Tsinghua University recently officially released the "Guiding Principles for the Application of Artificial Intelligence in Education" (hereinafter referred to as the "Guiding Principles"). This is the first time that the university has systematically proposed a comprehensive and scenario-based regulation and guidance for the use of artificial intelligence on campus, covering core educational activities such as teaching and academic research.

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The "Guiding Principles" consist of three main parts: "General Provisions," "Teaching Section," and "Dissertation and Practical Achievements Section." Among them, the "General Provisions" clarify the university's basic position of being "proactive yet prudent" in the era of artificial intelligence, and propose "five core principles":

  • Responsibility Principle: AI is always an auxiliary tool, and teachers and students are the main subjects of teaching and learning;

  • Compliance and Integrity: The use of AI must be disclosed, and any form of academic misconduct is strictly prohibited;

  • Data Security: It is forbidden to use sensitive, confidential, or unauthorized data to train or drive AI models;

  • Prudent Reflection: Encouraging multi-source verification to avoid mental laziness caused by over-reliance on AI;

  • Equity and Inclusiveness: Actively identifying and reducing algorithmic bias, paying attention to the digital divide.

In practical teaching applications, the "Teaching Section" supports teachers in autonomously designing AI usage methods based on course objectives, and they must clearly state the rules at the beginning of the course, taking corresponding teaching responsibility for AI-generated content. At the same time, students are encouraged to use AI reasonably to assist learning, but it is strictly prohibited to directly submit AI-generated content as homework or achievements.

For graduate students, the "Dissertation and Practical Achievements Section" further emphasizes originality and integrity standards, explicitly prohibiting the use of AI for writing, plagiarism, or forgery. Supervisors are required to bear full-process supervision responsibilities to ensure the integrity of academic training.