With the rapid advancement of global science and technology and the continuous evolution of industrial structures, innovation and entrepreneurship education in universities (dual-innovation education) plays an increasingly crucial role in cultivating students' innovation capabilities, practical skills, and entrepreneurial qualities. However, current dual-innovation education faces challenges such as outdated teaching models and uneven resource allocation, with traditional methods struggling to meet the growing demand for innovative talent in modern society. To address these issues, this paper proposes a dual-driven innovation development path for AI-enabled dual-innovation education in universities: one pathway involves AI technology-empowered intelligent and digital-assisted teaching scenarios to drive innovative development, while the other focuses on AI technology-empowered integration of artificial intelligence and dual-innovation practice scenarios to drive innovative development. By empowering dual-innovation education with AI technology, this approach aims to promote the digital transformation and deep content reform of education, ultimately improving quality and efficiency.
As interdisciplinary education gains prominence in global educational reforms, the design of high-quality interdisciplinary test items remains a challenge due to the complexity of knowledge integration, question difficulty control, and the inefficiency of manual generation. To address these issues, this study introduces Interdisciplinary-QG, an automated interdisciplinary question generation framework based on GPT-4. The framework integrates knowledge graph-enhanced retrieval-based generation with chain-of-thought reasoning and employs a structured BRTE (Background-Role-Task-Example) prompt template, enhancing both accuracy and interdisciplinary coherence. A case study in chemistry demonstrates that Interdisciplinary-QG effectively constructs interdisciplinary knowledge structures and generates high-quality test items with both depth and breadth. Experimental results show that it outperforms the general-purpose LLM ChatGLM in validity, efficiency, and interdisciplinary integration. This study provides new insights into leveraging AI for interdisciplinary education.
The development of higher-order thinking skills has become an important topic in educational practice, and the explosion of emerging technologies provides a new platform for the development of higher-order thinking in college students. From the perspective of classroom interaction, this study designs and constructs a series of classroom interaction strategies supported by the Internet and learning platform to promote the development of college students' high-order thinking skills. Through the analysis of specific indicators such as teacherstudent interaction, student-student interaction, interaction with information technology and the development of students' highorder thinking skills, it is found that the classroom interaction strategy supported by technology can improve the overall classroom efficiency, which plays an obvious role in improving students' high-level thinking ability.
With the continuous development and application of artificial intelligence (AI) technology, knowledge graphs, as an effective tool for knowledge organization and representation, have brought new opportunities and challenges for inquiry-based foreign language teaching. With Korean as a case study, this paper discusses the application of knowledge graphs in inquiry-based foreign language teaching. Through an analysis of their specific use in each teaching stage, the study evaluates their effectiveness. The findings indicate that knowledge graphs can effectively stimulate learning engagement and academic performance. However, challenges such as construction accuracy, teacher proficiency, and student adaptability remain. In response, this study proposes corresponding solutions, offering implications for improving foreign language teaching quality and educational intelligence.
As chatbots become increasingly prevalent in educational settings, their role in computer-supported collaborative learning (CSCL) remains underexplored. To bridge this gap, we reviewed 20 studies on chatbot-supported CSCL systematically. Specifically, we inductively analyzed the technological affordances, pedagogical affordances, collaboration modes, and challenges associated with using chatbots for CSCL. The results showed four key technological affordances of chatbots: ease of use, scalability, personalization, and auxiliary technology support. Four distinct pedagogical applications were identified, including 11 chatbot-facilitated collaborative activities. The main challenges associated with integrating chatbots in CSCL were categorized into design, implementation, and evaluation aspects. Based on these findings, we propose directions for enhancing chatbot-assisted CSCL, such as expanding chatbot functionalities, incorporating objective evaluation methods, and including data mining techniques. This review highlights the significant positive impacts of chatbots in CSCL and provides insights that may guide future research in both collaborative learning analysis and chatbot design.
