[AIbase Report] The rise of generative artificial intelligence, especially large language models (LLMs), is changing the landscape of knowledge acquisition at an unprecedented speed. Professor Patrick Dode from the School of Business at the University of Auckland wrote in The Conversation that as AI provides knowledge in a low-cost and efficient way, the value of universities as traditional sources of knowledge is being challenged. He believes that universities must re-examine their core functions to adapt to this new era driven by AI.

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Professor Dode analyzed that universities have long followed the principle of "knowledge scarcity," proving students' ability to acquire knowledge through exclusive courses and degree certificates. However, advances in AI technology have greatly lowered the barriers to accessing professional knowledge. LLMs can not only retrieve facts but also explain, translate, and summarize, making the previously "scarce" knowledge lose much of its value. This change has already been reflected in the labor market, with entry-level job vacancies in the UK decreasing by about one-third since the launch of ChatGPT, and some states in the US even removing degree requirements for public sector positions.

However, Dode emphasized that not all knowledge is equally devalued. While the value of basic knowledge is declining, tacit knowledge, such as teamwork, ethical judgment, creativity, and the ability to solve complex problems, remains a scarce resource that AI cannot replace. He pointed out that the focus of future education should shift from transmitting information to cultivating these key human skills.

To address this challenge, Professor Dode proposed four transformation suggestions for universities:

  1. Assessment Transformation: Shift the focus of classroom assessments from mere knowledge memorization to evaluating judgment and synthesis abilities.

  2. Experiential Learning: Invest resources in developing mentorship programs, simulating real-world scenarios, and using AI as a tool for ethical decision-making research.

  3. Micro-Credentials for Skills: Create micro-credentials targeting key capabilities such as collaboration, self-directed learning, and ethical judgment.

  4. Deepening Industry-Academia Collaboration: Universities provide professional knowledge, companies offer real case studies, and students focus on verifying and refining ideas, jointly cultivating versatile talents adaptable to the future market.

Dode concluded that if universities want to remain competitive in the future, they must transition from a mere source of information to a center of judgment, teaching students how to think collaboratively with AI, rather than competing with it?