AI Oracle: A Blockchain-Powered Oracle for LLMs and AI Agents

Understanding how AI influences employee emotions is becoming critical as organizations prepare for widespread AI agent deployment. While existing research has explored human-AI interactions in corporate settings, little is known about how employees emotionally navigate relationships with AI agents exhibiting distinct personality traits. This empirical study examines white-collar employees' emotional responses while interacting with three generative AI agents in a virtual workspace, revealing novel social dynamics enabled by AI technologies. Using qualitative methods and inductive analysis, our findings show that anthropomorphic AI agents evoke a broad spectrum of emotions, from connection and contentment to amusement and frustration , extending beyond those typically triggered by web-based AI agents. Notably, participants experienced new emotional subsets, including unique manifestations of relational assurance and perceived worthlessness , which introduces new emotional subcategories within established frameworks. Moreover, the visual embodiment of AI agents in virtual workspaces significantly shapes user expectations and satisfaction. While a more human-like appearance can enhance engagement, it also introduces risks—a mismatch between an AI’s visual representation and its actual behavior can heighten disappointment if the AI fails to meet human-like expectations. As organizations integrate AI agents into the workplace, our findings provide key insights for designing effective human-AI interactions. We emphasize the importance of human-centered design approaches that foster, rather than hinder, employee engagement, ensuring AI contributes positively to corporate environments. • Presents an empirical study exploring white-collar employees' interactions with anthropomorphic AI agents in a virtual workspace, offering actionable insights for human-centered corporate AI design and deployment. • Extends a framework for understanding emotional dynamics in human-AI interactions, with a particular focus on emerging emotional patterns unique to anthropomorphic AI collaboration. • Explores the innovative convergence of AI and VR technologies, laying the groundwork for research on future workspaces where human employees collaborate with virtual AI agents.

This paper presents Hyborg Agency, a computational ecosystem that explores how AI agents can meaningfully coexist with human society through the metaphor of a digital forest. By reimagining AI agents as mechanical deer, mutated from discarded electronics, the project defamiliarizes common perceptions of anthropomorphized AI agents, transforming them into non-human creatures with conversational abilities. The system employs Large Language Models (LLMs) to process community conversations, treating them as nutrients for AI growth, and features a dual-platform structure that connects an immersive 3D environment with Discord, a widely used communication platform. Based on community chats, Hyborgs generate daily summaries of their observations and share them with one another, fostering consistent memories and perceptions of the world. Through thematic analysis of public exhibitions and structured interviews with fourteen experts (seven pairs), we identified three key themes: social expansion in human relationships, language evolution through code-infused communication, and creative engagement through defamiliarized interaction. These findings highlight how AI agents can enrich human social relationships while maintaining transparency about their artificial nature. This work contributes to ongoing discussions on AI participation in society through a speculative scenario: human discourse serves as the foundation for constructing a digital forest that nurtures AI agents. In this virtual forest, humans and AI grow together in a symbiotic relationship, shaping one another and their shared environment, ultimately achieving harmonious coexistence.

Following the rapid evolution of AI in education, AI agents are being integrated into K-12 classrooms. However, their effectiveness in students’ cognitive enhancement remains inconsistent across studies. This study conducted a meta-analysis to evaluate the effectiveness of AI-agents on students’ cognitive learning outcomes in K-12 education, and their impact on specific cognitive categories, while also highlighting the influence of moderator variables, such as the type of AI agents, learner level, discipline, and experimental duration. A random-effect meta-analytic approach was employed, synthesizing 73 effect sizes from 34 studies published between 2020 and 2025. The main effect size was calculated using Hedge’s g, while the heterogeneity was assessed using the Q, I 2 , and τ 2 statistics. Publication bias was tested using the funnel plot, while the Classic fail-safe N test, Orwin’s fail-safe N test, and trim-and-fill methods were used to assess the robustness of findings. The results demonstrate that: 1) AI agents have a statistically significant, moderate positive effect on students’ cognitive learning outcomes (with an overall effect size of 0.404, p < 0.001); 2) Across cognitive categories, AI agents showed a moderate significant impact on skills-based outcomes (g = 0.391, p < 0.001) and knowledge-based outcomes (g = 0.344, p < 0.05); 3) For higher-order thinking skills, the impact was insignificant (g = 0.540, p = 0.066); 4) There was a significant moderating effect of learner level and discipline under the intervention; 5) The publication bias test showed a slight study effect, whereas sensitivity analysis confirmed the robustness of results. The findings indicate the need to consider both the disciplinary context and students’ characteristics when integrating AI agents in K-12 classrooms. The results provide direction to educators, policymakers, and researchers on the effectiveness of AI-agents on students’ cognitive learning outcomes in K-12 education. • AI agents significantly boost K-12 students' cognitive learning outcomes. • Strongest benefits for skill development and higher-order thinking. • Effectiveness depends on AI type, grade level, and subject area. • Supports tailored implementation strategies for optimal impact.

