We might not realize it, but we rely on artificial intelligence (AI) for our pleasures and distractions. AI algorithms feed us our posts on Facebook, products on Amazon and movies on Netflix. They have become our dopamine “fixes.” Educators are wondering: Will AI chatbots like ChatGPT or MediaTek Inc’s “Taiwanese GPT” become the education equivalent of the effortless, quick “fix” for students?
Our view is that without a new “intelligent education,” we will let AI algorithms become smarter while we become dumber.
In recent months, educators have been mulling three ways to deal with this AI threat: Ban AI chatbots, do nothing or incorporate AI usage.
Although some schools have proactively banned ChatGPT, there are many ways students can use AI chatbots and evade detection. The second option ignores the real harm that AI can do to developing minds if left unchecked: Students might outsource their mental effort and learning to AI.
The third option recognizes not only the problems, but also the possibility that AI can even enhance human intelligence.
There are potential dangers of AI chatbots such as ChatGPT, Bing Chat and Google Bard. These chatbots use large-language models and advanced natural-language processing to provide linguistically accurate and creative responses.
AI chatbots can already outperform most humans in writing essays, solving math problems, summarizing texts and even writing code.
Many are seduced into outsourcing thinking and creativity to AI, but this would make us dumber in the same way that AI in other technologies constantly “recommends” a never-ending stream of distractions. We are already losing our attention and our ability to think for ourselves.
There are also AI “hallucinations.” AI chatbot responses look and sound authoritative, but can be factually wrong. A recent Microsoft report on GPT4, the latest — and paid — version of ChatGPT, puts the error rate at about 30 percent for many types of inquiry.
If students do not verify the results, they “create” misinformation one out of every three times. It is bad enough that this can be self-deceptive, but what will happen as more AI-generated content fills news reports, blogs, YouTube videos and social media posts? Misinformation could proliferate exponentially.
Everyone, including elementary-school students, should understand these pitfalls.
So, where do humans outperform AI? While AI is excellent at big data analysis and pattern recognition in stable systems, humans are much better at making inferences and dealing with new and unstable situations, relying on human judgement to interpret them.
Human judgement, while neither perfect nor evenly distributed across people, is the result of thousands of hours of implicit learning and alignment in how humans tend to interpret and react to certain situations. It depends on the rational and emotional parts of the human brain.
AI systems, in contrast, are typically programmed to find the most rational and efficient solution, regardless of its impact on humans.
“Intelligent education” would understand and leverage human and artificial intelligence.
Traditional education systems, especially examination-based ones like Taiwan’s, focus on lower-order thinking skills, such as understanding, remembering and applying. This “shallow learning” model emerged from an industrial economic model designed primarily to produce factory workers, but also to separate the wheat from the chaff to create a small class of university professors and white-collar specialists, like doctors, lawyers and engineers.
This model fails to support a knowledge economy. Most workers in Taiwan are not factory workers. Many white-collar jobs, like legal experts and radiologists, are on the brink of being replaced by more efficient and accurate AI systems.
Education needs to cultivate people who are able to navigate fast-paced and changing environments. These knowledge workers need to be cognitively efficient, adaptable, quick-learning and collaborative problem solvers who are clear communicators.
These skills require deeper learning, like problem-based learning and inquiry-based learning that encourage exploration, understanding and developing solutions.
Deep learning can be enhanced with the addition of AI chatbots that can help students not just acquire knowledge, but also learn how to ask questions and have conversations that lead toward understanding and solutions.
Students can ask AI chatbots to help them understand the problem by asking for as many explanations and examples as needed. Asking the right questions and following one’s curiosity is at the heart of the scientific process. It is also at the heart of effective AI use.
Unfortunately, Taiwan’s education system does not teach students how to ask questions. If anything, it discourages it: Curiosity often gets trammeled by exams and shallow learning.
However, asking questions is the first step toward understanding how to analyze problems. After this, students need to evaluate answers and iterate levels of knowledge to create innovative solutions.
This “intelligent chat” with AI is a skill that should be taught and practiced in schools. As students learn how to work with AI technology to enhance their shallow and deep cognitive abilities, they can simultaneously nurture their judgement skills to choose the right solutions for open-ended social situations, and also collaboration and communication skills to share these solutions.
There is no shortage of humanitarian crises: global warming, overpopulation, genetic engineering, data privacy, food supply, expansionist wars, political polarizations and AI. We cannot afford to become dumber.
We hope that the national education system will become more intelligent and help younger generations learn how to harness the power of AI to develop the superhuman intelligence needed to resolve these crises.
Nigel P. Daly has a doctorate in teaching English to speakers of other languages from National Taiwan Normal University and researches technology in language learning education. Chang Yung-Chun has a doctorate in information management from National Taiwan University and is an associate professor in Taipei Medical University’s Institute of Data Science.
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