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Journal articles on the topic 'Interaction with AI'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

van Berkel, Niels, Mikael B. Skov, and Jesper Kjeldskov. "Human-AI interaction." Interactions 28, no. 6 (2021): 67–71. http://dx.doi.org/10.1145/3486941.

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Pituxcoosuvarn, Mondheera, Midori Tanimura, Yohei Murakami, and Jeremy Stewart White. "Enhancing EFL Speaking Skills with AI-Powered Word Guessing: A Comparison of Human and AI Partners." Information 16, no. 6 (2025): 427. https://doi.org/10.3390/info16060427.

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This study explores the effects of interacting with AI vs. human interlocutors on English language learners’ speaking performance in a game-based learning context. We developed Taboo Talks, a word-guessing game in which learners alternated between giving and guessing clues with either an AI or a human partner. To evaluate the impact of interaction mode on oral proficiency, participants completed a story retelling task, assessed using complexity, accuracy, and fluency (CAF) metrics. Each participant engaged in both partner conditions, with group order counterbalanced. The results from the retel

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Renza, Victor. "AI Generated Art." Morals & Machines 2, no. 2 (2022): 32–39. http://dx.doi.org/10.5771/2747-5174-2022-2-32.

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Human-robot interaction (HRI) through the practice of different types of art is bringing to the table new perspectives on innovative interactions where the cooperation between artists and robots not only deliver original works of art but also novel communication mechanisms beyond spoken and written-based forms. Generative art, that is, artworks mediated by the interaction with computers in codes and art generated by artificial intelligence (AI), are having a profound impact in different fields of the arts, including visual arts, performative arts, music and literature. This research proposes t

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Wang, Qiaosi, Ida Camacho, Shan Jing, and Ashok K. Goel. "Understanding the Design Space of AI-Mediated Social Interaction in Online Learning: Challenges and Opportunities." Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (2022): 1–26. http://dx.doi.org/10.1145/3512977.

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Our online interactions are constantly mediated through Artificial Intelligence (AI), especially our social interactions. AI-mediated social interaction is the AI-facilitated process of building and maintaining social connections between individuals through information inferred from people's online posts. With its impending application across a number of contexts, the challenges and opportunities of AI-mediated social interaction remain underexplored. This paper seeks to understand the design space of AI-mediated social interaction in the context of online learning, where students frequently f

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Aksoyalp, Zinnet Şevval, and Betül Rabia Erdoğan. "COMPARATIVE EVALUATION OF ARTIFICIAL INTELLIGENCE AND DRUG INTERACTION TOOLS: A PERSPECTIVE WITH THE EXAMPLE OF CLOPIDOGREL." Ankara Universitesi Eczacilik Fakultesi Dergisi 48, no. 3 (2024): 22. http://dx.doi.org/10.33483/jfpau.1460173.

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Objective: The study aims to compare the ability of free artificial intelligence (AI) chatbots to detect drug interactions with freely available drug interaction tools, using clopidogrel as an example. Material and Method: The Lexicomp database was used as a reference to determine drug interactions with clopidogrel. ChatGPT-3.5 AI and Bing AI were selected as the free AI chatbots. Medscape Drug Interaction Checker, DrugBank Drug Interaction Checker and Epocrates Interaction Check were selected as free drug interaction tools. Accuracy score and comprehensiveness score were calculated for each d

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Bennett, Casey, Benjamin Weiss, Jaeyoung Suh, Eunseo Yoon, Jihong Jeong, and Yejin Chae. "Exploring Data-Driven Components of Socially Intelligent AI through Cooperative Game Paradigms." Multimodal Technologies and Interaction 6, no. 2 (2022): 16. http://dx.doi.org/10.3390/mti6020016.

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The development of new approaches for creating more “life-like” artificial intelligence (AI) capable of natural social interaction is of interest to a number of scientific fields, from virtual reality to human–robot interaction to natural language speech systems. Yet how such “Social AI” agents might be manifested remains an open question. Previous research has shown that both behavioral factors related to the artificial agent itself as well as contextual factors beyond the agent (i.e., interaction context) play a critical role in how people perceive interactions with interactive technology. A

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Etibar Aliyev, Oluyinka Adedokun, Adetutu Temitope Fabusoro, and Babatunde Keshinro. "AI-enhanced conversational avatars for immersive interaction." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 627–34. https://doi.org/10.30574/wjarr.2025.26.1.1101.