Recent advances in generative AI and machine learning have enabled systems like ChatGPT to produce increasingly human-like text.ChatGPT and its successors represent an exciting opportunity to expand human creativity and productivity, but they also pose real risks and concerns that must be addressed.These generative AI systems are able to generate plausible paragraphs, articles, images, and more on any topic or in any style based on analyzing massive amounts of data.They can complement and augment human abilities, helping people write faster, develop new creative ideas, and produce content at scale.By enabling AI agents to take over more routine and repetitive forms of manual work, human lives may be enriched with increased leisure time and opportunity for meaningful work.However, there are valid worries about bias and unfairness being reinforced or even amplified in generative AI models due to flaws in training data or algorithms.There are also concerns that these systems could be used to generate spam, scams, fake content, and manipulate public opinion at scale.As the technology improves, there may be concerns about AI systems developing their own objectives and goals that differ from human values and priorities.While ChatGPT and similar models represent an exciting new frontier of capability, they must be developed and applied carefully and ethically.Partnerships between humans and AI agents could help maximize the benefits of generative AI while mitigating the risks, but it will require proactive safeguards and oversight.Policies and guidelines should aim to ensure that generative AI progresses in a way that is aligned with human judgment, enhances trust in information, and supports human dignity and well-being.With proper stewardship, generative AI can positively transform our world, but we must be vigilant and thoughtful in how we develop and deploy this powerful technology.The opportunity of ChatGPT comes with real responsibilities that cannot be ignored.With concerted action, the future of generative AI can be shaped for the benefit of humanity.

Aim: The aim of the paper is to examine how AI agents within Oracle AI Agent Studio can be integrated into Oracle’s ERP systems to enhance intelligent business process automation, improve system responsiveness, and optimize overall business operations. Methods: The paper focuses on the design, deployment, and management of AI agents residing in Oracle Agent Studio and their role in ERP module operand process optimization. It discusses implementations of agentic AI within Oracle Fusion Cloud Applications across finance, HR, and operations. Results: The study finds that AI agents support advanced workflow automation, contextual decision - making, and problem - solving, which help reduce user workload and improve system responsiveness. The paper also highlights automated audit log creation, compliance workflows, and automated stakeholder prompts as key functional benefits. Conclusion: The paper concludes that integrating AI agents into ERP systems enhances responsiveness, openness, and operational efficiency, aligning with the author’s perspective on practical implementation, system scalability, and the evolving role of Artificial Intelligence in ERP systems. Recommendations: The author recommends the integration of AI agents within ERP systems to further optimize workflow automation, improve decision - making processes, and support more efficient business operations.

Increased delegation of commercial, scientific, governmental, and personal activities to AI agents—systems capable of pursuing complex goals with limited supervision—may exacerbate existing societal risks and introduce new risks. Understanding and mitigating these risks involves critically evaluating existing governance structures, revising and adapting these structures where needed, and ensuring accountability of key stakeholders. Information about where, why, how, and by whom certain AI agents are used, which we refer to as visibility, is critical to these objectives. In this paper, we assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging. For each, we outline potential implementations that vary in intrusiveness and informativeness. We analyze how the measures apply across a spectrum of centralized through decentralized deployment contexts, accounting for various actors in the supply chain including hardware and software service providers. Finally, we discuss the implications of our measures for privacy and concentration of power. Further work into understanding the measures and mitigating their negative impacts can help to build a foundation for the governance of AI agents.

