## 2 Review of Related Literature
### Application in Wizard-of-Oz  
The Wizard-of-Oz (WOZ) technique has long served as a cornerstone in early usability engineering, wherein human \"wizards\" simulate system functionalities that have yet to be implemented, thereby providing a flexible and dynamic means of prototyping [15]. This method's historical significance lies in its capacity to capture a comprehensive spectrum of user-system interactions, offering researchers invaluable insights into how users engage with emerging technologies even before a system is fully realized. This approach offers distinct advantages over conventional prototyping methods. As Bernsen et al. explains, WOZ avoids the oversimplification of complex task domains, allowing for an authentic simulation of system behavior that can capture subtle nuances in user interactions [15]. Such detailed data collection is crucial for understanding usability in its full complexity, making WOZ an indispensable tool for iterative design and evaluation. However, Simpson et al. highlights several challenges inherent in employing human operators, including the complexities of managing intricate interfaces, the distractions that arise during multitasking, and the reliance on manual inputs such as keyboard hotkeys [27]. These issues can introduce errors and inconsistencies, potentially undermining the fidelity and scalability of the simulated interactions.  
To address these challenges, recent developments have sought to augment the conventional WOZ framework with AI-assisted methodologies. Shin et al. introduces the Apprentice of Oz system, a human-in-the-loop approach designed to alleviate the cognitive burden on human wizards by automating specific subtasks [28]. By decomposing the wizard's role into smaller, more manageable components, this integrated system enhances both the consistency and reliability of the simulation, thereby addressing many of the limitations associated with manual control. The versatility of the WOZ method is further demonstrated through its application in domain-specific contexts. Li et al. illustrate this through a dataset that leverages the WOZ technique to simulate realistic human-robot dialogues within industrial robotics and manufacturing settings [29]. The creation of such high-quality, annotated datasets is pivotal for training task-oriented dialogue systems, underscoring the method's capacity to yield insights that are both domain-relevant and practically applicable.  
Further expanding the scope of WOZ research, studies have begun to examine the dynamics of human-AI teamwork in collaborative settings. Zheng et al. explore the integration of AI as an equal participant in group decision-making processes by granting it equal voting rights alongside human counterparts [30]. Their findings reveal that, although AI contributes commendably with commendable consistency and objectivity, it still encounters difficulties adapting to dynamic discussions and contributing progressively. Innovations that enhance the believability and scalability of WOZ-inspired systems have also emerged. Schecter et al. propose the Vero method, which fuses traditional WOZ techniques with modern technological solutions such as video conferencing and animation [31]. This approach facilitates the simulation of autonomous AI behavior in a controlled, yet realistic, environment and thereby renders human-AI teamwork research more accessible and scalable. The Vero method effectively addresses several scalability and authenticity issues that have long challenged manual WOZ simulations.