AI Tools Research Articles

Global initiatives and challenges in integrating artificial intelligence literacy in elementary education: Mapping policies and empirical literature

AbstractArtificial intelligence (AI) has permeated most facets of life in the 21st century and has rapidly transformed various aspects of modern society. From entertainment to education, these advanced technologies have achieved a high level of competency in skills that once necessitated human involvement. Given AI's potential impact, ensuring students are literate in AI will support the careful integration of these advanced technologies to achieve sustainable development goals. This review hence examines the avenues for integrating AI literacy into elementary education by analyzing current global initiatives focused on implementing AI literacy education. The purpose is to support innovations within the educational framework to develop a universally accessible AI literacy education program. In line with this purpose, this study explores worldwide AI literacy initiatives that use hands‐on activities, collaborative learning, and project‐based learning to introduce AI fundamentals to diverse learners. Limitations on the provision of AI literacy education are also discussed, including professional development, openness to AI tools, and other challenges. This review aims to inform global efforts to support universal access to AI literacy education, which can ensure equitable outcomes for all learners, emphasizing the need for collaborative efforts to support the development and delivery of quality AI literacy education.

How can artificial intelligence impact the evaluation phase of a company takeover? Contribution of an in-depth case

Purpose AI has a leading influence and impact on organizations’ strategies. Yet few studies address how AI tools can impact M&A negotiation and the takeover evaluation stages of firms. Through an in-depth case study, our work questions how such tools can help with key stages of screening and due diligence periods. Design/methodology/approach We conduct several interviews with founders of the acquiring firm (Group Impact), and we had access to a large amount of information on the launch of the takeover process (including the data room of negotiation documents). Findings We detail the phases in which AI saved time (nongenerative AI) and, with a view to the future, we detail how a generative version of AI could help in the due diligence and negotiation phases. Also, we address the sticking points that prevented the takeover from being finalized. Originality/value First, few studies address the negotiation stage of takeovers as part of these are not finalized and, for the others, researchers are present in the field when the operation is signed. Second, studies that address takeovers that have failed are rare, yet understanding the blocking factors are essential to continue a long-term external growth strategy.

AI and memes: Across the boundaries

Memes have become one of the most popular cultures encapsulated by digital artifacts across digital spaces of all kinds. Users can create memes by themselves but can also use online services or tools. Among these, AI tools are popularly used because they can quickly generate creative memes by exploring vast stores of preexisting resources. Depending on the configurations of their algorithms and storehouses of resources, different AI meme generators can create different memes with the same prompt; further, the same generator can create different memes at different times with the same prompt. In addition, AI-generated memes may include controversial content through images and captions or other components, unanticipated by the writer of the prompt. In this study, we will examine several AI meme generators by creating memes using identical prompts and a series of prompts and compare the outputs, with a focus on cross-world ethical issues through the theoretical lens of information domains (Burnett, 2015; Burnett & Burnett, 2019). The results will provide insights into learning, meme generation, and ethical issues related to the use of AI tools.

Fallacy as Foundation of Post-Truth Knowledge and Knowing in LIS

How can library and information science (LIS) better promote epistemic vigilance and critical ethics toward post-truth (i.e., harmful; false; mis/dis/mal) information? This preliminary critical philosophical investigation argues LIS must go beyond considering mis/dis/mal information, and instead examine how post-truth shapes the process of producing mis/dis/mal epistemology through fallacies. Drawing from insights related to epistemicide and epistemic injustice in LIS, we reconsider post-truth and the modes of justification validating false beliefs as knowledge. We operationalize Fallacy 1 (“deceptively bad arguments”) and Fallacy 2 (“false popular belief”) to consider post-truth knowledge production. LIS faces an immediate pedagogical imperative of preparing information professionals to equitably mitigate fallacious harms inflicted by fake news proliferation, wavering information literacy, and the largely uncritical popularization of AI systems and tools which forcefully facilitate knower interactions with post-truth information. The evolving notions of post-truth information requires a critical ethical revolution for LIS.

