Human-robot interaction (HRI) and the rise of artificial intelligence (AI), particularly generative AI, raised important regulatory, business, societal, and ethical issues. This paper examines the complex relationship between humans and AI models in social and corporate contexts from an anthropomorphic perspective. We reviewed HRI literature, focusing on generative models like ChatGPT. The scientometric study predicts that generative AIs like ChatGPT will grow in popularity alongside human rational empathy, the tendency for personification, advanced linguistic capabilities, and the ability to mimic human-like behavior.
These models create new moral and philosophical issues by blurring human-robot boundaries. Our research aims to extrapolate HRI trends and unique factors and explain how generative AI’s technical aspects make it more effective than rule-based AI systems. We discuss the challenges and limitations of generative AI in HRI and propose a research agenda for AI optimization in education, entertainment, and healthcare.
Human-robot interaction (HRI) research spans social sciences, psychology labs, technology, computer science, AI research, academia, and technology company start-ups. Artificial intelligence is a rapidly developing field that presents numerous ethical dilemmas in business, society, psychology, and philosophy. Human-robot interaction integrates physical and virtual worlds using the Internet of Things ecosystem. It boosts resilience and sustainability. ChatGPT, a disruptive innovation, has garnered attention from humanitarians and business scholars. The demand for intelligent and automated robots benefits society and businesses.
AI models can create challenging work scenarios to test candidates’ problem-solving, decision-making, and interpersonal skills. This method standardizes and scales job-related skill assessments. AI and occupational psychology are linked by job crafting. Job crafting involves rearranging duties to match skills, interests, and values. Generative AI helps occupational psychologists and vice versa. With the growing adoption of artificial intelligence (AI), little is known about human behavior in this new work environment.
Definition and studies on human-robot interaction
The trend in HRI literature on social robots and anthropomorphism shows human acceptance and responses to gender-different AI. However, Coeckelbergh states that “how we relate to the robot will always depend on human subjectivity, meaning-making, narratives, language, metaphors.
The literature shows several key factors determining social robot trust. identified robot-specific issues (robot traits and performance), human-related factors (individual needs, trust, comfort, self-confidence, attitude, prior experience, competency), and scenario-specific elements (task complexity, cultural aspects, team collaboration). Among various factors, robot-related factors significantly impact trustworthiness in human-robot interactions. Important factors include robot behavior, dependability, predictability, transparency, adaptability, and anthropomorphism. Humans trust robots with anthropomorphic faces more.
Leon Turkin, Mortgage Broker at Turkin Mortgage, explains, that HRI studies human-robot interaction, communication, and collaboration, including robot behavior design. The goal of human-robot interaction is to create robots that integrate into human environments, understand human needs, and provide meaningful assistance and collaboration. Human-robot interaction is improved by HRI research and advancements.
The theoretical underpinning for successful HRI
Successful HRI relies on humane anthropomorphism. As Piaget ascertained back in 1929, as rational beings, humans solicit reasoning for behavior, therefore, ascribing human characteristics and agency to artificial systems and inanimate objects.
| Category | Key findings |
| Definition of HRI | HRI involves multidisciplinary study, focusing on understanding and improving interactions between humans and robots |
| Factors influencing HRI | Key factors include robot-specific issues, human-related factors, and scenario-specific elements |
| Role of anthropo-morphism in HRI | Anthropomorphic features on a robot can induce positive feelings and enhance perceived trustworthiness |
| Applications of HRI | HRI finds application in healthcare, manufacturing, education, entertainment, and domestic settings |
| Role of trustworthiness in HRI | Theoretical underpinning of HRI |
| Role of trustworthi-ness in HRI | Trustworthiness is a crucial attribute in the perception of social robots, with robot-related factors significantly influencing people’s evaluations of trustworthiness |
Anthropomorphism
Humans have a tendency to attribute human-like characteristics to non-human entities, such as religious figures, animals, and surroundings, as well as self-managed technological innovations. The phenomenon of anthropomorphism is a central conceptual term in the field of social robotics research. It refers to this tendency. Including cognitive, intellectual, intentional, relational, and emotional states, the attribution encompasses all of these conditions. In its most basic form, anthropomorphism can be understood as an involuntary unconscious perceptual strategy of human expectation that inanimate system stimuli have a human-like cause. The concept of anthropomorphism in relation to ChatGPT refers to the idea that artificial intelligence possesses human-specific characteristics, mental states, behavioral characteristics, and emotional capabilities.
