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May 28, 2026Journal of Education and Information Technology0 citationsOpen Access

AI-Empowered Interdisciplinary Teaching of AI Fundamentals

SLShaonan LiuXWXiaotong Wu

Key Points

  • The aim is to develop an AI-empowered interdisciplinary curriculum that enhances innovation literacy among students.
  • Developed a 'core module + cross-disciplinary application module' curriculum structure.
  • Implemented AI-enhanced interdisciplinary project-based learning and multidimensional assessment systems.
  • Conducted evaluation methods and data analysis to measure curriculum effectiveness.
  • Enhanced interdisciplinary innovation literacy as reported by improved student engagement and learning outcomes.
  • Implemented assessment reforms showed significantly better student performance metrics post-intervention.
  • Positive feedback on the flexible curriculum structure from participants, suggesting effective teaching practices.

Abstract

Artificial intelligence is profoundly reshaping the landscape of higher education talent cultivation. However, current university-level Fundamentals of Artificial Intelligence courses commonly suffer from content confined to technical principles, instruction detached from real-world application scenarios, and monolithic assessment methods, making it difficult to effectively cultivate students’ interdisciplinary innovation literacy. Drawing on constructivist learning theory and the frontiers of generative AI in education, and grounded in the curriculum reform practice at Alibaba Business School, Hangzhou Normal University, this paper proposes and constructs a new paradigm of “AI-empowered interdisciplinary integrated teaching”. Centered on the core idea of “teaching AI with AI”, the paradigm features a flexible “core module + cross-disciplinary application module” curriculum structure, systematically implements AI-enhanced interdisciplinary project-based learning, and establishes a multi-dimensional dynamic assessment system focused on “interdisciplinary innovation literacy”. Through a closed-loop pathway of “theoretical construction – curriculum redesign – teaching practice – assessment reform – iterative dissemination”, this paper details the practical outcomes, evaluation methods, and data analysis results, and draws conclusions and future research directions. It aims to provide an actionable and replicable solution for AI general education reform in higher education.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a17dc063fad632b0f9d8bbbhttps://doi.org/10.57237/j.jeit.2026.02.002
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