The rapid development of AI is forecasted to bring about a profound transformation in the workplace, poised to impact the future for generations to come significantly. In light of this, the Yonglin Education Foundation and CW Lab have collaborated to produce the first-ever "Survey on Future AI Applications and Regulatory Exploration: Listening to the New Generation of AI Education" for Taiwanese university students.

The initiative aims to understand the aspirations of this new generation regarding AI education and to outline further how to meet the challenges and opportunities brought by AI in this unprecedented era.

CommonWealth Magazine partnered with the Yonglin Education Foundation, which has been dedicated to improving the quality of AI education in Taiwan and pioneering interdisciplinary courses in recent years.

Through this questionnaire, they sought to profile the "Empowered Generation" and provide a reference for future AI education promotion. According to the survey results, 73% of young people identify AI as an essential skill for career development within the next five years. Beyond believing that learning AI can help them adapt to future workplace demands, 71.8% of the youth feel that future talent must possess significant "AI literacy"—meaning an understanding of AI operational principles, ethics, and regulations—highlighting this as a critical area for future talent cultivation.

Promoting AI General Education with Diverse Strategies: Enabling Students of All Majors to "Use" AI

Addressing these findings, Dr Yong-Chen Chang, Dean of the School of Law and the Department of Law at Soochow University, noted, "In the past, the public concept of law was mostly stuck on penalties and compensation. However, legal norms are mechanisms to reduce transaction costs and create maximum social wealth; rather than just preventing abuse, they are about promoting benefits. Facing the surging impact of AI, we can use legal norms—specifically an AI Basic Law—to take preventative measures."

He emphasized that AI legal education is not solely for law students. "It is even more important for non-law students to understand this," Dr Chang stated. Consequently, in the classroom, he encourages students to use law to understand technology and use technology to expand the law. "This is not just to ensure students know how to use AI, but to hope they can 'understand the risks and values behind it.' Only in this way can we cultivate talent for Taiwan that is truly internationally competitive and capable of flexibly responding to future challenges."

Regarding the high recognition of AI education in the survey, Dr Yong-Chen Chang also observed, "Students are full of interest in AI, yet their grasp of its application principles, ethical issues, and legal risks is not comprehensive enough." He noted that non-STEM students often face issues of "tool anxiety" and "fragmented knowledge," while teachers frequently struggle to find the appropriate language to bridge the gap between technology and law or the humanities. To reinforce these areas, Soochow University has adopted a diverse strategy. First, in curriculum design, they are striving to promote general AI education courses and have established courses within the Law School, such as "Artificial Intelligence Law" and "Cross-Domain AI Applications." "We are also actively engaging with industry and academia," Dr Chang added, "collaborating with entities like Microsoft and the Lee and Li Academy to organize academic forums and workshops, allowing students to engage with frontline issues and cases."

Driving Talent Transformation through Technology and Tools to Build Valuable Future Capabilities

Given the diverse forms of AI education, Professor Jay Lee, a prominent data expert, Chair Professor at the University of Maryland, and Director of the Industrial AI Centre, stated that Industrial AI contains three significant elements: Data, Domain, and Discipline. "Historically, AI focused on the data itself—using massive amounts of data to train machines," Lee explained. However, in industrial applications, more is required.
Lee noted, "AI is cognitive science, primarily enabling software to perform deep learning, facial recognition, and human-machine interaction. Compared to traditional AI, which does not guarantee the reliability of outcomes—since results vary with different input data and users—Industrial AI emphasizes reliable prediction." Therefore, he advocates that Industrial AI should concentrate on professional knowledge (domain expertise), ensuring that every use of AI yields reliable results suitable for safety-critical industries such as automotive, subways, and machine tools.
In his innovative manufacturing courses designed to cultivate Industrial AI talent, Lee believes that beyond transmitting professional knowledge, it is crucial to foster problem-solving abilities in a rapidly changing era. The approach involves students mastering self-learning projects to refine their expertise quickly; professors then provide data and tools, allowing students to solve problems previously faced by enterprises. Subsequently, students return to enterprises to gather their own data and apply the tools taught in class to address current corporate issues. Finally, they pass on what they have learned to others, enabling companies to successfully use Industrial AI to accelerate productivity, competitiveness, and innovation.

The survey also showed that 71.2% of respondents from public schools agree that AI technology can promote Taiwan's industrial transformation and upgrading, a figure significantly higher than that of private school students and others. Addressing this disparity, Dr Yong-Chen Chang candidly pointed out, "In terms of resources, public schools indeed have an advantage over private ones, so the level of agreement is higher. Soochow's approach, in addition to establishing the School of Big Data Management five or six years ago—a national first—also involves a deep gratitude to the Yonglin Education Foundation and Professor Chang Li-ching of the Foundation for the Study of Law and Policy in Artificial Intelligence. They provided Soochow University with a rare opportunity to jointly launch a 16-week series course titled 'Taiwan Innovation: Future AI Application and Regulatory Exploration.'

The birth of this course signifies Soochow's high regard for "AI and Law" issues and reflects its long-standing efforts in promoting interdisciplinary AI education. "The 'Future AI Application and Regulatory Exploration' series is planned with a complete 16-week structure, spanning the impact of AI education on the future workplace, AI basics and applications, and the impact of AI on the legal system and governance," said Dr Yong-Chen Chang. "We hope students gain not just knowledge, but also cultivate critical thinking and integration skills through discussion, simulation, and case analysis. These are the most precious' future capabilities' we hope to bring to our students."

Domain Knowledge is Key: Shifting from a "Contract Manufacturing" to a "Leading" Mindset

Additionally, 70% of the youth are optimistic that AI development will aid Taiwan's global competitiveness and facilitate industrial transformation and elevation. Meanwhile, 60% of the youth believe that, looking longer-term, AI education should be incorporated into Taiwan's mid- to long-term education policy.

Professor Jay Lee summarised the direction of AI development as "ABC": Algorithm, Big Data, and Computing. In future competition, there are three distinct tracks.

The first track is based on ABC plus Data Centres and Electricity, forming AI Infrastructure. "The real players in this category are high-end enterprises, and nations, corporations, and investors support the track. The future direction here is not yet certain," Lee said.

The second track shares the same ABC foundation. "The difference lies in D for Deep Data Computing and E for E-Process—essentially, how to make AI products," Lee pointed out. "Taiwan comes from a manufacturing background and excels at making devices, but lacks 'AI thinking.' If Taiwan can shift from a 'contract manufacturing' (OEM) mindset to a 'leading' mindset, transforming valuable data into resources that can be modeled, learned from, and inherited to build a data ecosystem, it can use the massive data generated by these systems to influence and conduct next-generation design and services. This is the direction for value transformation."

As for the third track, the ABC remains the same, but with the addition of Domain and Enterprise, forming what is known as Industrial AI. "This requires long-term cultivation; there are no short-term victories. What needs to be nurtured here is strong AI Systems Engineering. This curriculum is currently very scarce," Lee stated. "Therefore, a new mindset is needed for the future, combining data, domain knowledge, and discipline to establish and maintain product lifecycles. We must find ways for AI to 'land' (be practically applied). These are areas that need to be discovered. We should not blindly follow traditional paths but explore new territories for AI applications, because the pace of industrial transformation is accelerating."

Read the full article here: https://futurecity.cw.com.tw/article/3715