Summary of Key Points
This article focuses on the development of "artificial intelligence (AI) + education," emphasizing the need to embrace technology while upholding the fundamental principles of education. It discusses five critical issues:
1. The risk of primary and secondary school students abusing AI to bypass the learning process, which requires limiting or even banning the use of AI on student devices;
2. Traditional evaluation methods (such as closed-book exams) serve as an important defense against the weakening of skills in the AI era;
3. The potential for ideological risks associated with AI, suggesting the establishment of a whitelist of trusted educational AI models;
4. Solving the issue of insufficient computing power in education systems through a shared computing resources alliance;
5. The danger of AI being used merely as a tool for exam preparation. The article concludes by stressing the importance of balancing technological advancement with prudent governance to ensure that AI truly contributes to building a strong educational foundation.
Detailed Analysis
1. Using AI for Homework: Beware of Shortcuts That Undermine Fundamental Skills
For adults, AI is a tool for efficiency, but primary and secondary school students are in the process of developing learning habits, thinking patterns, and self-discipline. If they rely on AI to complete assignments—such as writing essays or solving math problems—they miss out on the essential steps of understanding, memorizing, and reasoning. This can lead to a lack of genuine knowledge acquisition. For example, Westerners' reliance on calculators has led to a decline in basic mathematical skills, and similarly, powerful AI tools may weaken children's foundational abilities, resilience in thinking, and independent expression. Therefore, the use of AI on student devices in these classrooms should be strictly restricted or prohibited, as it cannot be assumed that students will use it responsibly.
2. Are Closed-Book Exams More Important in the AI Era? They Serve as a Defense Against Cheating
AI can easily generate answers to homework, making it difficult to determine whether assignments are completed by the students themselves. In this context, traditional methods like closed-book exams, written tests, and oral presentations become crucial for verifying students' understanding of basic concepts and principles. Without these practices, so-called "innovative skills" are merely superficial. Thus, in the AI era, these evaluation methods are not outdated but need to be reinforced to assess students' true abilities.
3. Could AI Answers Be Misleading? Establishing a Trustworthy Model Whitelist
AI models may provide seemingly authoritative answers, which students might accept as truth. However, tests have shown that foreign models (such as ChatGPT) can make mistakes on fundamental questions (e.g., whether Taiwan is an independent country), and some domestic models also lack rigor. Moreover, AI training data may be influenced by Western knowledge systems, potentially shaping students' values. To mitigate this, educational systems should establish a whitelist of trusted models that have undergone rigorous review and align with positive values.
4. Insufficient Computing Power in Universities? Create a Shared Pool for Efficiency
The implementation of AI in education requires substantial computing power, but individual universities or regions often face high costs and low utilization rates (some schools have excess capacity, while others struggle). The approach used for sharing large-scale equipment under the "211 Project" can be replicated: the government should coordinate and integrate the computing resources of key universities and research institutions to create a shared pool that enables all educational institutions to access these resources at lower costs or for free, thus saving money and meeting the needs of AI-based education.
5. Turning AI into an Exam-Preparation Tool? Preventing Over-reliance on Algorithms
Research by Good Future shows that AI-powered learning devices have significantly increased company revenue (33.7%) and net profit (527.4%). While AI products themselves are not inherently problematic, their use for targeted practice, question prediction, and score improvement can turn exam preparation into an algorithm-driven process, which is more efficient than traditional methods but still traps students in a focus on exams. Educational authorities must not solely blame companies (which naturally seek market demand) but take proactive steps to prevent the overuse of AI for exam preparation.
Conclusion
"Artificial intelligence + education" is not just about combining technologies; it requires finding a balance between leveraging technology to improve efficiency and preserving the essence of education. We must use AI to enhance learning outcomes and empower students, while preventing it from replacing the learning process, distorting values, or becoming a tool for exam preparation. Only in this way can AI truly contribute to building a strong educational system rather than creating new problems.