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This paper reviews the significance of key technologies of AI-enhanced conversational avatars and their applications for immersive interaction, including increased AI system adoption, enhanced trustworthiness, and personalized and engaging interactions. Using empirical data and case studies, this paper provides evidence regarding recent advancements that facilitate high-fidelity and style-controllable avatar generation, providing customizable options for users for online social interactions. This is possible by integrating several AI modules like text-to-speech, speech recognition, and open-do

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Lushyn, Pavlo, and Yana Sukhenko. "Metacommunications and Artificial Intelligence: The ECPF Approach for International Management." Організаційна психологія Економічна психологія 35, no. 2 (2025): 161–73. https://doi.org/10.31108/2.2025.2.35.15.

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Introduction. The article addresses the development of metacommunicative competence among international management professionals amid rapid AI integration. Based on analysis of contemporary human-AI interaction research, it establishes theoretical foundations of metacommunication in cognitive heterogeneous environments and substantiates the eco-facilitative approach as a methodological basis for developing this competence in hybrid management contexts. Aim. The aim is to conceptualize metacommunication as a key mechanism for interacting with AI agents within interface-based reality, and to jus

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Choudhury, Avishek, and Onur Asan. "Human factors: bridging artificial intelligence and patient safety." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (2020): 211–15. http://dx.doi.org/10.1177/2327857920091007.

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The recent launch of complex artificial intelligence (AI) in the domain of healthcare has embedded perplexities within patients, clinicians, and policymakers. The opaque and complex nature of artificial intelligence makes it challenging for clinicians to interpret its outcome. Incorrect interpretation and poor utilization of AI might hamper patient safety. The principles of human factors and ergonomics (HFE) can assist in simplifying AI design and consecutively optimize human performance ensuring better understanding of AI outcome, their interaction with the clinical workflow. In this paper, w

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Ismailova, R., K. Abysheva, and M. Sadykova. "RADICAL FORGIVENESS AND ARTIFICIAL INTELLIGENCE: PSYCHOLOGICAL ASPECTS OF INTERACTION." Scientific heritage, no. 146 (October 7, 2024): 43–45. https://doi.org/10.5281/zenodo.13896864.

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This article explores the impact of interaction with artificial intelligence on cognitive processes, emotional states, and social relationships. Based on the theoretical framework of Colin Tipping's method of Radical Forgiveness, the article analyzes how forgiveness can influence a person's perception of AI interaction and emotional adaptation. The focus is on how AI affects emotional stability, interpersonal relationships, and social interactions. The research includes stages such as a literature review on the psychological aspects of AI interaction, surveys, and interviews with participants

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Şen Bayram, Asena Kumsal, and İnanç Şencan. "An AI-Infused Creativity Framework for Human AI Interaction." International Journal of Mardin Studies 6, no. 1 (2025): 63–78. https://doi.org/10.63046/ijms.1673173.

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This research presents a framework questioning the interaction between humans and artificial intelligence through creativity. The framework under investigation in this study begins to question the integration of artificial intelligence into contemporary art productions, addressing topics such as collectivity, archaic humanity, and fragmented historical narratives, and offers a new interpretation through its relationship with the Co-Design concept used in the architecture-engineering-construction sectors. The proposed framework has been tested through the production of an artwork named [Blinded

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Kim, Soojung, Hyemin Suh, and Boram Lee. "The Interaction between Intelligent Robots and Children in Daycare Centers." Korean Journal of the Human Development 30, no. 4 (2023): 73–87. http://dx.doi.org/10.15284/kjhd.2023.30.4.73.