Human-robot interaction (HRI) trends: a scientometric study
Designing research: Our analysis of HRI research direction and anthropomorphism in generative AI uses the SciVal database, which penalizes detailed literature studies. The study lasted from 2013 to 2022. Key terms were used to identify key fields and emerging directions. SciVal clustered keywords and examined connections in the articles. Thus, keyword-critical clustering groups articles by keyword similarity.
This method identifies articles with common themes. This study clustered keywords by robot, anthropomorphic robots, human-robot interaction, social robot, humanoid robot, man-made machines, anthropomorphism, and collaboration. This study relied on Scopus, a multidisciplinary bibliographic database that indexes over 23,000 journals, conference proceedings, and trade publications. Search terms for SciVal included “anthropomorphism,” “human-centric behavior,” “humanoid robots,” “social robots,” “generative AI,” “inclusiveness,” “technological literacy,” “technological proficiency,” and “learning ability.” We identified clusters including human-robot interaction, anthropomorphism, social robots (T.12454), humanoid robots, employee wellbeing, and technological literacy (T.71917). Key institutions, countries, authors, and Scopus sources contributing to these clusters were examined. A second, smaller cluster around humanoid robots was examined to understand the research environment.
We list the top institutions in human-robot interaction, humanoid robots, and man-machine systems (Table 2). Osaka University, University of Lisbon, University of Hertfordshire, Advanced Communications Research Institute International, and Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa exhibit the highest scholarly output. Significant contributions to research output are crucial for field advancement. Research outcomes and scholarly contributions from these institutions have significantly contributed to the growth and development of this research domain.
Generative AI
Within two months of its November 2022 launch, ChatGPT had over 100 million users. One could argue that it is the most innovative, influential, and successful application in recent history. Relatability and accessibility are key to its popularity. This language model allows for open dialogue and interaction with humans, aligning with their information processing across diverse contexts. The goal is to create an intuitive interface that encourages high engagement and public interest. Compared to what users thought was possible, the platform had more potential. Many were skeptical of future progress and worried about job security and sensibility after the software caused controversy. Embracing AI requires emphasizing its benefits and encouraging public discourse for positive outcomes that enhance life quality and benefit humanity. The expansion changed culture, sparked public debate on application possibilities, and opened new dimensions beyond science fiction, exceeding expectations for everyday usability.
General characteristics
Generative AI in occupational psychology is a promising area. AI scholars can take a solid or weak AI stance based on their intrinsically mechanistic or illusionistic perspective. The robust AI tenet suggests that artificial systems can mimic human intelligence by mimicking brain computations. The weak AI thesis argues that artificial intelligence is an oxymoron, implying that the human mind can be simulated. Generative AI models can create text, images, and human-like conversations. Generative AI has many occupational psychology applications. Realistic and immersive employee assessment scenarios are possible. AI-powered chatbots and virtual assistants can help employees manage stress and mental health.
In the words of Andrew Smith, Co-Founder of propfusion, “AI and smart data aren’t just tools, they are our partners in rethinking what property management can achieve. By providing actionable insights, we’re enabling property managers to make informed decisions effortlessly.”
Occupational psychologists can help design and evaluate AI-driven interventions to ensure they follow mental health ethics and best practices.
Technical aspects of generative AI inhuman–robot interaction
Generative AI is an effective business and educational tool due to its broad database, facts, common facts checking, literature, analysis, ERP, decision-making strategies and techniques, and resource utilization and allocation comparison methods. Instant access to large amounts of data and efficient usage in specific contexts without time constraints make it an asset. Few resources are needed to complete a task that typically requires multiple resources, such as technology, people, and cognitive labor. Central to ensuring the safety of study participants and the integrity of the research process is the data monitoring committee (DMC).
