Artificial Intelligence Literacy Research Articles

Unraveling generative AI in BBC News: application, impact, literacy and governance

Purpose This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies. Design/methodology/approach This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI. Findings By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI. Originality/value Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.

Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.

Analysis of college students' attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible use

BackgroundIn recent years, the use of artificial intelligence (AI) in education has increased worldwide. The launch of the ChatGPT-3 posed great challenges for higher education, given its popularity among university students. The present study aimed to analyze the attitudes of university students toward the use of ChatGPTs in their academic activities.MethodThis study was oriented toward a quantitative approach and had a nonexperimental design. An online survey was administered to the 499 participants.ResultsThe findings of this study revealed a significant association between various factors and attitudes toward the use of the ChatGPT. The higher beta coefficients for responsible use (β=0.806***), the intention to use frequently (β=0.509***), and acceptance (β=0.441***) suggested that these are the strongest predictors of a positive attitude toward ChatGPT. The presence of positive emotions (β=0.418***) also plays a significant role. Conversely, risk (β=-0.104**) and boredom (β=-0.145**) demonstrate a negative yet less decisive influence. These results provide an enhanced understanding of how students perceive and utilize ChatGPTs, supporting a unified theory of user behavior in educational technology contexts.ConclusionEase of use, intention to use frequently, acceptance, and intention to verify information influenced the behavioral intention to use ChatGPT responsibly. On the one hand, this study provides suggestions for HEIs to improve their educational curricula to take advantage of the potential benefits of AI and contribute to AI literacy.

Beyond CheatBots: Examining Tensions in Teachers’ and Students’ Perceptions of Cheating and Learning with ChatGPT

As artificial intelligence (AI) is increasingly integrated into educational technologies, teachers and students must acquire new forms of AI literacy, including an understanding of responsible use of AI. In this study, we explored tensions in teachers’ and students’ opinions about what constitutes learning and cheating with AI. Using qualitative methods, we asked Pre-K through postsecondary writing teachers (n = 16) and a linguistically diverse group of students (n = 12) to consider examples of how students might use ChatGPT, rank them in order of how much they thought each student learned and cheated, and explain their rankings. Our study yielded three findings. First, teachers and students used similar criteria to determine their rankings. Second, teachers and students arrived at similar conclusions about learning with ChatGPT but different conclusions about cheating. Finally, disagreements centered on four main tensions between (1) using ChatGPT as a shortcut versus as a scaffold; (2) using ChatGPT to generate ideas versus language; (3) getting support from ChatGPT versus analogous support from other sources; and (4) learning from ChatGPT versus learning without. These findings underscore the importance of student voice in co-constructing norms around responsible AI use.

Generative Artificial Intelligence in the EFL Writing Context: Students' Literacy in Perspective

The birth of generative artificial intelligence (AI) has the potential of developing language learners' English as a foreign language (EFL) writing skills; however, there is a substantial need to ensure that these learners are literate of generative AI-based tools and their potential to enhance learning writing skills. Therefore, the study aims to detect language learners' generative AI literacy in learning EFL writing skills. To achieve this purpose, the descriptive-survey method was used; (278) respondents completed a 30-closed item questionnaire and answered three questions in a semi-structured interview. The results showed that language learners’ generative AI tools literacy in the EFL writing context is at a medium level. Also, the study sample’s responses were not affected by their specialization, but by their GPA and mastery level of using generative AI tools in learning EFL writing skills did impact their responses. The analysis of the interviews revolved around three major themes: Challenges, affordance, and suggestions. In light of the findings, recommendations and suggestions are presented.