Considerable effort has been dedicated to detecting machine-generated texts to prevent a situation where the widespread generation of text-with minimal cost and effort-reduces trust in human interaction and factual information online.Our study takes a more refined approach by analysing different Conversational AI Agents (CAAs).By constructing linguistic profiles for each AI agent, the aim is to identify the Uniquely Identifiable Linguistic Patterns (UILPs) for each model and to demonstrate the effectiveness of these UILPs in identifying its respective AI agent using authorship attribution techniques.Promisingly, we are able to classify AI agents based on their original texts with a weighted F1-score of 96.94%.Further, we can attribute AI agents according to their writing style (as specified by prompts), yielding a weighted F1-score of 95.84%, which sets the baseline for this task.By employing principal component analysis (PCA) for dimensionality reduction, we achieve a weighted F1-score ranging from 89.25% to 97.83%, and an overall weighted F1-score of 96.93%.

In Human-AI collaborative settings that are inherently interactive, direction of communication plays a role in how users perceive their AI partners. In an AI-driven cooperative game with partially observable information, players (be it the AI or the human player) require their actions to be interpreted accurately by the other player to yield a successful outcome. In this paper, we investigate social perceptions of AI agents with various directions of communication in a cooperative game setting. We measure subjective social perceptions (rapport, intelligence, and likeability) of participants towards their partners when participants believe they are playing with an AI or with a human and the nature of the communication (responsiveness and leading roles). We ran a large scale study on Mechanical Turk (n=199) of this collaborative game and find significant differences in gameplay outcome and social perception across different AI agents, different directions of communication and when the agent is perceived to be an AI/Human. We find that the bias against the AI that has been demonstrated in prior studies varies with the direction of the communication and with the AI agent.

Objective This study developed an AI-powered chatbot simulating a patient with acute pulpitis to enhance history-taking training in stomatology, aiming at providing a cost-effective tool that improves diagnostic and communication skills while fostering clinical competence and empathy. Methods The study involved 126 undergraduate clinical medicine students who interacted with an AI agent simulating a patient suffering acute pulpitis. The AI agent was created and optimized in a five-step process, including preliminary creation, usability testing with a Chatbot Usability Questionnaire (CUQ), analysis and optimization, retesting, and comparison of pre- and post-optimization results. The platform used was ChatGLM, and statistical analysis was performed using R software. Results The pre-optimization group’s CUQ mean score was 64.2, indicating moderate satisfaction. After optimization, the post-optimization group’s mean score improved to 79.3, showing significantly higher satisfaction. Improvements were noted in all aspects, particularly in the chatbot’s personality, user experience, error handling, and onboarding. Conclusion The optimized AI agent effectively addresses challenges in history-taking training, improving realism, engagement, and accessibility to diverse scenarios. It demonstrates the potential of AI-powered chatbots as valuable tools for enhancing medical education.

Purpose This study aims to draw on social identity theory to explore the impact of service agent type (human staff vs AI agent) on customers’ moral choices. Design/methodology/approach This study conducts five experiments to validate the proposed hypotheses, involving a total of 900 Chinese participants. Findings The results suggest that in service settings, AI agents lead to more unethical customer behavior than human agents. Empathy and moral obligation play a serial mediating role in this process. When customers perceive lower power, AI agents (vs human staff) are more likely to lead customers to engage in unethical behavior. When customers perceive higher power, this effect is mitigated. Practical implications This work offers valuable insights into how to enhance customers’ group identification with AI agents and elevate their ethical standards in service-oriented environments. Originality/value This study advances the explanatory framework of social identity theory in moral decision-making contexts, enriching the psychological mechanism through which service agents influence customers’ moral choices.

