AI drives shift from human–machine interaction to true cooperation

The integration of artificial intelligence (AI) into industrial, digital, and collaborative systems is accelerating a historic shift in how humans and machines work together. This transformation, driven by rapid progress in AI models, sensing technologies, and multimodal interaction, is reshaping industries from manufacturing and robotics to healthcare and communication systems.

In a recent editorial published in Applied Sciences, researchers present a comprehensive overview of these developments in their paper “From Human–Machine Interaction to Human–Machine Cooperation: Status and Progress”.

From interaction to cooperation: The paradigm shift

The editorial highlights a significant evolution from basic human–machine interaction (HMI) to advanced human–machine cooperation (HMC). Traditional HMI frameworks focused on enabling machines to communicate or respond to commands. In contrast, HMC systems are designed for goal-sharing, adaptability, and collaborative problem-solving in real time.

This shift is evident in emerging fields such as collaborative robotics, bioinspired systems, and immersive environments like the Internet of Things (IoT), the Metaverse, and extended reality (XR). These systems rely on complex integrations of robotics, computer science, psychology, and engineering to predict human intentions, plan tasks, and adapt dynamically to changing environments.

For industries like manufacturing, these advancements mean robots can now assist human operators seamlessly, using sensors and machine vision to detect context and predict human actions. In safety-critical environments, algorithms powered by deep learning and machine vision can predict hazards and adjust operations instantly, creating safer and more efficient workplaces.

The integration of AI also strengthens context-awareness, enabling production systems and service platforms to dynamically adapt to shifts in human behavior or operational requirements. This adaptability is critical for Industry 5.0 applications, where collaboration between humans and intelligent machines is not only expected but essential for achieving higher levels of efficiency and flexibility.

Key research directions and contributions

The editorial reviews six pivotal studies included in the Special Issue, each addressing unique challenges in the journey toward seamless human–machine cooperation. Together, they showcase the interdisciplinary nature of this transformation and highlight the range of applications driving the field forward.

One study explores deep neural networks for object detection, benchmarking supervised, self-supervised, and transfer learning methods. The findings confirm that transfer learning with advanced architectures such as YOLOv8 offers the highest accuracy for image-based recognition tasks, reinforcing its utility for diverse industrial and operational contexts.

Another contribution demonstrates the potential of AI-driven healthcare tools. Using clinical data from pediatric patients, researchers developed an AI-based diagnostic system for real-time detection of respiratory conditions. By integrating advanced signal processing with multiple classification algorithms, the platform shows promise for early screening and cost-effective diagnosis in clinical settings.

In decision-making environments where humans and algorithms interact, one study introduces a simulation-based framework that models human and AI rationalities. Using operational data from New York City’s bike-sharing system, the researchers demonstrated how misalignments between algorithmic incentives and human decision-making can create inefficiencies — a finding that underscores the need for better integration of behavioral insights into AI systems.

The Special Issue also includes advancements in EEG-based emotion recognition, where hybrid feature sets combined with artificial neural networks delivered high accuracy across time, frequency, and spatial domains. This breakthrough has potential applications in adaptive systems, allowing machines to respond more effectively to human emotional states in real time.

In the realm of industrial planning, the Integrated Multilevel Planning Solution (IMPS) was introduced as a flexible, human-centric platform for small and medium-sized enterprises. By connecting multiple software platforms, IMPS facilitates multiuser and multivariant planning, helping organizations overcome resistance to digital transformation while enabling scalable, efficient operations aligned with Industry 4.0 and 5.0 principles.

Lastly, a longitudinal content analysis of AI research in communication studies from 2006 to 2022 revealed steady growth in academic interest and the urgent need for updated theoretical frameworks. This research highlights the growing cultural, political, and societal dimensions of AI, emphasizing the importance of interdisciplinary approaches to address emerging ethical and governance challenges.

Future directions for human–machine cooperation

The transition to human–machine cooperation is more than incremental progress - it is a fundamental rethinking of how intelligent systems are designed and deployed. Two areas in particular are expected to drive the next generation of HMC technologies: agentic AI and foundation models.

Agentic AI introduces reasoning and goal-driven behavior, enabling machines to make context-aware decisions and coordinate complex tasks autonomously over extended periods. By reducing the need for constant human oversight, agentic AI promises significant efficiency gains in industries that rely on complex, high-stakes operations.

Foundation models, with their broad multimodal capabilities, enhance machine perception, language comprehension, and adaptive dialog. These models allow for more natural, intuitive interactions between humans and machines, supporting collaboration in environments ranging from manufacturing to healthcare and customer service.

However, challenges remain. Current datasets often lack the multimodal depth needed to train robust models for industrial contexts. There is also a need for greater reliability under uncertain conditions, ensuring that adaptive systems remain safe and trustworthy even in dynamic or unpredictable environments.

Looking forward, the authors highlight the importance of cross-domain integration. The principles of HMC extend beyond industrial automation and are increasingly relevant in healthcare, education, smart cities, and other sectors where adaptive, responsive systems are becoming essential.