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ABSTRACT
The use of AI is increasing to guide tasks, optimize workflow and support safety of the workers. But these systems often remain vague and difficult to comprehend by the workers due to lack of practical training and literacy. The goal is to understand how these systems can support human judgment, safety, accessibility, and inclusion rather than just task automation. There is scope of developing prototypes that are industry and task oriented to test in real workplaces. This study aims to explore how workers can experience AI systems (through prototypes and real execution) as transparent, autonomous work companions with accountability, transparency and bias awareness. Also, the aim is to investigate how interaction design can support work transparency, inclusion, and user empowerment.
Industrial work like inspection, equipment maintenance, and quality control can be physically demanding that depend on expertise that are difficult to encode into automated systems. If AI tool is introduced in this context with augmented and mixed reality workers can be trained to understand defect-detection algorithms, and decision-support systems. This can extend worker’s perception and reduce repetitive burden. However, they can also undermine trust, autonomy, and job satisfaction if not designed from worker’s perspective.
The research combines interaction design, accessibility studies, digital culture, and human-AI collaboration to contribute to the emerging discussion on how AI can be integrated into work environments without reducing human agency. The findings are expected to present data on sustainable workplace design by showing collaborative AI agent’s productivity and workers’ diversity, inclusion, and autonomy.
Keywords: Collaborative AI; Human–AI interaction; Digital twins; Virtual environment prototyping; Workplace AI; Human-centered design; Role-based training; Industrial fieldwork; Accessibility; Inclusive design; Human autonomy; Trust in AI; Augmented reality; Wearable technologies; AI-supported workflows; Defect inspection; Fault detection; Socially sustainable work; Transparent AI; Multimodal interaction