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 However, current dual-innovation education faces challenges such as outdated teaching models and uneven resource allocation, with traditional methods struggling to meet the growing demand for innovative talent in modern society.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.