In the current context of the rapid advancement of educational intelligence, the intelligent learning competencies of college students have emerged as a crucial standard for assessing the efficacy of education and the quality of talent cultivation. This paper focuses on the multidimensional evaluation pathways of the intelligent learning competencies of college students, with the overarching aim of constructing a comprehensive and scientific evaluation framework to propel college education towards greater precision and personalization. It systematically dissects the connotative structure of intelligent learning competencies, encapsulating the progressive relationships from basic learning abilities, through intelligent learning, and ultimately to the formation of intelligent learning competencies. Building on this, a multidimensional evaluation route is devised, incorporating aspects such as the learning process, learning outcomes, technological application competencies, as well as social and collaborative learning. Concurrently, a diverse array of evaluation sources, namely teachers, self-evaluations by students, peer evaluations, and Artificial Intelligence(AI)-assisted evaluations, are incorporated. This endeavor furnishes college educators with a novel perspective and a practical tool, facilitating the enhancement of students' intelligent learning competencies and fueling the progress of the intelligent transformation in college education.
In the face of intelligent manufacturing, new quality productivity, and AI empowerment, the digital construction of teaching resources in the experimental teaching demonstration centers is a necessity of the times and an inevitable trend in the cultivation of new engineering talents. In the context of the education digital transformation from the teaching centered to the learning centered teaching model, it is particularly important to provide students with the personalized, the hierarchical, and the modular knowledge system architecture. This article is based on the intelligent manufacturing innovation platform and uses the knowledge graph to construct typical cases of the digital experimental and the practical teaching. It deeply integrates knowledge objectives, ability objectives, and ideological and political objectives, builds a practical knowledge system from the student perspective, the cultivates their comprehensive ability of the independent learning and the innovative practice, and establishes a multi-scale entropy based the experimental and practical case teaching evaluation method to explore the reform of experimental and the practical teaching based on the knowledge graph, truly realizing the talent cultivation goal and the education mode of the new engineering concept of OBE.
As interdisciplinary integration deepens, the demand for applying engineering principles to address biological and medical challenges is on the rise. While machine learning serves as a crucial tool for processing complex medical data, the effective application of its techniques remains a significant challenge. Therefore, the “Medical Data Mining” course is designed to cultivate students' theoretical and practical skills in biomedical engineering. Current pedagogical practices are hindered by excessive teacher dominance and insufficient student interaction. To address these issues, this study introduces generative artificial intelligence technologies and interactive teaching models, integrated with the BOPPPS instructional design framework. This approach is designed to stimulate deep thinking, enhance student engagement and knowledge retention, and foster the development of critical and creative thinking skills among learners.
According to the actual needs of students majoring in vehicle engineering, this paper focuses on the application of virtual simulation experimental environment in their professional learning and ability training. Through the experimental courses such as automobile CAD/CAE/ CAM, automobile engine parts design and modeling, and automobile chassis parts design and modeling, students can simulate the real automobile design, analysis and manufacturing process in the virtual environment. This teaching method can effectively make up for the lack of resources and security risks in traditional experimental teaching, and can also improve their learning effect. The characteristics and requirements of vehicle engineering major are deeply analyzed, especially for the core experimental courses. The subject will discuss the talent demand of vehicle engineering specialty, and compare the matching degree between the industry demand and the existing experimental course system of vehicle engineering. On this basis, the subject will propose an automobile virtual simulation experimental platform architecture, which will use advanced virtual simulation technology to simulate the real automobile design, and provide an immersive and interactive learning experience. This topic will adopt a variety of teaching methods to stimulate students' initiative. These methods will be integrated into the automobile virtual simulation experimental platform, providing students with diversified learning paths and rich experimental opportunities. The teaching mode will provide students with a brand-new learning experience and help to cultivate their experimental ability and innovative spirit.
The language model ChatGPT, developed by Ope-nAI, can assist educators in managing classrooms more efficiently, enhance the learning outcomes of university students, and foster greater collaboration and communication between teachers and students on various topics. In the context of higher education talent cultivation, this paper investigates the collaborative relationship between educators and university students when utilizing ChatGPT. Under the assumption of bounded rationality, a co-evolutionary game model is constructed to analyze the equilibrium points of the replicator dynamics equations. This study delves into the synergistic relationship between teachers and students in the application of ChatGPT in education, and proposes targeted management optimization strategies for the cultivation of talent in higher education within the framework of human-machine collaborative education.