Are we ready? Generative AI and the LIS curriculum

The US Department of Education’s 2023 report highlights the importance of AI literacy for educators and other stakeholders (Cardona, Rodriguez & Ishmael, 2023). Analyzing this report, Lo (2023) notes that “for librarians, AI literacy could involve understanding how AI tools work, how they can be used to enhance library services, and how to navigate potential ethical issues related to AI” (p. 2). Southworth et al (2023) observe that technical aspects of AI are covered in the traditional STEM curricula but are largely absent outside of it. While there are efforts currently to provide education and training opportunities for librarians in addressing AI in their work, as well as literature that talks about how AI will impact various aspects of work in libraries, few have touched on the need for curriculum development to effectively address such disruptive technologies by identifying the competencies or threshold concepts that will be affected by them. The ubiquitous impact of AI has included applications in medical research as well as law, design, economics, and humanities, to name just a few. Agile practice and improved dissemination of best practices are called for, moving forward. How should LIS curriculum address AI and related issues to prepare future librarians and information professionals? This poster covers an in-progress scoping review of literature and a content analysis of the publicly available course descriptions of ALA accredited LIS programs, as well as their current mission statements and objectives to provide a snapshot of extant integration of this new and challenging technology.

A sociotechnical history of an AI video chatbot: Mapping AI chatbot innovation trajectory for information policy design

This study traces the patent citation network of U.S. Patent 11,107,465 Natural Conversation Storytelling System (Gustman et al, 2021) to map the innovation trajectory of AI Chatbots. This patented AI video chatbot enables the reanimation or digital resurrection of humans in video form. As GLAMs continue to experiment with new technologies such as historical figure chatbots to increase engagement through gamification and empathy, a critical examination of technologies that are designed to appear human-like is warranted as they often skirt credibility, accuracy, and likely insert bias. Furthermore, the rise of generative AI tools and the propensity for producing hallucinations (Christensen et al, 2024) increases ethical issues for the implications on humans and truth (Ranalli, 2022; Stahl and Eke, 2024). Patent citation network analysis is frequently conducted as a competitive strategy for mapping the trajectory of technological development and patented inventions (Érdi et al, 2013; Jain et al, 2020). Drawing from this method, innovation trajectory routes or pathways will be traced through the patent citations to identify the most prominent or critical patents in the AI video chatbot innovation trajectory and analyze which features of technical innovation are retained across its pathway. These methods are frequently utilized to understand changes, patterns, and development in innovation to predict economically viable futures and can be utilized to predict the needs for information policy (Frohmann, 1995) by understanding the implications asymmetrical power on society.

Different Perception and Use of ChatGPT in Writing Between American and International Students

The emergence of AI writers has significantly affected and reshaped the environment of academic research and paper writing in the world. Many university students relying on these AI tools for searching information and assisting their writing. However, there will be a difference existed in the utilization and perceptions of AI writers between American and international students. To find out potential difference, this study seeks to delve into the underlying factors that may contribute to this contrast. Employing the Technology Acceptance Model (TAM) as a theoretical framework, which investigates users' acceptance and adoption of technology, the research employs a mixed-method approach comprising surveys and interviews conducted among students from both American and international contexts. By integrating TAM into the methodology, the study aims to investigate the motivations to use of ChatGPT, an AI writing tool, and examine how linguistic and cultural differences influence the user experience of AI writers. Through this approach, the research sheds light on the nuanced ways in which AI writers are integrated into the academic workflows of students from diverse backgrounds. By comparing and contrasting the perspectives and practices of American and international students, the study will provide valuable insights into the interplay between technology, language, and culture in the educational domain. Ultimately, this research will contribute to a deeper understanding of the complex dynamics shaping the adoption and usage of AI writers among higher education students worldwide.

