Bridging the AI Skills Gap: The Future of Tertiary Education and Training in Southeast Asia

The rise of artificial intelligence (AI) has been transformative, reshaping industries and creating demand for a new generation of skilled professionals. Yet, as the World Economic Forum highlights, access to AI skilling remains inequitable, leaving significant gaps in talent pools worldwide. This disparity is particularly pronounced in Southeast Asia, a region with immense growth potential but uneven access to quality education and training. For tertiary institutions and training colleges, addressing this inequity is critical. However, achieving it requires a deep understanding of the differences between education and training, and a reevaluation of current AI skills acquisition methods.

Education vs. Training: A Key Distinction

To effectively address AI equity gaps, we must first distinguish between education and training.

1. Education: This involves a broad, foundational learning process that builds critical thinking, adaptability, and problem-solving skills. Tertiary institutions, such as universities, focus on providing students with the theoretical knowledge needed to understand and innovate within a field. In AI, this would include studying machine learning algorithms, data ethics, and the mathematics underlying neural networks.

2. Training: By contrast, training is skill-specific and geared towards immediate application. Training colleges and programs aim to equip learners with practical, job-ready competencies, such as using AI tools, coding in Python, or operating platforms like TensorFlow.

Both education and training play crucial roles in building a skilled AI workforce, but they address different aspects of the AI skillset. The challenge lies in finding the right balance between them to create a comprehensive talent pipeline.

The Current State of AI Skills Acquisition

Today’s AI skills acquisition landscape is heavily skewed toward training, often delivered through bootcamps, certifications, and short-term courses. While these programs effectively teach specific tools and frameworks, they often fall short in preparing individuals for the broader challenges of AI, such as ethical decision-making, creative problem-solving, and innovation.

For instance, a training program may teach a data scientist how to clean data and build a predictive model. However, it might not prepare them to critically evaluate bias in the data, consider the societal implications of their model, or adapt their approach when faced with unforeseen challenges. This narrow focus on technical skills can limit long-term career growth and innovation.

In Southeast Asia, the emphasis on training over education is particularly pronounced. Many countries in the region prioritize rapid skilling programs to meet the immediate demands of the job market. While this approach has its merits, it risks creating a workforce that is adept at using AI tools but lacks the depth of understanding needed to drive the field forward.

The Impact on Tertiary Education in Southeast Asia

Tertiary education institutions in Southeast Asia face significant challenges in adapting to the evolving demands of AI. Historically, many universities in the region have been slow to update curricula, leaving graduates with outdated skills. For example, while industries now require expertise in AI ethics and generative AI, university programs may still focus on traditional programming languages or legacy systems.

This lag risks making tertiary education irrelevant in the AI era. However, with the right strategies, universities can reclaim their position as leaders in AI education.

Key Areas for Transformation

1. Integration of AI into Core Curricula

Universities must integrate AI as a foundational subject across disciplines, not just within computer science departments. Business, healthcare, engineering, and even humanities students should gain exposure to AI concepts, enabling interdisciplinary innovation.

2. Partnerships with Industry

Collaborations between universities and tech companies can bridge the gap between theory and practice. For instance, partnerships could offer students access to cutting-edge tools, real-world projects, and mentorship from industry experts.

3. Focus on Lifelong Learning

AI is a rapidly evolving field, and the skills needed today may be obsolete tomorrow. Universities should adopt a lifelong learning model, offering modular courses and certifications that professionals can pursue throughout their careers.

The Role of Training Colleges

Training colleges have a unique opportunity to complement tertiary education by focusing on practical, job-oriented skills. However, to truly address AI equity gaps, they must also evolve.

Strategies for Training Colleges

1. Expanding Access to Underserved Populations

Training colleges should prioritize inclusivity, offering affordable programs and reaching marginalized communities. Mobile-based learning platforms, for example, can make AI training accessible in rural areas of Southeast Asia.

2. Teaching Beyond Tools

While tool-specific training is important, colleges should also teach foundational concepts, such as data literacy and ethical AI practices. This broader focus will prepare learners to adapt as technologies evolve.

3. Emphasizing Collaboration Skills

AI projects often involve interdisciplinary teams. Training programs should include modules on communication, teamwork, and project management to ensure graduates can thrive in collaborative environments.

Guidance for Institutions and Policymakers

To effectively address AI equity gaps, both tertiary institutions and training colleges need support from governments and industry leaders.

1. Invest in Infrastructure

Policymakers should invest in digital infrastructure to ensure all institutions have the resources needed to deliver high-quality AI education and training.

2. Standardize AI Curriculum Frameworks

Establishing national or regional standards for AI education and training can ensure consistency and quality across institutions.

3. Incentivize Industry Collaboration

Governments can offer tax incentives or grants to companies that partner with educational institutions to develop AI talent.

4. Promote Awareness of AI Careers

Many students and professionals are unaware of the diverse career opportunities in AI. Public awareness campaigns can inspire more people to pursue AI education and training.

Examples of Success and Shortcomings

• Success: Singapore’s SkillsFuture Program

Singapore’s SkillsFuture initiative offers a model for promoting lifelong learning and AI skilling. The program provides funding for AI courses and certifications, making them accessible to a wide audience.

• Shortcoming: Limited Access in Rural Areas

In contrast, many rural areas in Southeast Asia lack access to high-quality AI training. For example, while urban centers in Indonesia boast advanced tech training hubs, rural communities often rely on outdated curricula.

A Holistic Approach to AI Skills

The rise of AI presents both challenges and opportunities for Southeast Asia’s tertiary education and training sectors. By embracing a holistic approach that balances education and training, institutions can equip learners with the skills they need to thrive in the AI era.

For universities, this means integrating AI across disciplines and fostering lifelong learning. For training colleges, it involves expanding access, teaching beyond tools, and emphasizing collaboration. With the right strategies, Southeast Asia can build an AI-ready workforce that drives innovation, inclusivity, and equitable growth.