With the advancement of generative artificial intelligence, the deployment of large language models in education has become a fast-growing research hotspot. The extensive application and innovative prospects of large language models also yielded vigorous discussions in translation research and teaching. Being one of the pivotal drivers in the development of translation technology, large language models bring a significant change in translation teaching. This paper first employed a questionnaire to gather feedback for the views towards large language models from undergraduates majoring in translation in China, and then explored the application of large language models in teaching translation courses, aiming to devise the approach of using emerging generative artificial intelligence tools within translation teaching.
The increasing use of AI-powered tools such as ChatGPT and Grammarly among university students has significantly influenced their approach to improving spelling proficiency. While these tools offer a convenient alternative to traditional learning methods, concerns have arisen regarding over-reliance and diminished self-efficacy, which may impede the learning process. This study explores the opportunities and the challenges associated with AI-powered learning tools and examines students' attitudes toward traditional approaches. This paper involved a sample of 50 English department students from the Faculty of Letters and Human Sciences Dhar El Mahraz (FLDM), Sidi Mohamed Ben Abdellah University (USMBA) of Fes, Morocco. The findings provide insights into the implications of AI dependency and the balance between modern and conventional learning strategies.
This study investigates the effectiveness of an Artificial Intelligence Generated Content (AIGC)-supported lesson planning approach in enhancing teacher education students' performance in educational practice courses. A quasiexperimental pretest-posttest control group design was employed in this study, including 100 education majors divided into experimental and control groups. The experimental group utilized an AIGC-supported approach for lesson planning, while the control group followed traditional methods. Results revealed that the intervention significantly reduced test anxiety and improved test grades in the experimental group, while the control group showed no notable changes. These findings highlight the potential of AIGC technology to address the limitations of traditional instruction by providing personalized feedback, iterative refinement, and dynamic support, fostering creativity, critical thinking, and self-efficacy. This study underscores the transformative role of AIGC in bridging the gap between theory and practice, offering valuable insights for integrating AI-driven tools into teacher education.
In today's higher education, interdisciplinary integration and all-round talent cultivation have become important trends in innovation and entrepreneurship education. To enable the art design major to better define its position and responsibilities in interdisciplinary projects, it is urgent to explore effective cooperation paths. This paper, through literature review, interdisciplinary research methods and empirical research methods, analyzes the current situation and problems of art design in innovation and entrepreneurship interdisciplinary projects. Based on the author's participation in interdisciplinary team innovation and entrepreneurship projects, it explores the responsibilities and cooperation paths of art design in them, and on this basis, proposes new role positioning and cooperation methods, providing a new model and new ideas for collaborative innovation in the cultivation of new talents in colleges and universities. The research finds that art design plays a very important role in innovation and entrepreneurship interdisciplinary projects. It not only needs to jointly formulate project goals, manage teams, participate in research and development and results evaluation, but also provide innovative methods for projects, realize creative practices, optimize user experience, and participate in brand and market promotion. It runs through the entire project cooperation process, indicating that art design plays a significant role in the project.
The integration of the Internet of Things (IoT) in Physical Education (PE) has become a burgeoning area of research, driven by advancements in wearable technologies, smart environments, and data analytics. This study conducts a bibliometric analysis to explore the research frontiers, focus areas, and development trends of the Internet of Things (IoT) in the field of physical education. Using literature indexed in SCI and SSCI as of December 24, 2024, covering the period from 2014 to 2024, we employed Citespace to analyze countries/regions, publications, organizations, authors, and citation patterns. We examined 339 articles and 13 review articles, revealing a significant increase in research related to the Internet of Things (IoT) and physical education. Among them, China and South Korea dominate this field, with Linyi University as the top contributor.