Primary school students’ perceptions of artificial intelligence – for good or bad

AbstractSince the end of 2022, global discussions on Artificial Intelligence (AI) have surged, influencing diverse societal groups, such as teachers, students and policymakers. This case study focuses on Swedish primary school students aged 11–12. The aim is to examine their cognitive and affective perceptions of AI and their current usage. Data, comprising a pre-test, focus group interviews, and post-lesson evaluation reports, were analysed using a fusion of Mitcham’s philosophical framework of technology with a behavioural component, and the four basic pillars of AI literacy. Results revealed students’ cognitive perceptions encompassing AI as both a machine and a concept with or without human attributes. Affective perceptions were mixed, with students expressing positive views on AI’s support in studies and practical tasks, alongside concerns about rapid development, job loss, privacy invasion, and potential harm. Regarding AI usage, students initially explored various AI tools, emphasising the need for regulations to slow down and contemplate consequences. This study provides insights into primary school students perceptions and use of AI, serving as a foundation for further exploration of AI literacy in education contexts and considerations for policy makers to take into account, listening to children’s voices.

Combining Human and Artificial Intelligence for Enhanced AI Literacy in Higher Education

This paper seeks to contribute to the emergent literature on Artificial Intelligence (AI) literacy in higher education. Specifically, this convergent, mixed methods case study explores the impact of employing Generative AI (GenAI) tools and cyber-social teaching methods on the development of higher education students’ AI literacy. Three 8-week courses on advanced digital technologies for education in a graduate program in the College of Education at a mid-western US university served as the study sites. Data were based on 37 participants’ experiences with two different types of GenAI tools–a GenAI reviewer and GenAI image generator platforms. The application of the GenAI review tool relied on precision fine-tuning and transparency in AI-human interactions, while the AI image generation tools facilitated the participants’ reflection on their learning experiences and AI's role in education. Students’ interaction with both tools was designed to foster their learning regarding GenAI's strengths and limitations, and their responsible application in educational contexts. The findings revealed that the participants appeared to feel more comfortable using GenAI tools after their course experiences. The results also point to the students’ enhanced ability to understand and critically assess the value of AI applications in education. This study contributes to existing work on AI in higher education by introducing a novel pedagogical approach for AI literacy development showcasing the synergy between humans and artificial intelligence.

Transforming academic librarianship through AI reskilling: Insights from the GPT-4 exploration program

The emergence of Artificial Intelligence (AI) in academic librarianship necessitates a corresponding evolution in librarians' competencies. This case study examines the GPT-4 Exploration Program at the University of New Mexico's College of University Libraries and Learning Sciences, which aimed to foster a culture of continuous learning and innovation by providing hands-on experience with advanced AI technology. Drawing on Malcolm Knowles' Adult Learning Principles, the program's structure and implementation are analyzed, highlighting the diverse range of projects undertaken by participants. The study reveals that effective AI reskilling involves cultivating a culture of continuous learning, adaptability, and collaborative exploration, anchored in a practical, hands-on approach. Participants reported significant improvements in AI literacy and confidence in applying AI tools to their work. The program's success demonstrates the potential of well-structured reskilling initiatives in empowering library professionals to navigate the evolving landscape of AI in academic libraries. The case study offers insights and strategies for other academic libraries seeking to develop AI reskilling initiatives, emphasizing the importance of fostering a supportive learning environment, addressing technical and ethical challenges, and ensuring the long-term sustainability of such programs. The GPT-4 Exploration Program serves as a blueprint for integrating AI into library services and operations.

Assessing the Knowledge and Perception of Artificial Intelligence for Teaching and Research among Lecturers in the Faculties of Arts in Nigeria

This study assesses the knowledge of artificial intelligence (AI) for teaching and research among lecturers in the faculties of arts in Nigeria. Despite the growing recognition of AI's potential to enhance educational practices, there are significant gaps in educators' AI literacy. The research adopts a quantitative approach, surveying lecturers across Nigerian universities to gauge their awareness, engagement, and perceptions of AI integration. Results reveal a moderate level of AI awareness among respondents, with a notable interest in further training tailored to arts disciplines. Challenges such as technical barriers and limited resources hinder seamless AI integration, highlighting the need for targeted interventions and support mechanisms. Recommendations include enhanced training programs, infrastructure improvement, and ethical guidelines to facilitate responsible AI utilization in arts education. Addressing these challenges and fostering AI literacy among educators can create an enabling environment for leveraging AI to enhance teaching and research outcomes in Nigerian arts faculties.

