Research Article
A Two-Dimensional Framework for AI Integration in Tourism Management Curricula: Design and Practical Pathways
Yanchun Jin
,
Bing Guan*
Issue:
Volume 11, Issue 6, December 2026
Pages:
227-236
Received:
15 September 2026
Accepted:
4 October 2026
Published:
10 November 2026
DOI:
10.11648/j.her.20261106.11
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Abstract: As the digital transformation of higher education accelerates, improving the digital level of traditional courses and the application capabilities of digital technology of teachers and students have become the core content of the current teaching and curriculum reform. The tourism management major is one of the most distinctive majors among management majors. Since its development in the 1980s, it has cultivated a large number of talents for the development of China's tourism industry. With the intelligent upgrade of the cultural tourism industry in recent years, common problems in the teaching of professional tourism management courses, such as the disconnect between theory and practice, rigid teaching models, and the lag in case updates, have become increasingly obvious. This issue fails to satisfy the talent demands of modern cultural tourism industry development. In this context, this paper adopts constructivist learning theory and the technology acceptance model to explore the integration path of AI technology from two dimensions: the major level and the curriculum level. Taking Tourism Economics, a core course for the tourism management major as an example, it puts forward concrete practical pathways. According to the research findings, the deep integration of AI technology can effectively address the core dilemma in traditional course teaching and bridge the gap between theory and industrial practice. Meanwhile, the two-dimensional system at the major and curriculum levels can facilitate the shift of teaching objectives from knowledge transmission to competency development. The findings offer practical pathways for AI-integrated courses, not only for the tourism management major, but also for other related majors.
Abstract: As the digital transformation of higher education accelerates, improving the digital level of traditional courses and the application capabilities of digital technology of teachers and students have become the core content of the current teaching and curriculum reform. The tourism management major is one of the most distinctive majors among managem...
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Research Article
Exploration of a Paradigm for Building LLM Knowledge Corpora for Computer Science Courses: Computer Architecture as an Example
Qin Guofeng,
Jia Yibo,
Xu Ting,
Qin Minchuan*,
Li Qihang
Issue:
Volume 11, Issue 6, December 2026
Pages:
237-245
Received:
2 October 2026
Accepted:
26 October 2026
Published:
10 November 2026
DOI:
10.11648/j.her.20261106.12
Downloads:
Views:
Abstract: Artificial intelligence is driving a new wave of educational transformation and profoundly reshaping knowledge acquisition, course teaching, and learning assessment. Computer science courses are both an important foundation for the development of artificial intelligence and a key category requiring adaptation as large language models are introduced into discipline-specific education. Compared with general-purpose question answering, computer science courses are characterized by complex knowledge hierarchies, tightly interconnected concepts, explicit computational reasoning, and substantial practical requirements. Building knowledge corpora tailored to computer science courses is therefore an important foundation for the deeper integration of artificial intelligence into course instruction. Taking the Computer Architecture course as an example, an approach is systematically discussed a method how to constructing LLM knowledge corpora for computer science courses, focusing on four main stages: establishing a chain-of-thought reasoning framework, building a course knowledge corpus, conducting domain model training, and generating questions and answers. By organizing the course knowledge system, knowledge-point reasoning trees, question-type structures, and reasoning procedures, course knowledge is transformed into training data with an explicit structure and clearly defined relationships. The paper further examines the value of such data for question answering, test-paper generation, and online tutoring, providing a practical reference for the construction of LLM knowledge corpora for computer science courses.
Abstract: Artificial intelligence is driving a new wave of educational transformation and profoundly reshaping knowledge acquisition, course teaching, and learning assessment. Computer science courses are both an important foundation for the development of artificial intelligence and a key category requiring adaptation as large language models are introduced...
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