In recent years, an increasing number of studies have begun to use conversational data in spontaneous speech to estimate cognitive function in older people. The targets of spontaneous speech with older people used to be physicians and licensed psychologists, but it is now possible to have conversations with fully automatic AI agents. However, it has not yet been clarified what difference there is in conversational communication with older people when the examiner is a human or an AI agent. This study explored the psychological burden experienced by elderly participants during cognitive function assessments, comparing interactions with human and AI conversational partners. Thirty-four participants, averaging 78.71 years of age, were evaluated using the Mini-Mental State Examination (MMSE), the Visual Analogue Scale (VAS), and the State-Trait Anxiety Inventory (STAI). The objective was to assess the psychological impact of different conversational formats on the participants. The results indicated that the mental strain, as measured by VAS and STAI scores, was significantly higher during the MMSE sessions compared to other conversational interactions (p < 0.01). Notably, there was no significant difference in the mental burden between conversations with humans and AI agents, suggesting that AI-based systems could be as effective as human interaction in cognitive assessments.

For decades, autonomous agents and multiagent systems (AAMAS) have developed foundational theories of autonomy and coordination. While large language models (LLMs) have renewed interest in AI agents, connections to established frameworks remain limited. This paper surveys AAMAS theoretical foundations (Concept), their LLM-based implementations (Code), and societal deployment (Commerce), providing an integrated framework for understanding AI agents' current trajectory. This survey bridges LLM researchers' groundbreaking empirical advances in AI agents with established AAMAS theories to catalyze future progress.

AI innovation is increasingly focusing on the development and utilization of AI agents: systems that gather information, make decisions and take actions with some degree of autonomy. Agents represent a natural evolution from AI that generates outputs following user commands, such as ChatGPT, to AI that plays a much more active role by completing tasks. For example, in the future, a consumer AI agent might book and pay for a holiday or decide which groceries to order. Tech luminaries believe agents will make AI truly transformative, and tech firms and investors have been investing in AI agents as they look to monetize AI. Given the emerging focus on agents, it will not be long before competition agencies take a closer look to assess any concerns against benefits. This article previews some of the likely debates to come. Many possible issues that could be raised appear analogous to those of other digital markets, such as self-preferencing, pre-installation and defaults, market tipping and ecosystem effects. But agencies may also raise new or potentially more challenging issues about AI agents, ranging from access to hardware/software, conflict of interests, use of personalized data, to a lack of transparency. Equally, agents might trigger more seismic changes in the competitive landscape.

Games that feature multiple players, limited communication, and partial information are particularly challenging for AI agents. In the cooperative card game Hanabi, which possesses all of these attributes, AI agents fail to achieve scores comparable to even first-time human players. Through an observational study of three mixed-skill Hanabi play groups, we identify the techniques used by humans that help to explain their superior performance compared to AI. These concern physical artefact manipulation, coordination play, role establishment, and continual rule negotiation. Our findings extend previous accounts of human performance in Hanabi, which are purely in terms of theory-of-mind reasoning, by revealing more precisely how this form of collective decision-making is enacted in skilled human play. Our interpretation points to a gap in the current capabilities of AI agents to perform cooperative tasks.

Background: Generative AI agents will need to work together, which requires monitoring and managing their performance. Objectives: The chief objective of this paper is to understand the joint design choice of the number of agents and their rewards. Methods: We study this problem in a theoretical framework of optimal incentives, where a system designer (principal) selects the environment in which multiple autonomous decentralized AI agents work together. These agents respond to incentives, such as rewards and penalties. We first consider a principal who selects the size of the agent team in addition to their incentives. Results: We prove a general result that the optimal team size will vary with the parameters of the environment, but the optimal incentives will not. This invariance property shows that agents should have different-sized teams on work projects rather than differing financial incentives. Conclusions: We show these results are robust to a more general framework, where the principal employs a supervisory AI agent to manage the tasks of the underlying AI team. Finally, we propose different levels of quality for the supervisory and worker agents, and find that it is efficient to match the best supervisors with the best worker agents.