A Study of Undergraduates’ Behavioral Intention to Use Generative AI Tools—Ernie Bot as An Example

With the rapid advancement of artificial intelligence technology, its influence on various sectors is increasing. Ernie Bot, a leading tool in generative AI, has garnered significant attention and application. This study utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) and introduces the concepts of Perceived Risk and Information Quality to construct a model that examines the factors influencing undergraduates’ behavioral intention to use generative AI tools like Ernie Bot. Using questionnaire data collected from 175 undergraduates, a structural equation model was employed to analyze the relationships between variables. The findings indicate that Social Influence, Information Quality, and Performance Expectancy positively impact the Behavioral Intention to use Ernie Bot. However, Effort Expectancy was not found to be a significant determinant of Behavioral Intention among undergraduates. Additionally, the study revealed that Perceived Risk did not negatively affect Behavioral Intention. These results offer valuable insights for the design and development of generative AI products, suggesting that enhancing Information Quality and leveraging Social Influence could improve user acceptance.

Technology-agnostic framework for designing assessments in the era of artificial intelligence

There is an urgent need to understand and benefit from artificial intelligence within schools. However, government policies that focus on academic integrity and duty of care do not address how students can leverage AI to enhance their learning nor how teachers can intentionally design assessments to account for AI. The Australian Framework for Generative Artificial Intelligence in Schools suggests that assessments need to clearly state “how generative AI tools should or should not be used” while also permitting a “clear and unbiased evaluation of student ability”. This is a worthy aspiration, yet there are presently few tools and examples to guide teachers in creating assessments. This conceptual paper draws from the field of design to articulate a framework for developing assessments that focuses on AI dialogue and trace-augmented critical reflection.

Student’s Utilization and Assistance of AI Tools in Assessment Completion: Perceptions and Implications

The study investigates the utilization and impact of AI Tools' usage by students for their assessment completion. It also explores students’ perceptions and examines the implications of using AI technology on students’ learning and development process. A total of 145 students majorly between 21-25 of age studying in Higher Education Institutions in the Sultanate of Oman participated in the survey. Data was collected employing non-probability convenience sampling. Results indicated that improving writing skills, critical thinking and analysis were the top 3 ranked skills which get enhanced in students by AI tools. As to fairness in AI tools' usage, the majority of the students agreed that these tools should only be used for generating ideas and structuring assessments. Results also revealed that 50% of the students were of the opinion that their institutions do not effectively regulate the use of AI. However, responses to questions on utilizing AI indicated that out of the students who had been penalized, 35% had a reduction in marks, 30% were asked to resubmit the assessment, 26% were given warnings, while 10% were given a ‘fail’ grade. Formulating standardized policies and regulations on AI usage, proper dissemination, and their implementation were the general recommendations obtained from the study.

Human Computation, Equitable, and Innovative Future of Work AI Tools

As we enter an era where the synergy between AI technologies and human effort is paramount, the Future of Work is undergoing a radical transformation. Emerging AI tools will profoundly influence how we work, the tools we use, and the very nature of work itself. The ’Human Computation, Equitable, and Innovative Future of Work AI Tools’ workshop at HCOMP’24 aims to explore groundbreaking solutions for developing fair and inclusive AI tools that shape how we will work. This workshop will delve into the collaborative potential of human computation and artificial intelligence in crafting equitable Future of Work AI tools. Participants will critically examine the current challenges in designing fair and innovative AI systems for the evolving workplace, as well as strategies for effectively integrating human insights into these tools. The primary objective is to foster a rich discourse on scalable, sustainable solutions that promote equitable Future of Work tools for all, with a particular focus on empowering marginalized communities. By bringing together experts from diverse fields, we aim to catalyze ideas that bridge the gap between technological advancement and social equity.