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The purpose of this study is to introduce artificial intelligence (AI) robots to day-care centers to examine how children interact with robots in a natural environment and whether these interaction patterns change over time. Accordingly, the AI robot ALPHA MINI was introduced into two classes (33 children and 3 teachers) at daycare centers for 5 years olds to observe interactions among children, teachers, and a robot for three months. In addition, interviews with teachers were conducted after using the robot. The findings of the study revealed that children mainly use simple language with robo

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Li, Xiaodong. "Research on Generative Artificial Intelligence in Innovate Strategies of Teaching Interaction." Education Reform and Development 7, no. 4 (2025): 13–24. https://doi.org/10.26689/erd.v7i4.10323.

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In this modern era, effectively integrating artificial intelligence (AI) technology into classroom teaching interactions presents a novel challenge for many university educators. This paper begins by outlining the theoretical foundations of generative AI and teaching interaction. It then provides an analysis of the challenges that classroom teaching interactions face in today’s context, along with the benefits that generative AI can offer to such interactions. Additionally, it examines the current state of generative AI applications in classroom teaching interactions and explores innovative st

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Taylor, Alex, Daniela Rosner, and Mikael Wiberg. "AI activism." Interactions 28, no. 1 (2021): 5. http://dx.doi.org/10.1145/3442368.

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P SONAJI, L SUBRAMANIAN, and M RAJESH. "Artificial intelligence-driven drug interaction prediction." World Journal of Biology Pharmacy and Health Sciences 17, no. 2 (2024): 297–305. http://dx.doi.org/10.30574/wjbphs.2024.17.2.0070.

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Artificial intelligence (AI) is developing at a rapid pace and this has led to revolutionary changes in many fields, including healthcare. Drug interaction prediction, which evaluates possible interactions between various medications to guarantee patient safety and maximize therapeutic outcomes is a crucial component of healthcare. This work investigates the use of artificial intelligence (AI) methods for predicting drug interactions, with a particular emphasis on the combination of natural language processing, knowledge graphs, and machine learning algorithms. The manual curation and experime

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P, SONAJI, SUBRAMANIAN L, and RAJESH M. "Artificial intelligence-driven drug interaction prediction." World Journal of Biology Pharmacy and Health Sciences 17, no. 2 (2024): 297–305. https://doi.org/10.5281/zenodo.11296167.

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Artificial intelligence (AI) is developing at a rapid pace and this has led to revolutionary changes in many fields, including healthcare. Drug interaction prediction, which evaluates possible interactions between various medications to guarantee patient safety and maximize therapeutic outcomes is a crucial component of healthcare. This work investigates the use of artificial intelligence (AI) methods for predicting drug interactions, with a particular emphasis on the combination of natural language processing, knowledge graphs, and machine learning algorithms. The manual curation and experime

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Gottschlich, Daniela, Nadereh Saadati, Seyed Alireza Saadati, and Ayşe Şahin. "AI in Elderly Care: Understanding the Implications for Independence and Social Interaction." AI and Tech in Behavioral and Social Sciences 2, no. 3 (2024): 36–42. http://dx.doi.org/10.61838/kman.aitech.2.3.5.

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This study aims to explore the implications of AI in elderly care, specifically its impact on enhancing independence and social interaction among the elderly. Additionally, it seeks to identify and analyze the ethical and privacy concerns, technological challenges, and the potential for personalization and customization in AI applications in elderly care settings. Employing a qualitative research design, this study collected data through semi-structured interviews with a purposively sampled cohort of 17 participants, including healthcare professionals specializing in geriatric care, caregivers

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Yousefi, Zahra, Nadereh Saadati, and Seyed Alireza Saadati. "AI in Elderly Care: Understanding the Implications for Independence and Social Interaction." AI and Tech in Behavioral and Social Sciences 1, no. 4 (2023): 26–32. http://dx.doi.org/10.61838/kman.aitech.1.4.5.

