The speedy development of emerging technologies brings in their demand for changing training needs. It is focused on how two upcoming technologies (Wearables/Smart Glasses as well as Artificial Intelligence/Internet of Things (AI/IoT)) will change the future of training programs. The paper will delve deeper into both, seeing the advantages and shortfalls emerging among learners and trainers. The paper will also project the potential of these technologies to be used within manufacturing in the coming years. Finally, implications for the evolving role of trainers in a high-tech workplace will be discussed.
Wearables/Smart Glasses
For wearable devices, such as smartwatches and smart glasses, the promise lies in the concept of Just-in-Time learning using Augmented Reality to improve hands-on training and real-world tasks by directing interactive digital content—or through the use of optical head-mounted displays—in the users’ range of eyesight. In manufacturing, the trainees can achieve step-by-step visual instructions, repair guidance, or troubleshooting tips and work on equipment using smart glasses. This ‘context-aware’ learning methodology naturally cuts down time as the delays from searching for printed material or pausing tasks to view on stands or separate mobile screens can be avoided. As of now, though, several challenges have come up to keep in check the effectiveness of wearables for wearable work training on a grand scale. However, on this list are the issues of unreliability at the very top—like if essential aids vanish at precisely the wrong time, if latency issues crop up out of the blue, if the display provides access to other glitches, or if network failure undermines the learning process altogether. This amounts to an unacceptably high risk of simply making productivity-focused training unworkable. Other challenges concern user comfort: flashes and sounds in an ample library light are wearable and can be perceived as hot, oversized, and intrusive. “It, of course, will introduce complex issues of privacy and monitoring capabilities, given handing supervisors such a ‘window’ onto employees’ worlds could easily shatter trust if not carefully regulated.
Artificial Intelligence/IoT
The potential strength in artificial intelligence and the Internet of Things could bring up new data-enabled models for workforce training personalized within their potential. In so doing, if AI systems were embedded inside ecosystem manufacturing equipment, tools, or workstations as a general rule, then sensors and other industrial Internet of Things (IoT) devices could, indeed, make it possible to pull in data concerned with operational and performance at a time of high granularity. Combining those data streams, the AI processes the patterns with machine learning algorithms that improve over time and make practical, action-oriented results from the analyses. All the spells mentioned above promise that automatically created personalized training plans and procedures are much the same in detail as specific strengths or weaknesses and preferences of individual employees as learners. Besides, technology from the domain of IoT allows connectivity in virtual/augmented reality simulations to effectively integrate actual performance in production. This type of guidance would be softly done through highly contextualized nudges trained by real-world action, for instance, their GPS suggesting how to reach a place during a walk. On the other hand, if scaled appropriately across organizations, AI and IoT combined promise to drive step changes in workforce capacity through radically personalized, adaptive learning.
Implications for Trainers
As employees increasingly use the tools new technologies offer, the traditional role of a trainer has naturally moved to become more of a facilitator and advisor to a lot of self-paced, interactive learning. A point, in this case, is where the trainers lead the employees as they make their way through simulations or virtual exercises and, increasingly, augmented reality tools strongly supported with AI and data analytics, spilling over to learning that highlights just-in-time coaching and collaborative problem-solving. The technologies augment trainers with the capacity and new capabilities to take up more profound technical roles in embedding tools into the curriculum, working through how these are applied, and using data to make regular updates. The curriculum is redesigned using new immersive and adaptive modalities that consider the continuously considered and adapted knowledge. The critical skills broaden from knowing about a given field of work to being literate in various other skills, including digital literacy, change management, and design thinking. Successful guidance of this transformation takes ongoing development and learning for the trainers to maximize the potential in new learning and performance augmentation through technology.
These technologies will essentially be fundamentally rendered from ground-breaking wearable devices, smart glasses, AI, and IoT that are all strongly invigorated by high-class analytics to open new and exciting vistas for impactful hands-on training with very high levels of personalization. This technology is up-and-coming to improve manufacturing safety, quality, and efficiency. Apart from several lingering barriers related to equipment costs, technical competence, and preserving some form of collaboration, there are still some remaining barriers. However, with considered change management that addresses employee concerns, it can leverage these immersive learning tools, informed by performance data, which is increasingly a part of application suites, to tailor workforce development more effectively. Continuous evolution will further personalize training for each worker’s development of the whole ability. Thoughtful planning to reap the most benefit from learning and operations as technology marches forward to reopen the work site.
References
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216–1216. https://doi.org/10.3390/educsci13121216
Patel, V., Chesmore, A., Legner, C. M., & Pandey, S. (2021). Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity. Advanced Intelligent Systems, 4(1), 2100099. https://doi.org/10.1002/aisy.202100099
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