted through a multi-methodological approach that includes a systematic literature review plus participatory action research and qualitative analysis. The results showed that integrating text-to-image technology with education boosted students’ interest in activities such as prompt design and project creation and suited themes like landscapes and conceptual art. Testing AI tools enhanced technical proficiency (average of 3.95/5), while pedagogy shifted to project-based learning, increasing engagement. Students’ digital art skills improved from 3.26 to 3.78 (16% growth), with creativity and originality (3.82/5), style diversity, visual complexity, and divergent thinking notably advanced. The technology also fostered metacognitive skills and critical thinking, proving to be an effective teaching aid beyond a mere digital tool. This discovery provides a fresh theoretical viewpoint and instructional procedures for high school art education curricula, anchored in technology, and highlights the importance of nurturing students’ innovativeness and adaptability within the contemporary digital age. Full article

This study aims to create an AI system that analyzes text to evaluate student engagement in STEAM education. It explores how sentiment analysis can measure emotional, cognitive, and behavioral involvement in learning. We developed an AI-based text sentiment analysis system to assess learning engagement, integrating speech recognition, natural language processing techniques, keyword analysis, and text sentiment analysis. The system was designed to evaluate the level of learning engagement effectively. A computational thinking curriculum and study sheets were developed for university students, and students’ participation experiences were collected using these study sheets. The study utilized the strengths of SnowNLP and Jieba, proposing a hybrid model to perform sentiment analysis on students’ learning experiences. We analyzed: 1, The effect of sentiment dictionaries on the model’s accuracy; 2, The accuracy of different models; and 3, Keywords. The results indicated that different sentiment dictionaries had a significant impact on the model’s accuracy. The hybrid model proposed in this study, utilizing the NTUSU sentiment dictionary, outperformed the other four models in effectively analyzing learners’ emotions. Keyword analysis indicated that teaching materials or courses designed to promote practical, fun, and easy ways of thinking and building logic helped students develop positive emotions and enhanced their learning engagement. The most frequently occurring keywords associated with negative emotions were “problem”, “error”, “not”, and “mistake”. This finding suggests that learners experiencing challenges during the learning process—such as encountering mistakes, errors, or unexpected outcomes—are likely to develop negative emotions, which in turn decrease their engagement in learning. Full article

Advancements in large language models have significantly transformed higher education by integrating AI chatbots into course design, teaching, administration, and student support. This study evaluates the use, effectiveness, and perceptions of chatbots in a Python-based graduate-level GIS programming course at a U.S. university. Students self-reported perceived improvements in skills and the use of different help resources across three home assignments of varying complexity and spatial context. In group discussions, students shared their experiences, strategies, and envisioned future applications of chatbots in GIS programming and beyond. The results indicate that prior programming experience enhances students’ perception of assignment usefulness, and that chatbots serve as a partial replacement for traditional help resources (e.g., websites) in completing assignments. Overall, students expressed positive sentiments regarding chatbot effectiveness, especially for complex spatial tasks. While students were optimistic about the potential of chatbots to enhance future learning, concerns were raised about overreliance on AI, which could hinder the development of independent problem-solving and programming skills. In conclusion, this study offers valuable insights into optimizing chatbot integration in GIS education. Full article

In recent years, the application of artificial intelligence (AI) in industry has matured, requiring deeper learning and integration of existing technologies. This study started with technical education to improve the professional quality of human resources. The double-triangular fuzzy number and gray area testing methods in the fuzzy Delphi method (FDM) were used to evaluate expert consensus, plan technical capability indicators, and ensure the integrity and appropriateness of teaching materials. Based on these indicators, special subject teaching course units were designed and integrated into existing courses for experimental teaching and evaluation. The teaching module arrangement in this research used a virtual instrument control system with LabVIEW v2021 as the GUI and the myRIO controller. The proposed system integrates an artificial neural network (ANN) AI model built with Python v3.7 for data analysis and prediction, forming an embedded teaching module for a deep learning-oriented intelligent robotic environmental monitoring system. This study evaluated students’ acceptance of deep learning robotics teaching modules and their impact on improving their technical skills. The psychomotor scale established by the scholars was adopted and revised, including this study’s technical ability indicators. The test-retest reliability of the psychomotor scale was high. The results revealed that the post-test scores of the psychomotor scale were significantly better than those of the pre-test, indicating that students’ overall technical abilities improved. Students’ affective attitudes toward the four dimensions of teaching material and equipment, cognitive development, skills performance, and self-exploration were positive. Feedback revealed that students who participated in the teaching experiment responded positively on all levels of the affective scale, indicating increased motivation and willingness to continue learning. This study successfully constructed a teaching module and evaluation model for deep learning robotic environmental sensing and control. The teaching module and evaluation model established through this research contribute to the cultivation and effectiveness evaluation of relevant technical talents. Full article