Impact of artificial intelligence on health information literacy: guidance for healthcare professionals

Purpose This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all. Design/methodology/approach This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL. Findings Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices. Practical implications To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research. Originality/value This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

Tool or Tyrant: Guiding and Guarding Generative Artificial Intelligence Use in Nursing Education.

As artificial intelligence (AI) continues to evolve rapidly, its integration into nursing education is inevitable. This article presents a narrative exploring the implementation of generative AI in nursing education and offers a guide for its strategic use. The exploration begins with an examination of the broader societal impact and uses of artificial intelligence, recognizing its pervasive presence and the potential it holds. Thematic analysis of strengths, weaknesses, opportunities, and threats collected from nurse educators across the southeastern United States in this case-based descriptive study used four codes: time, innovation, critical thinking, and routine tasks. Findings from the qualitative analysis revealed the overarching themes that AI can serve as both a tool and a tyrant, offering opportunities for efficiency and innovation while posing challenges of transparency, ethical use, and AI literacy. By establishing ethical guidelines, fostering AI literacy, and promoting responsible implementation in nursing education with a clear articulation of expectations, nurse educators can guide and guard the use of generative AI. Despite the concerns, the transformative potential of generative AI to enhance teaching methodologies and prepare students for the interprofessional health-care workforce provides a multitude of innovative opportunities for teaching and learning.

Towards identifying the components of students' AI literacy: An exploratory study based on Hungarian higher education students' perceptions

With the advent of the popular use of artificial intelligence (AI), the higher education (HE) sector is now facing a new challenge regarding how to exploit the educational potentials of Human-computer interaction (HCI). Developing students’ AI literacy is now attracting the attention of the HE and HCI research community. This paper aims to explore HE students’ perceptions of efficient and critical use of AI tools, and to systematically map the potential components of AI literacy as a new 21st century skill for HE students. This study applied a qualitative, exploratory approach in the form of semi-structured interviews with HE students. Results indicate that the participants primarily use ChatGPT for tasks such as brainstorming, topic selection, searching for information, and translation. While many find it useful for creating and reformatting texts, some encountered challenges, including generality in responses, outdated information, and issues during exams. Students highlighted its effectiveness in various academic tasks, from writing essays and CVs to language learning and transcription. Instructors’ perspectives on ChatGPT varied, with some advocating for its integration, while others expressed concerns about job security and misinformation. The implications of the study call for a more systematic introduction and discussion around AI literacy in educational settings.

Higher Education’s Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania

Higher education is currently under a significant transformation due to the emergence of generative artificial intelligence (GenAI) technologies, the hype surrounding GenAI and the increasing influence of educational technology business groups over tertiary education. This commentary, prepared for the Special Issue of the Journal of University Teaching & Learning Practice (JUTLP) on “Enhancing student engagement using Artificial Intelligence (AI) and chatbots,” delves into the complex landscape of opportunities and threats that AI chatbots, including ChatGPT, introduce to the realm of higher education. We argue that while GenAI offers promise in enhancing pedagogy, research, administration, and student support, concerns around academic integrity, labour displacement, embedded biases, environmental sustainability, increased commercialisation, and regulatory gaps necessitate a critical approach. Our commentary advocates for the development of critical AI literacy among educators and students, emphasising the necessity to foster an environment of responsible innovation and informed use of AI. We posit that the successful integration of AI in higher education must be grounded in the principles of ethics, equity, and the prioritisation of educational aims and human values. By offering a critical and nuanced exploration of these issues, our commentary aims to contribute to the ongoing discourse on how higher education institutions can navigate the rise of GenAI, ensuring that technological advancements benefit all stakeholders while upholding core academic values.