Relationships That Matter: Four Perspectives on AI, Work, and Organizations

The past decade has witnessed a surge of interest in how AI will transform work and organizations, presenting scholars with the difficult task of studying emergent technologies. This article advances a relational perspective that emphasizes how firms and workers are situated in, and respond to, extra-organizational pressures and forces. We examine four sets of relationships that can frame scholarly engagement with AI: finance and technology, monopoly and competition, producers and adopters, and high-status and low-status workers. We argue that this perspective illuminates connections between levels of analysis that might otherwise seem unrelated, revealing how AI tools and practices are situated within, and contribute to, broader social, financial, and industrial dynamics.

Promptology: Enhancing Human–AI Interaction in Large Language Models

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 SPARRO framework addresses these challenges, offering a step-by-step guide for planning, prompt design, reviewing, and refining AI outputs. While the framework shows promise in improving AI integration, future research is needed to validate its applicability across other academic disciplines and assess its long-term impact on critical thinking and academic integrity. This study contributes to the growing body of research on AI in education, offering practical solutions for ethically and effectively integrating AI tools in academic settings.

Sketchar: Supporting Character Design and Illustration Prototyping Using Generative AI

Character design in games involves interdisciplinary collaborations, typically between designers who create the narrative content, and illustrators who realize the design vision. However, traditional workflows face challenges in communication due to the differing backgrounds of illustrators and designers, the latter with limited artistic abilities. To overcome these challenges, we created Sketchar, a Generative AI (GenAI) tool that allows designers to prototype game characters and generate images based on conceptual input, providing visual outcomes that can give immediate feedback and enhance communication with illustrators' next step in the design cycle. We conducted a mixed-method study to evaluate the interaction between game designers and Sketchar. We showed that the reference images generated in co-creating with Sketchar fostered refinement of design details and can be incorporated into real-world workflows. Moreover, designers without artistic backgrounds found the Sketchar workflow to be more expressive and worthwhile. This research demonstrates the potential of GenAI in enhancing interdisciplinary collaboration in the game industry, enabling designers to interact beyond their own limited expertise.

Generative AI as a catalyst for HRM practices: mediating effects of trust

This study investigated the impact of generative AI tools on human resource management practices, organizational commitment, employee engagement, and employee performance. The authors also investigated the mediating role of trust in the relationship between user perception and organizational commitment. A structured questionnaire was used to collect the data from the information technology industry employees to measure the 9 reflective constructs optimism, innovativeness, ease of use, usefulness, trust, organizational commitment, vigor, dedication, and employee performance. Two constructs, ease of use and usefulness, are modeled as a higher-order construct – User Perception, whereas vigor and Dedication are modeled as another higher-order construct, Employee Engagement. The mode-fit indices were assessed for both the higher- and lower-order constructs, and the model-fit indices for both the models, higher- and lower-order constructs reveal an excellent model fit. Positive and statistically significant impacts were observed between the study constructs. The impact of organizational commitment on employee engagement was positive and statistically significant, and in turn, the impact of employee engagement on employee performance was also positive and statistically significant. Trust partially mediated the relationship between user perception and organizational commitment, fostering enhanced employee engagement and performance. The theories of technology readiness, stimulus-organization-response, and technology acceptance model were utilized in the development of the study’s theoretical framework, which provided fresh perspectives on how employee engagement, organizational commitment, and performance are influenced by user experience and trust in the context of generative AI. Theoretically, it opens up new applications such as personalized education and services, digital art, and realistic virtual assistants that were previously unfeasible or impractical for automation. The practical implications are that the field of interdisciplinary research as well as the information technology industry will be greatly impacted by generative AI. To facilitate swift adoption, the principles of generative AI are conceptualized from model-, system-, and application-level perspectives in addition to a social-technical perspective, where they are explicated and defined. In the end, our research has given future scholars a significant research agenda that will enable them to study generative AI from different theoretical perspectives while incorporating the concepts from these theories.