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This study aims to explore the implications of AI in elderly care, specifically its impact on enhancing independence and social interaction among the elderly. Additionally, it seeks to identify and analyze the ethical and privacy concerns, technological challenges, and the potential for personalization and customization in AI applications in elderly care settings. Employing a qualitative research design, this study collected data through semi-structured interviews with a purposively sampled cohort of 17 participants, including healthcare professionals specializing in geriatric care, caregivers

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Shade, Marcia, Changmin Yan, Valerie Jones, and Julie Boron. "PERSONALITY BYTES: CRACKING THE CODE OF OLDER ADULTS ENGAGEMENT WITH AI." Innovation in Aging 8, Supplement_1 (2024): 582. https://doi.org/10.1093/geroni/igae098.1905.

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Abstract Despite stereotypes about older adults’ reluctance to embrace technology, smartphone and technological device adoption among this demographic is increasing, including the use of Artificial Intelligence (AI). However, perceptions about AI may hinder engagement in research among older adults. This study examined how older adults’ personalities influence their interaction with AI-driven interactive routines. Fifty community-dwelling older adults participated in a 12-week randomized pilot study, divided into standard and enhanced groups, interacting with conversational AI for intervention

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Pypenko, I. S. "Human and artificial intelligence interaction." International Journal of Science Annals 6, no. 2 (2023): 54–56. http://dx.doi.org/10.26697/ijsa.2023.2.7.

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Background and Aim of Study: The advent of artificial intelligence (AI) has changed our world forever. No matter what it is that we do, there will always be a place for AI in what we do. Controlling and managing this system of interactions is still within our power. However, the potential and the speed of developing AI-based information technology is so great that we may soon need to concede this primacy. The aim of the study: to justify whether artificial intelligence will become our assistant or, on the contrary, create problems; to identify what needs to be done to build a harmonious Human-

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Sreedharan, Sarath. "Human-Aware AI – A Foundational Framework for Human-AI Interaction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15455. http://dx.doi.org/10.1609/aaai.v37i13.26822.

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We are living through a revolutionary moment in AI history. We are seeing the development of impressive new AI systems at a rate that was unimaginable just a few years ago. However, AI's true potential to transform society remains unrealized, in no small part due to the inability of current systems to work effectively with people. A major hurdle to achieving such coordination is the inherent asymmetry between the AI system and its users. In this talk, I will discuss how the framework of Human-Aware AI (HAAI) provides us with the tools required to bridge this gap and support fluent and intuitiv

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Pypenko, I. S. "Human and Artificial Intelligence Interaction." International Journal of Science Annals 6, no. 2 (2023): 54–56. https://doi.org/10.26697/ijsa.2023.2.7.

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<strong>Background and Aim of Study</strong>: The advent of artificial intelligence (AI) has changed our world forever. No matter what it is that we do, there will always be a place for AI in what we do. Controlling and managing this system of interactions is still within our power. However, the potential and the speed of developing AI-based information technology is so great that we may soon need to concede this primacy.The aim of the study: to justify whether artificial intelligence will become our assistant or, on the contrary, create problems; to identify what needs to be done to build a h

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Armansyah, Muhammad. "The Influence of Human-AI Interaction in the Decision-Making Process in the Health Sector: A Study at Dr. M. Djamil General Hospital, Padang, Indonesia." Arkus 10, no. 2 (2024): 548–53. http://dx.doi.org/10.37275/arkus.v10i2.542.

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Artificial intelligence (AI) plays an increasingly important role in the health sector, including in the decision-making process. Human-AI interaction can help doctors diagnose diseases, provide treatment recommendations, and improve the quality of patient care. This research uses experimental studies to investigate the influence of human-AI interactions in the decision-making process in the health sector. Two groups of doctors were included: a group that used AI to aid decision making and a control group that did not use AI. The research results showed that the group of doctors who used AI ha

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Bareth, Rishikesh. "Hyper AI Chatbot: An Intelligent Chatbot Framework for Seamless Human-AI Interaction." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49171.

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ABSTRACT In the modern digital era, seamless interaction between users and intelligent systems has become essential for enhancing productivity and support across various domains. To address this need, Hyper AI has been developed—an intelligent AI-powered chatbot framework designed to facilitate natural and efficient human–AI communication. This system caters to a wide range of use cases such as general inquiries, workflow support, and context-aware responses across multiple sectors including education, enterprise, and customer service. Hyper AI is architected using Node.js (Express) to handle

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Li, Feiming, Xinyu Yan, Hongli Su, Rong Shen, and Gang Mao. "An Assessment of Human–AI Interaction Capability in the Generative AI Era: The Influence of Critical Thinking." Journal of Intelligence 13, no. 6 (2025): 62. https://doi.org/10.3390/jintelligence13060062.

