Abstract

Generative Artificial Intelligence (GenAI) is rapidly advancing from niche applications to a ubiquitous force across industries and education. While its trajectory is not inevitable, GenAI’s increasing multimodality—text, speech, video, and immersive environments—has profound implications for vocational education and training (VET). This paper explores the potential and risks of GenAI integration into the VET sector, focusing on opportunities for workforce readiness, teacher professional development, and small-to-medium enterprise (SME) engagement. Drawing on current initiatives such as the Future Skills Organisation AI Accelerator and building on international research and policy perspectives, the paper argues that VET is uniquely positioned to lead in adopting generative AI responsibly. The study highlights the need for balanced approaches that foreground equity, ethics, and evidence-based practice.

Keywords: Generative Artificial Intelligence, Vocational Education, Workforce Skills, Teacher Training, SMEs, Multimodality, Educational Technology.

1. Introduction

Generative Artificial Intelligence (GenAI) is reshaping how knowledge is being transferred and/ordelivered, accessed, and applied in both professional and educational contexts. Unlike earlier forms of automation, GenAI introduces capabilities to create human-like text, images, speech, and multimodal outputs, which can be directly embedded into workplace and learning environments (Dwivedi et al., 2023).

Although AI is frequently framed as an inevitable technological evolution, the ways in which it is integrated remain contingent on human choices, sectoral priorities, and governance structures (Selwyn, 2022). Among the various education sectors—K-12, higher education, and adult learning—the vocational education and training (VET) sector occupies a distinctive position. VET’s responsiveness to industry demand and its emphasis on applied, competency-based skills make it particularly suited to experiment with and adopt emerging technologies.

2. Literature Review

2.1 Generative AI in Education

Research highlights that AI in education is shifting from narrow automation (grading, personalised recommendations) towards generative functions such as content creation, simulation, and multimodal interaction (UNESCO, 2023). This transition enhances flexibility in learning delivery but also raises concerns about ethics, equity, and over dependency on AI outputs (European Commission, 2023).

2.2 VET and Technological Agility

The VET sector is often described as slow-moving in governance yet agile in its capacity to integrate technologies into industry training (OECD, 2021). Partnerships with technology companies and industry associations demonstrate VET’s potential to leverage GenAI at scale. For example, the Future Skills Organisation AI Accelerator (Future Skills Organisation, 2024) provides a model of cross-sector collaboration aimed at piloting applied uses of AI.

• Education & Training Providers (TAFEs, RTOs, VET teachers)
• Industry Associations (sector-specific representatives)
• Technology Companies (e.g., Microsoft, Adobe)
• Government & Policy Makers (funding, regulation, alignment with skills strategy)

Each group contributes to the central hub, which designs, pilots, and scales AI-enabled vocational training initiatives.

2.3 Emerging Multimodal Applications

GenAI is moving rapidly beyond text-to-text systems. Multimodal AI encompassing speech-to-speech, video, and 3D immersive environments enables lifelike simulations of workplace interactions. Such tools are particularly valuable for vocational learners in health, hospitality, finance, and technical trades (Brynjolfsson & McAfee, 2023).

3. Discussion

3.1 Opportunities

• Enhanced Workforce Skills: AI-driven avatars and simulations provide learners with authentic, practice-based training environments.
• Administrative Efficiency: SMEs and educators can offload repetitive tasks to AI systems, increasing productivity and focusing humanpower on higher-value activities.
• Access to Learning: GenAI enables low-cost, scalable content creation that may expand access to vocational training globally.

3.2 Risks

• Equity and Access: Disparities in infrastructure and digital literacy risk extending educational gaps.
• Job Displacement: Automation of routine vocational roles could disrupt labour markets, particularly in low-skilled industries.
• Quality Control: The ease of generating educational content may lead to inconsistency and reduced standards if not carefully regulated.
• Ethical Challenges: Issues of privacy, bias, and accountability are particularly sensitive in vocational training where learners work with real workplace data.

3.3 Strategic Pathways Can be Taken into Consideration

Three essential pathways to balance risks and opportunities:

1. Teacher Professional Development: Training programs should equip educators with both technical proficiency and ethical frameworks for AI use.
2. SME Engagement: Targeted support must help SMEs adopt GenAI in ways that increase competitiveness without overwhelming limited resources.
3. Evidence-Based Practice: Policymakers and institutions must prioritise real-world case studies over speculative hype to guide informed adoption.

4. Conclusion

Generative AI is neither inevitable nor neutral. Its integration into vocational education depends on proactive strategies that align technological innovation with sectoral values of equity, relevance, and quality. VET’s close ties to industry, combined with its competency-based training frameworks, uniquely position it to lead in responsible AI adoption. By investing in teacher development, SME support, and transparent case-based evidence, the VET sector can shape GenAI as a tool that enhances inclusion, productivity, and lifelong learning rather than one that exacerbates risks and inequalities.

References

• Brynjolfsson, E., & McAfee, A. (2023). The Machine Economy: AI, Automation, and the Future of Work. MIT Press.
• Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., & Wamba, S. F. (2023). Metaverse, generative AI, and the future of education. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
• European Commission. (2023). Ethics Guidelines for Trustworthy AI. Retrieved from https://digital-strategy.ec.europa.eu
• Future Skills Organisation. (2024). AI Accelerator Pilot Program. Retrieved from https://futureskillsorganisation.com.au
• OECD. (2021). The Future of Education and Skills: Education 2030. OECD Publishing.
• Selwyn, N. (2022). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.
• UNESCO. (2023). AI and Education: Guidance for Policy-Makers. UNESCO Publishing.