AI literacy and its implications for prompt engineering strategies

Artificial intelligence technologies are rapidly advancing. As part of this development, large language models (LLMs) are increasingly being used when humans interact with systems based on artificial intelligence (AI), posing both new opportunities and challenges. When interacting with LLM-based AI system in a goal-directed manner, prompt engineering has evolved as a skill of formulating precise and well-structured instructions to elicit desired responses or information from the LLM, optimizing the effectiveness of the interaction. However, research on the perspectives of non-experts using LLM-based AI systems through prompt engineering and on how AI literacy affects prompting behavior is lacking. This aspect is particularly important when considering the implications of LLMs in the context of higher education. In this present study, we address this issue, introduce a skill-based approach to prompt engineering, and explicitly consider the role of non-experts' AI literacy (students) in their prompt engineering skills. We also provide qualitative insights into students’ intuitive behaviors towards LLM-based AI systems. The results show that higher-quality prompt engineering skills predict the quality of LLM output, suggesting that prompt engineering is indeed a required skill for the goal-directed use of generative AI tools. In addition, the results show that certain aspects of AI literacy can play a role in higher quality prompt engineering and targeted adaptation of LLMs within education. We, therefore, argue for the integration of AI educational content into current curricula to enable a hybrid intelligent society in which students can effectively use generative AI tools such as ChatGPT.

Impressing Artificial Intelligence: Automated Job Interview Training in Professional English Subjects

More organizations are using automated job interview platforms to screen candidates at the early stages of the recruitment process. These platforms enable job candidates to take automated video interviews remotely using their personal devices. The interviews are analysed by artificial intelligence (AI) powered algorithms to produce analytics which inform hiring decisions. In this article, we explore the use of automated job interviewing within professional English subjects delivered to undergraduate students in Hong Kong. The innovation was introduced to help students to keep up with recruitment practices, provide speaking practice and explore AI-powered evaluation as a form of feedback. As well as outlining how automated interviewing was integrated into our teaching practice, we discuss student feedback on the experience and offer suggestions for future teaching practice with this technology. We also highlight considerations for practitioners such as the acceptance and adoption of AI technologies into language learning provision and the development of AI literacy skills. AI technologies have much potential for language teaching and this article offers a practical exploration into one such technology: automated job interviewing.

Enhancing teacher AI literacy and integration through different types of cases in teacher professional development

Integrating artificial intelligence (AI) into teaching practices is increasingly vital for preparing students for a technology-centric future. This study examined the influence of a case-based AI professional development (PD) program on AI integration strategies and AI literacy among seven middle school science teachers. Employing three distinct case problems, from well-structured to ill-structured, the AI PD program aimed to stimulate teachers’ reflection on AI literacy development and encourage the construction of problem-solving and AI integration strategies within various pedagogical contexts. Analysis of video-recorded case discussions revealed that teachers primarily drew on personal experiences for collaborative problem-solving across the three cases. However, the complexity of the case problems influenced their approach to knowledge co-construction, and dealing with ill-structured problems promoted the application of new knowledge. Through analyzing the survey data, we found a marked increase in teachers’ AI literacy, particularly in the domain of knowing and understanding AI, suggesting a pivotal role for direct instruction that supports AI literacy growth. However, their application of this AI knowledge was limited during the case discussions, while other domains of teacher AI literacy were more frequently employed. The findings highlight the importance of combining direct instruction with case-based discussions in AI-related PD programs to bolster teacher AI literacy effectively. The research has implications for using a case-based learning approach during short-term PD initiatives and advocates the ongoing need for comprehensive AI literacy development to facilitate teachers’ AI integration in subject-specific teaching.

Examining artificial intelligence literacy among pre-service teachers for future classrooms

In the context of global integration and increasing reliance on Artificial Intelligence (AI) in education, evaluating the AI literacy of pre-service teachers is crucial. As future architects of educational systems, pre-service teachers must not only possess pedagogical expertise but also a strong foundation in AI literacy. This quantitative study examines AI literacy among 529 pre-service teachers in a Nigerian university, utilizing structural equation modeling (SEM) for comprehensive analysis. The research explores various dimensions of AI literacy, revealing that a profound understanding of AI significantly predicts positive outcomes in AI use, detection, ethics, creation, and problem-solving. However, no correlation exists between AI knowledge and emotion regulation or the assumption that active AI use enhances AI detection capabilities. The study identifies a trade-off between AI application and creation, emphasizing the ethical considerations intertwined with emotional and persuasive facets of AI use. It also supports the link between AI creation and problem-solving, emphasizing the foundational role of AI knowledge in shaping diverse aspects of AI literacy among pre-service teachers. The findings offer valuable insights for educators, administrators, policymakers, and researchers aiming to enhance AI literacy in pre-service teacher education programs.