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(1) Background: In the era of generative AI (GenAI), assessing AI literacy is essential for understanding how effectively non-expert users can interact with AI. However, existing assessment tools primarily focus on users’ understanding of AI principles or rely on self-reported scales, neglecting critical thinking and actual interaction capabilities. To address this gap, this study aims to design and validate evaluation indicators targeting the behavioral process of human–GenAI interactions and analyze the impact of critical thinking. (2) Methods: Grounded in information literacy and critical t

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Bi, Yifeng. "Tree narrative and AI interaction: Optimization and narrative bias handling prospects." Applied and Computational Engineering 71, no. 1 (2024): 163–67. http://dx.doi.org/10.54254/2755-2721/71/20241616.

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This paper reviews the current research on interactive narratives, particularly the tree narrative structure, analyzing the methods of player-AI interaction, the design of narrative branches, and the mechanisms by which player decisions influence the story's progression. It explores the definitions, classifications, and applications of tree narratives in gaming, emphasizing the significance of player-AI interaction and its impact on narrative outcomes. The study identifies and analyzes potential narrative deviations resulting from player-AI interactions within tree structures and introduces an

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Zou, Bin, Xin Guan, Yinghua Shao, and Peng Chen. "Supporting Speaking Practice by Social Network-Based Interaction in Artificial Intelligence (AI)-Assisted Language Learning." Sustainability 15, no. 4 (2023): 2872. http://dx.doi.org/10.3390/su15042872.

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In recent decades, the rapid development of artificial intelligence (AI) technology has led to the increasing use of AI speaking apps in foreign language learning. This research investigates the impact of social network-based interaction on students’ English speaking practice with the assistance of AI speaking apps in China. During the summer vacation, 70 students from different Chinese universities and majors were recruited for the experiment. They were required to practice speaking skills with AI apps for five weeks and were divided into two groups. Participants in the experimental group wer

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Krishnan, Ponnarasan. "Understanding Emotional AI Development, Techniques, and Ethical Considerations Interaction." International Journal of Science and Research (IJSR) 13, no. 9 (2024): 413–15. http://dx.doi.org/10.21275/sr24904001543.

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B K, Keerthana, and Arul Krishnan S. "Revolutionizing Voice Search Interaction Through Chatbots And Conversational AI." International Journal of Research Publication and Reviews 6, no. 4 (2025): 17259–68. https://doi.org/10.55248/gengpi.6.0425.16145.

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Niederwieser, Elias, Dietmar Siegele, and Dominik T. Matt. "AI-Driven ERP Systems." Zeitschrift für wirtschaftlichen Fabrikbetrieb 120, s1 (2025): 112–17. https://doi.org/10.1515/zwf-2025-0007.

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Abstract Large Language Models (LLM) offer significant potential for automating complex tasks across domains. This article presents a novel Enterprise Resource Planning (ERP) system that leverages LLM to fulfill customer requests by accessing the ERP database for real-time updates, modifications, and availability checks, enhancing interaction and efficiency. Using a graph-theoretic framework, the system supports stateful workflows with cycles, branching, and human-in-the-loop (HITL) interactions, allowing precise control over application flow. This approach redefines LLM deployment in ERP appl

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Shen, Fanxi. "A Study of Human-AI Interaction Patterns in Artificial Intelligence-Assisted Second Language Writing." International Journal of English Literature and Social Sciences 10, no. 2 (2025): 041–52. https://doi.org/10.22161/ijels.102.8.