This study, grounded in constructivist theory, presents an innovative artificial intelligence (AI) course framework for undergraduate students from the School of Artificial Intelligence at Anhui University. This research focuses on students’ perceptions and explores the impact of blogs as a platform for assignment submission in an AI course. Data are collected using structured questionnaires and analyzed quantitatively using a partial least squares structural equation model. Additionally, interviews are conducted to provide nuanced insights and contextual explanations, thus supplementing the quantitative findings. This study explores the specific impacts of blogs on students’ learning abilities, learning experiences, academic outcomes, and overall satisfaction within the AI course. By leveraging blogs as pedagogical tools, this study highlights their potential to transform traditional teaching methods and promote active learning and knowledge sharing in higher education. The proposed course framework also offers a scalable model that can be adapted for other science and engineering disciplines in colleges and universities. Full article

Based on the technology acceptance model (TAM), pre-service teachers’ acceptance of artificial intelligence (AI) is crucial in predicting their intentions to use AI in future teaching, as well as for their actual usage of AI. However, current research offers limited insights into the role of factors regarding usage intentions and behaviors. In particular, AI-related teacher training courses and AI-related technological pedagogical content knowledge (AI-TPACK) might be relevant, but are empirically underinvestigated within the TAM. This study addresses these gaps by investigating the relationships between pre-service teachers’ participation in AI-related courses, their self-reported AI-TPACK, their perceptions of AI’s usefulness and ease of use, and both their intention and actual usage of AI. Using path models with data from 143 pre-service teachers, the results revealed that participation in AI-related courses related positively to AI-TPACK and perceived AI-related usefulness. Further, AI-TPACK was positively related to perceived AI-related usefulness and ease of use, which in turn positively related to the behavioral intention to use AI in future teaching and the actual usage of AI for profession-related tasks in teacher training. The study results extend the existing research on TAM and highlight the consideration of participation in AI-related courses and AI-TPACK as further factors in understanding pre-service teachers’ AI acceptance. Full article

The application of Artificial Intelligence (AI) to teacher training is a rather recent phenomenon and there is a need for more research on its use in teacher education. This paper examines the use and interpretation of AI by student language teachers during a 10-week telecollaborative course between students from two universities, one in the USA and the other in Spain (n = 46). The course focused on Technology-Enhanced Project-Based Language Learning (TePBLL) and was divided into different ‘technological blocks’. This article is centered around the AI technology block. The analysis is based on three exit tickets (reflection prompts) that demonstrate participants’ thoughts and changing perspectives towards AI. Through thematic analysis of the open-ended responses, this study shows that participants initially appeared skeptical before moving to tentative optimism after first studying theory and examples of the application of AI, followed by the creation of AI-based lessons and activities. The student teachers identify AI as a means to personalize and make language learning more efficient while expressing concerns related to its overuse, ethical issues and potential for undermining critical thinking and creativity. This small study looks at the evolution of the student teachers’ concepts about and perspectives towards AI-enhanced language teaching and learning before, during and after they engage in the technology block. The findings suggest that hands-on training that includes lesson design helps student teachers view AI as a complementary tool for many aspects of their teaching, although this can only be achieved through an adequate pedagogical application. Full article

