An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions

Generative AI (GenAI) has shown remarkable capabilities in generating diverse and realistic content across different formats like images, videos, and text. In Generative AI, human involvement is essential, thus HCI literature has investigated how to effectively create collaborations between humans and GenAI systems. However, the current literature lacks a comprehensive framework to better understand Human-GenAI Interactions, as the holistic aspects of human-centered GenAI systems are rarely analyzed systematically. In this paper, we present a survey of 291 papers, providing a novel taxonomy and analysis of Human-GenAI Interactions from both human and Gen-AI perspectives. The dimensions of design space include 1) Purposes of Using Generative AI, 2) Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of Engagement, 5) Application Domains, and 6) Evaluation Strategies. Our work is also timely at the current development stage of GenAI, where the Human-GenAI interaction design is of paramount importance. We also highlight challenges and opportunities to guide the design of Gen-AI systems and interactions towards the future design of human-centered Generative AI applications

Success Factors and Development Areas for the Implementation of Generative AI in Companies

With the significant increase in public interest in ChatGPT since its breakthrough following the public release in November 2022, an expanding array of application possibilities is being discovered. This heightened interest is also reflected in economic contexts and for businesses. These Generative AI (GenAI) models are believed to have the potential to contribute trillions of dollars in value to the global economy. Now, pioneering companies face the challenge of successfully leveraging this Generative AI technology to their advantage, positioning themselves successfully at the forefront of AI. The adoption of Generative AI proves to be neither straightforward nor simple for companies and is associated with various challenges. Within this thesis, these challenges will be identified by conducting a multiple-case study involving expert interviews. Practical insights will be obtained to identify the decisive factors for the successful adoption of Generative AI, and these insights will be translated into a hands-on implementation framework for companies. Keywords: ChatGPT Enterprise; Generative AI; GenAI; GenAI adoption; GenAI frameworkWith the significant increase in public interest in ChatGPT since its breakthrough following the public release in November 2022, an expanding array of application possibilities is being discovered. This heightened interest is also reflected in economic contexts and for businesses. These Generative AI (GenAI) models are believed to have the potential to contribute trillions of dollars in value to the global economy. Now, pioneering companies face the challenge of successfully leveraging this Generative AI technology to their advantage, positioning themselves successfully at the forefront of AI. The adoption of Generative AI proves to be neither straightforward nor simple for companies and is associated with various challenges. Within this thesis, these challenges will be identified by conducting a multiple-case study involving expert interviews. Practical insights will be obtained to identify the decisive factors for the successful adoption of Generative AI, and these insights will be translated into a hands-on implementation framework for companies. Keywords: ChatGPT Enterprise; Generative AI; GenAI; GenAI adoption; GenAI framewor

AI Literacy: Principles for Ethical Generative Artificial Intelligence

To realise the benefits of generative AI (GenAI) users, educational institutions, government, workplaces and developers need AI literacies incorporating principles to ensure the ethical and effective use, regulation and development of these technologies. This resource outlines some of the issues with GenAI and suggests seven principles for ethical generative AI. These principles have been developed through engagement with the applied AI literature and generative AI tools in the learning and teaching, and research space

GenAI in rule-based systems for IoMT security: Testing and evaluation

Generative AI (GenAI) represents a significant advancement in artificial intelligence research, offering numerous benefits and opening new avenues for innovation across various domains. In healthcare, Generative AI has shown promise in applications such as drug discovery, personalized medicine, and medical imaging. This paper examines the role of Generative AI in rule-based systems, where vulnerabilities are detected with the help of formal logic. In this context, the ruleset is generated and tested to evaluate the performance of rule-based systems with the aid of GenAI. The effectiveness of the GenAI tool was evaluated using a publicly available case study from a laboratory setting. The results show that using generative artificial intelligence in rule-based systems leads to increased creativity, continuous learning, and robust performance. GenAI responded to each use case and provided the desired results compared to traditional rule-based systems. This integration of advanced AI techniques with traditional rule-based systems ensures that these hybrid systems perform reliably and effectively