The Impact of Artificial Intelligence Marketing on E-Commerce Sales

This review explores the influence of AI marketing on e-commerce sales, examining how AI-driven strategies affect key metrics such as customer acquisition and conversion rates. Given the growing importance of AI in online retail, this paper employs a critical review methodology, analyzing 50 documents from the Scopus database. The analysis reveals that AI tools like chatbots, personalization engines, and predictive analytics significantly enhance e-commerce performance. The study provides practical and theoretical contributions, offering recommendations for businesses and suggesting future research directions.

Automatic generation of physics items with Large Language Models (LLMs)

High-quality items are essential for producing reliable and valid assessments, offering valuable insights for decision-making processes. As the demand for items with strong psychometric properties increases for both summative and formative assessments, automatic item generation (AIG) has gained prominence. Research highlights the potential of large language models (LLMs) in the AIG process, noting the positive impact of generative AI tools like ChatGPT on educational assessments, recognized for their ability to generate various item types across different languages and subjects. This study fills a research gap by exploring how AI-generated items in secondary/high school physics aligned with educational taxonomy. It utilizes Bloom's taxonomy, a well-known framework for designing and categorizing assessment items across various cognitive levels, from low to high. It focuses on a preliminary assessment of LLMs ability to generate physics items that match the Bloom’s taxonomy application level. Two leading LLMs, ChatGPT (GPT-4) and Gemini, were chosen for their strong performance in creating high-quality educational content. The research utilized various prompts to generate items at different cognitive levels based on Bloom's taxonomy. These items were assessed using multiple criteria: clarity, accuracy, absence of misleading content, appropriate complexity, correct language use, alignment with the intended level of Bloom's taxonomy, solvability, and assurance of a single correct answer. The findings indicated that both ChatGPT and Gemini were skilled at generating physics assessment items, though their effectiveness varied based on the prompting methods used. Instructional prompts, particularly, resulted in excellent outputs from both models, producing items that were clear, precise, and consistently aligned with the Application level of Bloom's taxonomy.

EFL students' and teachers' perceptions of the ethical uses of AI tools

EFL students' and teachers' perceptions of the ethical uses of AI tools

Exploring generative AI in higher education: a RAG system to enhance student engagement with scientific literature.

This study explores the implementation and evaluation of OwlMentor, an AI-powered learning environment designed to assist university students in comprehending scientific texts. OwlMentor was developed participatorily and then integrated into a course, with development and evaluation taking place over two semesters. It offers features like document-based chats, automatic question generation, and quiz creation. We used the Technology Acceptance Model to assess system acceptance, examined learning outcomes, and explored the influence of general self-efficacy on system acceptance and OwlMentor use. The results indicated complex relationships between perceived ease of use, perceived usefulness, and actual use, suggesting the need for more dynamic models of system acceptance. Although no direct correlation between OwlMentor use and learning gains was found, descriptive results indicated higher gains among users compared to non-users. Additionally, general self-efficacy was strongly related to perceived usefulness, intention to use, and actual use of the system. These findings highlight the importance of aligning AI tools with students' needs and existing learning strategies to maximize their educational benefits.

AI Tools in LIS Research: Navigating Opportunities and Challenges for Scholarly Advancement

ABSTRACT This study examines the integration of AI tools in Library and Information Science (LIS) research to understand their implications on academic practices. Using a careful survey-based methodology, the study explores the advantages of AI tools, related challenges, and potential behavioural impacts, mainly focusing on whether they unintentionally promote satisfaction among researchers. Findings reveal an ideal position for AI tool utilisation, highlighting varied degrees of usage and perceived benefits like increased efficiency and expanded research scope. However, challenges encompass ethical concerns, integration complexities, and diverging opinions regarding the tools’ potential impact on researcher diligence. This study contributes significantly by addressing the less-explored behavioural implications of AI tool integration in research, offering refined insights into their multifaceted impact on academic practices. It underscores the need for a balanced and informed approach to AI integration, considering their transformative potential and ethical considerations in scholarly pursuits.