Skilled Trades Training 2030: Life of Tye

If we do it right, we might be able to evolve a form of work that taps into our uniquely human capabilities and restores our humanity. The ultimate paradox is that this technology may become a powerful catalyst that we need to reclaim our humanity.

John Hagel

In Canada, skilled trades apprentices in many careers including mechanics, welding, and electrical, as well as the various construction trades – carpentry, plumbing & heating among them – have historically relied on a training model where roughly 20% of apprentice education comes from a Technical Education and Vocational Training (TVET) institute and the other 80% comes from On-the-Job Training (OJT) under the guidance of a certified journeyperson. Traditionally, apprentices learn hands-on skills by working side-by-side with experienced mentors who demonstrate techniques, correct mistakes, and pass down knowledge from their years of field experience. This mentorship structure has been fundamental to developing the problem-solving skills, technical abilities, and confidence that a successful tradesperson has out in the industry. That is, if we have enough certified journeypersons out in the industry, to continue this.

By 2030, however, this apprenticeship model faces unprecedented challenges. Employment and Social Development Canada (2024) projects that approximately 700,000 skilled tradespeople will retire across the nation by 2028, creating a significant shortage across critical industries such as construction, manufacturing, and maintenance. BuildForce Canada (2023) underscores this urgency, noting that the construction industry alone will require over 300,000 new workers by 2030 to offset these retirements and meet increasing demands. With fewer certified tradespeople available to serve as mentors, skilled trades must evolve and may have adopted a hybrid model that combines remote mentorship with advanced technologies – specifically, Artificial Intelligence (AI), Augmented Reality (AR), and Mixed Reality (MR) – to help apprentices like Tye complete their essential OJT requirements (Chiang et al., 2022).

Morning:

Setting Up with AI Diagnostics and Remote Briefing

Tye begins his day by logging into a digital apprentice platform that leverages AI to offer a detailed rundown of the day’s Work-Learning tasks. Unlike the traditional workday, where Tye would start the day discussing repairs with his shop’s lead-hand and the other certified journeypersons face-to-face, Tye now relies on AI algorithms to analyze data from the job task list, which has been cross-referenced against similar repairs from a work-learning network connecting job sites, shops and manufactures from around the globe. Bozkurt et al. (2023) explain that such AI-driven platforms can detect patterns in data, highlighting the probability of repairs and predicting the tools, techniques and parts he might need for the day. This predictive guidance enables Tye to approach each task with a high level of preparedness, even in the absence of an onsite mentor.

In addition to AI diagnostics, Tye’s AR technology overlays interactive schematics directly onto his smartphone and head-up-display (HUD) safety goggles, highlighting the key areas and components on each vehicle and providing live data streaming directly from the machine being worked on. This digital overlay offers an experience similar to regular hands-on guidance, providing a visual roadmap for each task (Win et al., 2022). Tye’s digital platform allows him to proceed confidently with AI-supported insights, but also knowing he’ able to reach out to his government-assigned workday Remote Mentor, if a complex need arises.

Mid-Morning:

On-the-Job Training Supported by AI and AR

Once on the job, Tye is faced with a complex hydraulic repair. In an apprenticeship, just 6 years earlier, a journeyperson would likely be present to help walk Tye through each diagnostic step. Today, AR technology fills this gap by selecting appropriate service information and videos from the original equipment manufacturer (OEM), and by overlaying animated instructions and streaming data projected directly over his view, showing each step in real-time (Chiang et al., 2022). Through his AR headset, Tye receives real-time animations of the hydraulic systems schematic, guiding him through each component’s purpose, which tools to use to remove them and how to isolate the problem. This setup allows him to maintain the independence that traditional OJT aims for, despite the direct absence of in-person mentorship.

When he encounters a more complex issue, Tye connects in real-time with his assigned remote mentor – a certified journeyperson from Tye’s specific year of training who was stationed in Canada’s sunniest city; a central field service training hub and preselected by the apprenticeship agency as Tye’s, and 49 others, digital foreman. These new Off-site journeyperson positions, in 2030, are held by former technicians and TVET educators selected for the advanced digital literacy skills they possess, which were earned and developed initially during the 2020 COVID-19 pandemic and further refined through the Echo Strain lockdowns of 2027.  As described by Ipsita et al. (2022), this transition to remote mentoring enables head trainers to supervise multiple apprentices remotely via AI-driven dashboards. These dashboards track apprentices’ progression through the trade as would have a journeyperson – checking off required tasks and questioning Tye on foundational concepts before moving him on to his next training period.

Lunch with Friends:

Trades in the New Era

At lunch, Tye meets up online with his roommates – his girlfriend, Cassia, an electrician apprentice; his sister, Lorraine, a welder apprentice; and her boyfriend, Alfie, a carpenter. Each of them shares experiences of their trade-specific technologies, reflecting on how training and work is going.

For Cassia, AR technology overlays virtual circuit layouts onto her workspace, allowing her to visualize paths for conduit, connections and possible materials efficiency plans before she even begins. According to Veletsianos et al. (2024), AI-powered diagnostics enable apprentices to identify issues more precisely and quickly, supporting trades that once relied exclusively on mentor-led troubleshooting. Cassia’s mentor, also remote, can monitor her progress and step in only when advanced issues arise, optimizing time and resources.