Evaluating the Use of Generative AI in Software Development Proposing a Tentative Framework

Artificial intelligence (AI), especially generative AI (GenAI), has significantly impacted society and industries, driven by its ability to generate original content from data patterns. Recent advancements, like ChatGPT, have highlighted GenAI's potential to boost productivity in software development. However, its rapid adoption also brings risks, such as job displacement. This underscores the need to explore both the opportunities and challenges GenAI offers, particularly its impact on professional roles and processes in software development. This Master's thesis aims to explore the use of generative AI within a software company in two ways. The first question revolves around understanding employees' perceptions of the use of generative AI related to their work within the company, by exploring opportunities and challenges it presents across various departments. The second question focuses on how to evaluate the use of generative AI in software development. The study was conducted as a qualitative case study at the software company Zenseact, where data was collected through interviews which enabled a detailed and comprehensive analysis of the opportunities and challenges GenAI entails. Findings indicate a general tendency to embrace generative AI, with varying degrees of enthusiasm and skepticism shaped by personal and professional views. Despite concerns about security and privacy the sentiment towards GenAI remains positive. Findings recognize opportunities to boost efficiency and creativity by automating tasks, improving information processing, and enhancing learning. Challenges include the reliability of GenAI outputs, ethical considerations, job market effects, and reskilling needs. Evaluating GenAI in software development involves both quantitative metrics like time savings and productivity, and qualitative measures such as user satisfaction and perceived value, to fully assess its impact and utility. Previous literature suggests that software development will be revolutionized through AI-generated code, however, the findings of this study imply that GenAI could have a larger impact on other parts of the software development process

Children's Overtrust and Shifting Perspectives of Generative AI

The capabilities of generative AI (genAI) have dramatically increased in recent times, and there are opportunities for children to leverage new features for personal and school-related endeavors. However, while the future of genAI is taking form, there remain potentially harmful limitations, such as generation of outputs with misinformation and bias. We ran a workshop study focused on ChatGPT to explore middle school girls' (N = 26) attitudes and reasoning about how genAI works. We focused on girls who are often disproportionately impacted by algorithmic bias. We found that: (1) middle school girls were initially overtrusting of genAI, (2) deliberate exposure to the limitations and mistakes of generative AI shifted this overtrust to disillusionment about genAI capabilities, though they were still optimistic for future possibilities of genAI, and (3) their ideas about school policy were nuanced. This work informs how children think about genAI like ChatGPT and its integration in learning settings

Behavioral Intention for AI Usage in Higher Education

This study sought to further understand the cognitive factors that influence undergraduate students\u27 behavioral intention to use generative AI. Generative AI\u27s presence in academic spaces opens the door for ethical and pedagogical questions. This study surveyed 51 undergraduate communication students to measure their attitudes, subjective norms, self efficacy and their behavioral intention to use GenAI for school work. The results of this study showed behavioral intent had a positive relationship with attitudes and subjective norms. The implications of these findings show that personal beliefs and the perceived beliefs of others are correlated to undergraduate students’ intent to use GenAI for academic purposes. Keywords: AI, GenAI, Theory of Planned Behavior, Undergraduat

The ETHICAL Framework for Responsible Generative AI Use in Research

The ETHICAL Framework for Responsible Generative AI Use in Research provides practical guidance for the ethical use of generative AI (GenAI) tools in the research process. The framework was developed using a constructivist case study approach that examined multiple GenAI tools in several authentic research contexts. This resource is a graphical representation of the framework which consists of seven principles - Examine policies and guidelines, Think about social impacts, Harness understanding of the technology, Indicate use, Critically engage with outputs, Access secure versions, and Look at user agreements. The framework makes a significant contribution by promoting the effective use of GenAI tools in a way that adequately considers research integrity

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models