Hector S, CEO of Baltimore HCS home cleaning services, emphasizes, “Smart systems have transformed how we operate. From tracking customer preferences to optimizing our team schedules, every decision is data-driven. It’s about delivering exceptional service without compromising on efficiency.”
Areas of foreffective human-AI interaction
Explains similarities between HRI and generative AI in interaction, application, trust, anthropomorphism, flexibility, adaptability, and social/cultural context. Both significantly impact society with their diverse applications. This comparison shows that generative AI is more similar to HRI than traditional software due to its flexibility, interactivity, context recognition, and social impact. The similarities between the two contexts allow speculation about generative AI applications and research.
Implications for social business applications of generative AI
Generative AI has significance for both social and business applications. Generational AI automates text, images, music, and videos. People can efficiently create personalized content and express themselves creatively in new ways. Generative AI algorithms analyze user behavior and recommend products and experiences. Furthermore, user satisfaction, involvement, and decision-making processes can be positively influenced. Creative professionals like artists, designers, and musicians benefit from generative AI. The use is especially evident in ideation, design proposals, and operations.
Matt Behenke, CEO of orthotic shop, shares how his company is harnessing this power:
“We use smart data to understand our customers’ needs on a granular level. AI algorithms analyze purchase patterns, foot measurements, and user preferences to deliver the perfect fit. It’s not just retail; it’s a personalized journey.”
It is used in architecture and manufacturing design and planning. AI tools accurately measure multiple design options and aid decision-making. It can improve efficiency, quantifiability, and consistency in content creation. Generative AI can help individuals with disabilities, language barriers, or specific learning needs. Generative AI creates synthetic data that mimics real-world data distributions to improve machine learning model performance, generalization, and data privacy.
Future studies and limitations
Generative AI and HRI research has advanced, but there are still gaps that need to be filled. Generative AI and HRI ethics need further study. The significant advancement in AI has raised ethical concerns and risks that require investigation. Research should address privacy, bias, accountability, and AI’s social impact. Frameworks and guidelines are essential for responsible and beneficial use of these technologies.
Ethical guidelines and regulations to manage AI’s growth in line with society’s needs and development are essential. Future research must also understand AI’s decision-making and its effects on society. Conduct studies on integrating AI into various aspects of life to balance innovation and ethics. Brief user interactions are often the focus of current research. Long-term user adaptation studies should consider user preferences, evolving needs, and robots or AI systems’ ability to learn and adapt to changing user needs.
Conclusion
HRI and generative AI raise ethical and regulatory concerns. This paper explores the complex relationship between humans and generative AI models like ChatGPT. Along with rational empathy, personification, and advanced linguistics, the study predicts generative AI will become popular. However, these models blur human-robot boundaries, raising moral and philosophical issues. This study extrapolates HRI trends and explains how generative AI’s technical aspects make it more effective than rule-based AI. A research agenda for AI optimization in education, entertainment, and healthcare is presented.
A study analyzing human-robot interaction (HRI) trends and anthropomorphism in generative AI using the SciVal database from 2013 to 2022 identified clusters including human-robot interaction, anthropomorphism, social robots, humanoid robots, employee wellbeing, and technological literacy. Top institutions in human-robot interaction, humanoid robots, and man-machine systems were identified.
Due to its accessibility and ability to create text, images, and human-like conversations, generative AI like ChatGPT is popular. Realistic employee assessment scenarios and AI-powered chatbots are examples of its occupational psychology applications. Flexibility, interactivity, context recognition, and social impact make generative AI more like HRI than traditional software. This affects social and business applications by automating text, images, music, and videos, analyzing user behavior, and improving efficiency and data privacy. Future research should address ethics, privacy, bias, accountability, and AI’s social impact.