Developing AI literacy in HRD: competencies, approaches, and implications

ABSTRACT In recent years, the surge of interest in Artificial Intelligence (AI) technology has been notable across global academic and industrial spheres. Human Resource Development (HRD) scholars have paid considerable attention to its application. Recognizing the importance of preparing employees and organisations for the AI era, this paper explores the concept of AI literacy, focusing on the required competencies from an HRD perspective. Based on research on the Technology Acceptance Model (TAM) in the context of AI, we analysed AI-related competencies from four aspects: skills, relevance, values, and knowledge. Although the current literature on AI literacy contributes to a comprehensive understanding and highlights its importance across multiple domains, we identified some areas that lack attention and necessitate further research. The paper concludes by proposing directions for future research, accentuating the critical role of HRD in equipping the workforce with AI literacy to adeptly navigate the continually evolving technological landscape, and highlighting the need for scholars to engage in discussions about AI literacy on an international scale.

A novel Conceptualization of AI Literacy and Empowering Employee Experience at Digital Workplace Using Generative AI and Augmented Analytics: A Survey

With the fast, rapid, and expeditious integration of Artificial Intelligence (AI) technologies, particularly Generative AI and Augmented Analytics, organizations are presented with new opportunities to transform their operations and empower their workforce. This paper explores the intersection of AI literacy, Generative AI, and Augmented Analytics to propose strategies for fostering a culture of AI fluency among employees. This paper reviews the literature on AI literacy and its potential implications for employee experience (Ex) in digital workplaces. AI literacy and competency is the ability to understand, interact with, and thoughtfully assess the applications and ramifications of artificial intelligence (AI) across diverse domains. The paper argues that AI literacy is a key competence for employees in the digital era, as it enables them to leverage the potential of generative AI and augmented analytics, two of the most promising technologies for enhancing Ex. Generative AI refers to the use of AI to create novel and diverse outputs, such as text, images, music, or designs, while augmented analytics drives potential capability to make use of Artificial Intelligence technologies to automate and augment data analysis and decision-making. The paper discusses how these technologies can empower employees to be more creative, productive, collaborative, and engaged in their work and the challenges and risks they pose. This paper additionally highlights the obstacles, deficiencies, and proposed pathways for further research advancement concerning AI technological competency and literacy, aiming to enhance the employee experience significantly in digital workplaces.

Effects of AI understanding-training on AI literacy, usage, self-determined interactions, and anthropomorphization with voice assistants

Intelligent voice assistants (IVAs) such as Alexa or Siri are voice-based Artificial Intelligence systems that help users with various everyday tasks using simple voice commands. However, users often only have a superficial understanding of how the Artificial Intelligence (AI) integrated into IVAs works, which leads to misunderstandings and potential risks of use. To promote self-determined interaction with IVAs, the development of specific AI-related skills, such as a comprehensive understanding of AI, is crucial. Based on learning psychology and media pedagogy principles, two online training modules were developed to deepen the understanding of AI concerning IVAs and enable self-determined interaction. A total of 99 participants took part in the training. The results show that the training promotes both understanding of AI and AI literacy. It also increases the intention to use IVAs, promotes a positive attitude, and enhances the willingness for self-determined interaction. In addition, the training contributes to a more realistic assessment of the IVAs' capabilities and reduces anthropomorphic perceptions. Overall, the study emphasizes the relevance of specific AI skills and shows how targeted training can contribute to improving these skills. Thus, the present work contributes to improving the availability of digital education programs.