Lorraine’s MR welding equipment, meanwhile, provides real-time feedback on her angle and technique, through haptic cues, ensuring her work is precise even without a journeyperson standing beside her. Ipsita et al. (2022) note that such AR-enhanced feedback tools are crucial for developing high-level skills independently in trades like welding, where precision is essential. As Ghosh and Ravichandran (2024) observe, emerging technologies like VR and AR are instrumental in transforming vocational education by enhancing apprentices’ abilities to self-correct and improve. Tye’s carpenter friend Alfie explains that MR overlays have transformed his work, enabling him to project virtual blueprints directly onto his workspace for precise measurements and alignment. This MR-supported setup enables him to work independently, though his mentor is only a video call away for challenging tasks (Waskito et al., 2024).

Afternoon: High-Stakes Repair with AI and Remote Mentor Support

In the afternoon, Tye’s project is a touchy engine repair. Traditionally, this would be a task for an apprentice under close mentorship by their certified co-workers, as errors in engine repairs can be costly and dangerous. But today, AI-assisted AR guides Tye in completing the job with minimal supervision. His hybrid AR safety goggle headset overlays a diagnostic flowchart onto the engine, using colour-coded indicators to highlight high-risk components based on AI-analyzed repair data from thousands of similar repairs. This advanced diagnostic capability empowers Tye to navigate the repair process with confidence, simulating the guidance he would traditionally receive from a journeyperson Bozkurt et al. (2023). Tye’s mentor views his progress through a shared AR interface, and together, they resolve the repair with AI flagging potential issues and providing recommendations for each step that Tye’s mentor even found valuable. This layered and scaffolded support system ensures that Tye’s constructivist style apprenticeship still receives a high-quality OJT experience, even as he works remotely away from this knowledgeable other (Vygotsky 1979).

Evening Reflections:

A Hybrid Apprenticeship for the Future

At the end of his shift, Tye logs his completed tasks into the digital platform, where his mentor reviews his progress using AI-generated feedback on his skill development. As the Government of Canada (2024) emphasizes, Canada’s workforce crisis calls for technological adaptations to preserve the skilled trades. With the anticipated retirement of 700,000 tradespeople, this hybrid model is essential to sustain mentorship quality even as the workforce shifts and new apprentices enter the field.

Conclusion

Skilled trades education of 2030 is lining up to be characterized by many advanced tools and technologies as well as an evolution of the coaching process used traditionally to tackle the impending issue of labour shortages. As Canada says goodbye and thank-you to hundreds of thousands of retiring tradespeople, innovations in AI, AR, and MR, will ensure that apprentices like our Tye, Cassia, Lorraine and Alfie can achieve the same hands-on expertise while working away from their mentors. This hybrid approach could still maintain the 80% OJT model so essential to apprenticeship training, empowering apprentices to develop crucial skills independently, with digital and remote supports filling the gap left by the shortage of qualified on-site journeypersons.

Moreover, the shift toward a tech-driven apprenticeship model has created an ecosystem where trades apprentices are supported by data-driven diagnostics, predictive analytics, and immersive learning environments. This combination of skills and digital directions not only solves the pressing problems of manpower but equips the apprentices with knowledge of the dynamics of the world and the use of technology at large. As Canada’s economy continues to rely on skilled trades, the integration of AI and AR will remain vital, allowing tradespeople to navigate a rapidly changing landscape with resilience and expertise.

References

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BuildForce Canada. (2023). Construction and Maintenance Looking Forward. https://www.buildforce.ca/en

Chiang, A., Yuan, S., Wang, Y., & Zhang, L. (2022). Augmented reality in vocational training: A systematic review of research and applications. Educational Technology & Society, 25(1), 123-140. https://doi.org/10.1162/edte_a_00362

Fischer, D. (2024). Digital artwork creation. Multi-image panel – 3 – Digital Apprentices in training on job site and institute[image]. DALL-E.

Ghosh, L., & Ravichandran, R. (2024). Emerging technologies in vocational education and training. Journal of Digital Learning and Education, 4(1), 41-49. https://doi.org/10.52562/jdle.v4i1.975

Government of Canada, Employment and Social Development Canada. (2024). Skilled trades shortage and the need for new recruitment. https://www.canada.ca/en

Ipsita, A., Huang, J., Villanueva, A. M., Erickson, L., & Peppler, K. A. (2022). Towards modeling of virtual reality welding simulators to promote accessible and scalable training. CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491102.3517696

Veletsianos, G., Houlden, S., Ross, J., Alhadad, S., & Dickson-Deane, C. (2024). Higher education futures at the intersection of justice, hope, and educational technology. International Journal of Educational Technology in Higher Education, 21(43), 1-12. https://doi.org/10.1186/s41239-024-00475-0

Vygotsky, L. (1978). Mind in Society: Development of Higher Psychological Processes (M. Cole, V. Jolm-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press. https://doi.org/10.2307/j.ctvjf9vz4

Waskito, W., Fortuna, A., Prasetya, F., Wulansari, R. E., & Nabawi, R. A. (2024). Integration of mobile augmented reality applications for engineering mechanics learning with interacting 3D objects in engineering education. Journal of Digital Education Technology, 6(2), 123-134. https://ssrn.com/abstract=4751061

Win, L. L., Aziz, F. A., Hairuddin, A. A., & Abdullah, L. N. (2022). Effectiveness of training methods using virtual reality and augmented reality applications in automobile engine assembly. ASEAN Engineering Journal, 12(4), 83-88. https://doi.org/10.11113/aej.V12.18009