Aiming to address the current problems in robot training teaching, this paper focuses on the construction of industry-teaching-innovation cases for robot training under the integration of virtuality and reality, such as the disconnection between training content and industry practice, delays in digital transformation, and the lack of training resources. Relying on the intelligent manufacturing industry academy (IMIA), the teaching model integrates virtual simulation training with practical operation to enhance students' practical skills and innovative abilities. The study introduces complex technical challenges faced by enterprises in production, combining a dual-mentor system and a collaborative resource-sharing mechanism between school and enterprises. This ensures that students can master key skills required by enterprises during training, while innovation and entrepreneurship education is integrated throughout the whole training process to stimulate students' creative thinking and entrepreneurial mindset.
In this article, we use bibliometric techniques to conduct a comprehensive analysis of academic research on artificial intelligence in education. From 393 academic articles extracted from the Web of Science database, we identified emerging trends. We highlighted key publications, mapped the knowledge framework of the field, and predicted future research directions through co-citation analysis and co-word analysis. The co-citation evaluation revealed four distinct clusters, while the co-word analysis displayed five clusters. Despite the widespread attention to AI education research in recent years, more academic efforts are still needed to achieve an in-depth exploration of its application innovations.
Smart classes have become an important trend in education today. The new generation of information technology is driving changes in the way education is delivered. Observing students' emotional state in class will helps teachers accurately knowing how students feel about receiving knowledge, in order to adjust the teaching method. However, the lack of emotional interaction between teachers and students in most realistic classrooms will makes teacher difficult to accurately evaluate students' needs and responses, which affects the quality of teaching. A classroom sentiment evaluation system is proposed based on multi-modal analysis, which adopts vision modal and audio modal to analysis students' emotional state while in class and outputs evaluation scores of classroom quality. The system includes object detection, satisfaction scores analysis, speech recognition, text analysis, and decision feedback module. Additionally, the system will constraint correlation and provides adjustment decision to further improve quality of classroom teaching. This system is verified by real classroom data and obtains a series of experimental data support, achieving satisfactory results of classroom quality evaluation.
Communication barriers significantly impact the educational experiences and social integration of students with disabilities. Personalized artificial intelligence (AI) solutions offer a transformative approach to addressing these challenges by providing tailored support that adapts to individual needs. This paper explores the design, development, and application of AI-driven tools to support communication for disabled students, with a focus on augmentative and alternative communication (AAC) systems, speech recognition, natural language processing, and real-time adaptive interfaces. Through a review of current technologies and case studies, the research highlights the potential of AI to enhance both verbal and non-verbal communication, enabling more inclusive participation in educational settings. Key considerations such as user-centric design, ethical implications, and the integration of AI tools into existing educational frameworks are discussed. The findings underscore that personalized AI solutions can bridge critical gaps in communication, fostering greater autonomy, confidence, and academic success for students with disabilities. Future directions emphasize interdisciplinary collaboration and advancing AI models to ensure equitable and sustainable outcomes in inclusive education.
This paper introduces a novel Large Language Model (LLM)-based system designed to enhance learning effect through Socratic inquiry, thereby fostering deep understanding and longterm knowledge consolidation. Recognizing the challenges of implementing Socratic pedagogy and memory reinforcement principles (as highlighted by the Ebbinghaus forgetting curve) in traditional settings, this research explores a new framework that integrates the power of LLMs with established pedagogical approaches grounded in constructivist learning theory and cognitive load theory. This system, which includes carefully designed AIdriven questioning techniques informed by the Socratic method, dynamic note management, and memory reinforcement strategies guided by the testing effect, was evaluated through a quasiexperimental classroom intervention. The study revealed that students using the LLM-based framework demonstrated significantly better understanding of complex topics, and were also more engaged with the learning process, when compared to students using traditional methods. These results highlight the potential for LLMs to transform educational practices by creating new, more effective and personalized learning pathways that reduce cognitive load and facilitate long-term retention. This research offers practical insights for educators and researchers seeking to leverage AI to promote meaningful learning experiences and to help students internalize knowledge more effectively.