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The aim of this project is to investigate human-computer interaction patterns in AI-assisted second language writing in order to explore students' strategies and patterns when collaborating with generative AI tools, as well as key hidden states in AI-assisted writing. The project utilizes Hidden Markov Models (HMM) and process mining techniques to analyze the potential patterns of student interactions with AI and evaluate the efficacy of different patterns. The findings will reveal the cognitive mechanisms of human-computer interaction in second language writing, expand the theoretical framewo

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Atzmueller, Martin. "Behavioral Link Analytics on Heterogeneous Human Interaction Networks." International Journal of Transdisciplinary Artificial Intelligence 2, no. 1 (2020): 26–48. http://dx.doi.org/10.35708/tai1869-126248.

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For designing and modeling Artificial Intelligence (AI) systems in the area of human-machine interaction, suitable approaches for user modeling are important in order to both capture user characteristics. Using multimodal data, this can be performed from various perspectives. Specifically, for modeling user interactions in human interaction networks, appropriate approaches for capturing those interactions, as well as to analyze them in order to extract meaningful patterns are important. Specifically, for modeling user behavior for the respective AI systems, we can make use of diverse heterogen

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Van Rooy, Dirk, and Kristof Vaes. "Harmonizing human-AI synergy: behavioral science in AI-integrated design." Proceedings of the Design Society 4 (May 2024): 2287–96. http://dx.doi.org/10.1017/pds.2024.231.

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AbstractThis paper explores the role of integrating behavioral science to refine human-AI interaction, essential for ensuring safety and efficiency. Advocating for empathetic, user-centric design, the paper illustrates how behavioral insights can effectively inform AI-integrated designs, making AI applications more intuitive and ethically aligned with diverse human needs. This approach can ultimately enrich interaction across systems, fostering a more harmonious human-AI synergy.

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Aga, Birgitte, and Coral Ann Manton. "Women reclaiming AI." Interactions 28, no. 1 (2021): 96. http://dx.doi.org/10.1145/3441676.

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Lehmann, Florian, and Daniel Buschek. "Examining Autocompletion as a Basic Concept for Interaction with Generative AI." i-com 19, no. 3 (2020): 251–64. http://dx.doi.org/10.1515/icom-2020-0025.

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Abstract Autocompletion is an approach that extends and continues partial user input. We propose to interpret autocompletion as a basic interaction concept in human-AI interaction. We first describe the concept of autocompletion and dissect its user interface and interaction elements, using the well-established textual autocompletion in search engines as an example. We then highlight how these elements reoccur in other application domains, such as code completion, GUI sketching, and layouting. This comparison and transfer highlights an inherent role of such intelligent systems to extend and co

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Dodge, Jonathan, Roli Khanna, Jed Irvine, et al. "After-Action Review for AI (AAR/AI)." ACM Transactions on Interactive Intelligent Systems 11, no. 3-4 (2021): 1–35. http://dx.doi.org/10.1145/3453173.

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Explainable AI is growing in importance as AI pervades modern society, but few have studied how explainable AI can directly support people trying to assess an AI agent. Without a rigorous process, people may approach assessment in ad hoc ways—leading to the possibility of wide variations in assessment of the same agent due only to variations in their processes. AAR, or After-Action Review, is a method some military organizations use to assess human agents, and it has been validated in many domains. Drawing upon this strategy, we derived an After-Action Review for AI (AAR/AI), to organize ways

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Olla, Phillip, Lauren Elliott, Mustafa Abumeeiz, Karen Mihelich, and Joshua Olson. "Promptology: Enhancing Human–AI Interaction in Large Language Models." Information 15, no. 10 (2024): 634. http://dx.doi.org/10.3390/info15100634.

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This study investigates the integration of generative AI in higher education and the development of the SPARRO framework, a structured approach to improving human–AI interaction in academic settings. This ethnographic study explores the integration of generative AI in healthcare and nursing education, detailing the development of the SPARRO framework based on observations of student and faculty interactions with AI tools across five courses. The study identifies key challenges such as AI hallucination, mistrust of AI-generated summaries, and the difficulty in formulating effective prompts. The

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Sun, Ning. "Investigating the Mediating Role of Team Communication in the Relationship between Leadership Style and Team Performance in AI-based Interaction Systems Development." Journal of Internet Services and Information Security 14, no. 4 (2024): 144–62. http://dx.doi.org/10.58346/jisis.2024.i4.008.