China’s Yellow (Bohai) Sea bird habitat is an important ecological region. Its unique ecology and challenges provide rich resources for research and study. Our course design concept is supported by AI technology, and improves students’ abilities through innovative functions such as dynamic data support, personalized learning paths, immersive research and study experience, and diversified evaluation mechanisms. The course content revolves around the “human–land coordination concept”, including pre-trip thinking, research and study during the trip, and post-trip exhibition learning, covering regional cognition, remote sensing image analysis, field investigation, and protection plan display activities. ERNIE Bot participates in optimizing the learning path throughout the process. The course evaluation system starts from the three dimensions of “land to people”, “people to land”, and the “coordination of the human–land relationship”, adopts processes and final evaluation, and uses ERNIE Bot to achieve real-time monitoring, data analysis, personalized reports, and dynamic feedback, improving the objectivity and efficiency of evaluation, and helping students and teachers optimize learning and teaching. However, AI has limitations in geographical research and study, such as insufficient technical adaptability, the influence of students’ abilities and habits, and the adaptation of teachers’ role changes. To this end, optimization strategies such as improving data quality and technical platforms, strengthening student technical training, enhancing teachers’ AI application capabilities, and enriching AI functions and teaching scenarios are proposed to enhance the application effect of AI in geographical research and promote innovation in educational models and student capacity building. Full article

Generative artificial intelligence (GenAI) is transforming various sectors, including education. This study investigates the integration of GenAI in higher education, focusing on its potential to enhance teaching and learning. Through a series of workshops and courses delivered to university professors, it examines opportunities such as improved resource creation and challenges like ethical AI usage, proposing best practices for the sustainable implementation of GenAI in the classroom. The main objective is to analyze how the use of GenAI tools such as ChatGPT, Gemini, and Claude can improve teachers’ professional skills and the overall educational experience while ensuring ethical and responsible use. The methodology comprised a literature review and practical experimentation with university professors. Data collection involved observations, surveys, discussion forums, cooperative activities, and exercises focused on evaluating AI-generated educational resources and analyzing forum insights to identify best practices. The results highlight several opportunities around the use of GenAI in education, including improving writing, creating educational resources, supporting lesson planning, and increasing teacher productivity. In addition, significant challenges were identified, such as the ethical use of AI and strategies for detecting AI-generated text. For instance, workshops demonstrated a 30% increase in teacher confidence with GenAI tools like ChatGPT, highlighting the effectiveness of these technologies in professional development. To address these challenges, best practices for the responsible integration of GenAI in education are presented, focusing primarily on ongoing training, the establishment of institutional policies, the encouragement of responsible use, and the ongoing evaluation of impact in the educational setting. Best practices include clear ethical guidelines, prompt development techniques, and continuous professional training to ensure teachers can effectively and responsibly integrate GenAI tools into their instructional practices. These practices for the effective use of GenAI tools in education aim to maximize benefits while mitigating risks. These include the development of effective prompts for various activities and guidance on the ethical use of AI to ensure a balanced and responsible approach to the integration of GenAI in higher education. Full article

This study investigates the impact of AI-generated contexts on preservice teachers’ computational thinking (CT) skills and their acceptance of educational robotics. This article presents a methodology for teaching robotics based on AI-generated contexts aimed at enhancing CT. An experiment was conducted with 122 undergraduate students enrolled in an Early Childhood Education program, aged 18–19 years, who were training in the Computer Science and Digital Competence course. The experimental group utilized a methodology involving AI-generated practical assignments designed by their lecturers to learn educational robotics, while the control group engaged with traditional teaching methods. The research addressed five key factors: the effectiveness of AI-generated contexts in improving CT skills, the specific domains of CT that showed significant improvement, the perception of student teachers regarding their ability to teach with educational robots, the enhancement in perceived knowledge about educational robots, and the overall impact of these methodologies on teaching practices. Findings revealed that the experimental group exhibited higher engagement and understanding of CT concepts, with notable improvements in problem-solving and algorithmic thinking. Participants in the AI-generated context group reported increased confidence in their ability to teach with educational robots and a more positive attitude toward technology integration in education. The findings highlight the importance of providing appropriate context and support when encouraging future educators to build confidence and embrace educational technologies. This study adds to the expanding research connecting AI, robotics, and education, emphasizing the need to incorporate these tools into teacher training programs. Further studies should investigate the lasting impact of such approaches on computational thinking skills and teaching methods in a variety of educational environments. Full article