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This research investigates the complex interplay of leadership styles, team communication, and AI-interaction System development within the context of AI-based interaction systems development teams. The purpose is to elucidate how these variables intersect and collectively influence team performance. By exploring the dynamics specific to AI technologies, the study aims to contribute nuanced insights to the existing literature on leadership in technology-driven environments. Employing a quantitative approach, the study utilizes a structured questionnaire distributed to participants within AI de

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Ayub, Aamir. "Exploring Human-AI Communication in the Digital Era: A Need to Develop New Theories for Human-AI Communication." International Social Sciences and Education Journal 2, no. 2 (2025): 30–36. https://doi.org/10.61424/issej.v2i2.181.

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Over the last few years, a rather popular topic has emerged regarding how people communicate with artificial intelligence due to the availability of data or, more so, the interactions that they tend to mimic. The area of interest in the present work is the subtleties that may occur in the interaction between artificial intelligence and people for the development of a timely and adequate model comprehensively covering both the technical and psychological characteristics of such relationships. The present study draws on theories of communication and adopts discourse analysis to offer a different

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Churchill, Elizabeth, and Mikael Wiberg. "From Humans to AI: A Timely Debate on Human-AI Relations." Interactions 31, no. 1 (2024): 5. http://dx.doi.org/10.1145/3637223.

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Pandey, Siddhant, Nikhil Chandra Pandey, and Yash Bajaj. "AI Enabled Chatbot." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 1761–64. http://dx.doi.org/10.22214/ijraset.2024.60147.

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Abstract: As technology continues its rapid evolution, the integration of Artificial Intelligence (AI) into chatbot systems emerges as a pivotal frontier in reshaping human-computer interaction. This research paper embarks on a comprehensive exploration of the intricate synergy between AI technologies and chatbot development, with a primary focus on elevating user interactions to unprecedented levels of sophistication and intuitiveness. By delving into a nuanced understanding of methodologies, technologies, and challenges, this study aims to provide a holistic perspective on the present state

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Saxena, Dr Vikas, and Amardeep Gautam. "Can AI replace Human? A Study on Customer Perception and Attitude towards AI-Based Chatbot in Food Service." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2879–92. http://dx.doi.org/10.22214/ijraset.2022.42967.

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Abstract: When interaction comes in digital world means interacting with AI- enabled chatbot, this study focuses on customer perception and attitude to conclude can AI replaced the human? Primary survey was done for collected the customer data, 300 people were targeted out of which 219 responds. The study concludes that customer still prefer human interaction over chatbot even chatbot provides many benefits like quick response, easy to use and convenient to interact at any time. After conclusion there is future scope of this study and some recommendation for food service providers in India. Ke

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Yang, Diyi. "Human-AI Interaction in the Age of Large Language Models." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 66–67. http://dx.doi.org/10.1609/aaaiss.v3i1.31183.

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Large language models (LLMs) have revolutionized the way humans interact with AI systems, transforming a wide range of fields and disciplines. In this talk, I share two distinct approaches to empowering human-AI interaction using LLMs. The first one explores how LLMstransform computational social science, and how human-AI collaboration can reduce costs and improve the efficiency of social science research. The second part looks at social skill learning via LLMs by empowering therapists and learners with LLM-empowered feedback and deliberative practices. These two works demonstrate how human-AI

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Seufert, Sabine, Lukas Spirgi, Jan Delcker, Joana Heil, and Dirk Ifenthaler. "Interaction with AI robots: machine translators, text generators, chatbots & co - an empirical study among first-year students." Teaching and learning with and about artificial intelligence in vocational education and training 38, no. 1 (2024): 47–72. http://dx.doi.org/10.62350/qpiz5920.

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Artificial Intelligence (AI) increasingly influences people's lives by fostering enhanced interaction between humans and machines (Kim, 2022). AI robots are considered agents that undertake tasks traditionally performed by humans through AI programming. There are two categories of AI robots: those with human-like performance and those with human-like appearance for social interaction. Based on this concept, seven specific use cases were developed for this study. An online survey conducted among students (N = 636) in September 2022 (shortly before the release of ChatGPT) examined both the frequ

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Markose, Mariza, and Manoj K. Dewtwal. "Artificial Intelligence and Teacher Education: An Exploration." ISSRA Journal of Education, Linguistics and Literature 3, no. 6 (2023): 6–9. https://doi.org/10.5281/zenodo.10431993.

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<strong>Abstract: </strong>In the 21<sup>st</sup> Century, the confluence of Artificial intelligence (AI) and teacher education represents a transformative paradigm shift in the field of Teacher education. The rapid advancement in AI technologies has given rise to innovative approaches that hold immense potential for reshaping how educators are prepared and supported. AI systems offer effective support for online learning and teaching, including personalizing learning for students as well as the teacher community. In online learning, learner–instructor interaction has a profound impact o

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Shivendra, Singh* Shikha Lodhi. "Artificial Intelligence in Drug Interaction Prediction." International Journal of Pharmaceutical Sciences 3, no. 5 (2025): 2460–73. https://doi.org/10.5281/zenodo.15425567.

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Drug-drug interactions (DDIs) threaten patient safety and treatment results, especially with multiple drugs. Traditional methods like clinical trials and rule-based systems have limits in speed and scale. To improve detection, many now use artificial intelligence (AI) and machine learning (ML). These methods rely on a variety of data sources, including drug databases, patient records, and medical literature. They face challenges such as data quality, standardization, and missing information. Different AI models are used, like similarity-based systems, network models, graph neural networks, and

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Elmaresa, Maria Vina. "Navigating Virtual Bonds : Human-AI Friendship Dynamics through the Lens of Uncertainty Reduction Theory." Ultimacomm: Jurnal Ilmu Komunikasi 16, no. 1 (2024): 1–19. https://doi.org/10.31937/ultimacomm.v16i1.3542.

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This research examines the dynamics of human interaction and artificial intelligence (AI) through chatbots such as ChatGPT and character.ai. Data was collected using a qualitative approach and single case study methodology using two informants' in-depth interviews. Data analysis was carried out using pattern matching, emphasizing the axioms of uncertainty reduction theory. The research results show a decrease in uncertainty and skepticism towards AI through increased verbal communication and information seeking. However, this research also reveals limitations in applying uncertainty reduction

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Campion, John R., Donal B. O'Connor, and Conor Lahiff. "Human-artificial intelligence interaction in gastrointestinal endoscopy." World Journal of Gastrointestinal Endoscopy 16, no. 3 (2024): 126–35. http://dx.doi.org/10.4253/wjge.v16.i3.126.

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The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we us

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Rathi, Aman. "AI Virtual Mouse." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 852–58. https://doi.org/10.22214/ijraset.2025.70308.

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The method for creating a process of human-computer interaction has changed since the advancement of computer technology. The mouse is a great tool for the human-computer interaction. This study offers a way to move the pointer without using any technological devices. On the other hand, other hand moves can be applied for operations like drag and click objects. The suggested system will just need a camera as an input device. Along with additional tools, the system will need to be used with OpenCV and Python. The output from the camera shall be shown on a display that is connected so that the u

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Alexander Obaigbena, Oluwaseun Augustine Lottu, Ejike David Ugwuanyi, et al. "AI and human-robot interaction: A review of recent advances and challenges." GSC Advanced Research and Reviews 18, no. 2 (2024): 321–30. http://dx.doi.org/10.30574/gscarr.2024.18.2.0070.

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The integration of artificial intelligence (AI) into human-robot interaction (HRI) has witnessed significant advancements in recent years, revolutionizing the way humans and robots collaborate and coexist. This review provides a comprehensive overview of the latest breakthroughs in AI-driven HRI and identifies the challenges that lie ahead. Recent years have seen a surge in AI-driven capabilities that enhance human-robot interaction. Machine learning algorithms enable robots to adapt to user preferences and behaviors, creating personalized and intuitive interactions. Natural language processin

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