Artificial Intelligence Literacy Research Articles

Artificial Intelligence Tools Usage: A Structural Equation Modeling of Undergraduates’ Technological Readiness, Self-Efficacy and Attitudes

AbstractThis study investigates the relationship between undergraduates’ technological readiness, self-efficacy, attitude, and usage of artificial intelligence (AI) tools. The study leverages the technology acceptance model (TAM) to explore the relationships among the study’s variables. The study’s participants are 176 undergraduate students from a public university in southwestern Nigeria. The Partial Least Square Structural Equation Modeling (PLS-SEM) was used to analyze the responses from the participants. The questionnaire has six constructs measured on a 5-point Likert scale. The results show that undergraduates’ technological self-efficacy determines their usage of AI tools and perception of AI tools’ ease of use, but this does not determine their perception of the AI tools’ usefulness and attitude towards AI tools usage. Also, technological readiness was found to determine the perception of the AI tools’ usefulness, perception of AI tools’ ease of use, and technological self-efficacy among undergraduates but does not determine their usage of AI tools and attitude towards AI tools usage. In addition, undergraduates’ attitude towards AI tools was considered the primary determinant of the usage of AI tools. It was concluded that some factors determine the adoption of AI tools, which are interrelated. Educators can play a pivotal role in empowering students to harness the power of AI tools by encouraging their usage under well-coordinated guidance rather than imposing outright restrictions. By fostering AI literacy and equipping students with the knowledge and skills to navigate these innovative technologies, educators can instil the confidence and competency needed to integrate AI tools into various academic activities seamlessly.

Artificial Intelligence in the Education of Teachers: A Qualitative Synthesis of the Cutting-Edge Research Literature

The integration of Artificial Intelligence (AI) into teacher education has been transformative, offering personalized learning experiences, enhanced professional development, and improved teaching methodologies. AI technologies such as Intelligent Tutoring Systems (ITS), AI-driven analytics, and automated assessment tools have become central to modern educational practices, significantly improving engagement, adaptability, and effectiveness. This study employs a qualitative thematic analysis of current literature on AI in teacher education, examining peer-reviewed articles and reports using thematic coding to identify key patterns, opportunities, and challenges. The findings reveal that AI enhances teacher education by providing personalized learning pathways, fostering critical thinking, and supporting ongoing professional growth. Technologies like ITS, Virtual Reality (VR), and AI-driven analytics have proven effective in promoting motivation and engagement among teachers. However, ethical challenges such as biases in AI systems and concerns regarding data privacy require continuous attention. Furthermore, a gap in teacher preparedness, particularly in developing AI literacy and integrating AI tools into classroom practices, is evident. Despite these challenges, AI offers substantial benefits, transforming teaching practices and enabling personalized, adaptive instruction that supports both teachers and students. The study emphasizes the need for comprehensive teacher training programs focusing on digital literacy and ethical AI use, ensuring educators can navigate an AI-enhanced educational environment effectively. This research contributes to the ongoing discourse by highlighting the need for ethical guidelines and robust teacher training programs, offering actionable insights for educators, policymakers, and institutions aiming to integrate AI into teacher education

2024 Governance of artificial intelligence and data in Australasian higher education

This whitepaper is a follow-up of the Australasian Council of Open and Digital Education (ACODE) survey in 2023 on the governance of artificial intelligence (AI) and data in Australasian higher education (Selvaratnam & Venaruzzo, 2023). The results then showed the guidelines and policies in this space were still in the early days. Ethical implications were also emerging in tandem with initiatives and the adoption of generative AI in institutions. The latest survey conducted over July and August 2024 is 24 is picking up on the recommendations of the 2023 paper to inform recommendations for practice and further assure quality and equity in higher education. To this end, the JISC AI Maturity Model for Education is used to gauge the sector’s growth in the governance of AI and data both in policy and practice. The outcomes show that the sector is mainly at the experimenting and exploring stage of maturity in engaging with AI. The challenges were mainly in operationalising AI in a comprehensive manner across the enterprise, including increasing AI literacy across staff and students. More institutions are addressing the ethical implications of AI since the last survey; however, it appears that social and emotional wellbeing, and psychological safety still have to be carefully considered.

Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy

There is growing recognition among researchers and stakeholders about the significant impact of artificial intelligence (AI) technology on classroom instruction. As a crucial element in developing AI literacy, AI education in K-12 schools is increasingly gaining attention. However, most existing research on K-12 AI education relies on experiential methodologies and suffers from a lack of quantitative analysis based on extensive classroom data, hindering a comprehensive depiction of AI education's current state at these educational levels. To address this gap, this article employs the advanced semantic understanding capabilities of large language models (LLMs) to create an intelligent analysis framework that identifies learning theories, pedagogical approaches, learning tools, and levels of AI literacy in AI classroom instruction. Compared with the results of manual analysis, analysis based on LLMs can achieve more than 90% consistency. Our findings, based on the analysis of 98 classroom instruction videos in central Chinese cities, reveal that current AI classroom instruction insufficiently foster AI literacy, with only 35.71% addressing higher-level skills such as evaluating and creating AI. AI ethics are even less commonly addressed, featured in just 5.1% of classroom instruction. We classified AI classroom instruction into three categories: conceptual (50%), heuristic (18.37%), and experimental (31.63%). Correlation analysis suggests a significant relationship between the adoption of pedagogical approaches and the development of advanced AI literacy. Specifically, integrating Project-based/Problem-based learning (PBL) with Collaborative learning appears effective in cultivating the capacity to evaluate and create AI.

Negotiating Meaning with Machines: AI's Role in Doctoral Writing Pedagogy

AbstractThis paper examines the integration of generative artificial intelligence (AI) in doctoral writing pedagogy. It explores how AI augments traditional teaching and composition processes, fosters a new paradigm of cognitive engagement and collaborative academic writing, and the broader ethical and social implications of human-AI writing in doctoral writing pedagogy. A community-engaged participatory research methodology was employed within a Doctor of Healthcare Administration program. Data were collected through discussion board messages, self-assessment papers, student reflections, and a focus group interview, and analyzed using thematic analysis. The research unearthed a hybrid human-AI writing process characterized by dynamic brainstorming, continuous negotiation of meaning, and comparative evaluation. These practices enhanced students' cognitive and metacognitive engagement, confidence, and learner agency, signifying a shift toward a collaborative approach to academic writing. The findings highlight the need for academic institutions to adapt policies and curricula to incorporate AI technologies ethically and responsibly. Emphasis on AI literacy and academic integrity is crucial for preparing graduates for an AI-integrated workforce. This study contributes to the understanding of AI's role in doctoral education, specifically doctoral writing development, presenting a novel perspective on the synergistic collaboration between students and AI in academic writing and its implications for institutional policies and writing pedagogy.

AI in obstetrics: Evaluating residents’ capabilities and interaction strategies with ChatGPT

In line with the digital transformation trend in medical training, students may resort to artificial intelligence (AI) for learning. This study assessed the interaction between obstetrics residents and ChatGPT during clinically oriented summative evaluations related to acute hepatic steatosis of pregnancy, and their self-reported competencies in information technology (IT) and AI.The participants in this semi-qualitative observational study were 14 obstetrics residents from two university hospitals. Students’ queries were categorized into three distinct types: third-party enquiries; search-engine-style queries; and GPT-centric prompts. Responses were compared against a standardized answer produced by ChatGPT with a Delphi-developed expert prompt. Data analysis employed descriptive statistics and correlation analysis to explore the relationship between AI/IT skills and response accuracy.The study participants showed moderate IT proficiency but low AI proficiency. Interaction with ChatGPT regarding clinical signs of acute hepatic steatosis gravidarum revealed a preference for third-party questioning, resulting in only 21% accurate responses due to misinterpretation of medical acronyms. No correlation was found between AI response accuracy and the residents’ self-assessed IT or AI skills, with most expressing dissatisfaction with their AI training. This study underlines the discrepancy between perceived and actual AI proficiency, highlighted by clinically inaccurate yet plausible AI responses – a manifestation of the ’stochastic parrot’ phenomenon.These findings advocate for the inclusion of structured AI literacy programmes in medical education, focusing on prompt engineering. These academic skills are essential to exploit AI’s potential in obstetrics and gynaecology. The ultimate aim is to optimize patient care in AI-augmented health care, and prevent misleading and unsafe knowledge acquisition.

The AI Revolution: A New Paradigm of Economic Order

Abstract Artificial intelligence (AI) constitutes a new technological revolution that has far-reaching, somehow paradigmatic implications for the economy and as well as for the society – for good and for evil. Given the specific properties of artificial intelligence this article analyses the economic consequences of AI on productivity and growth, the distribution of income as well as on firms, markets, and competition. It argues that artificial intelligence is creating a new economic order that requires new approaches to economic policy and regulation. Issues of how to maintain competition, protect consumer rights, promote AI literacy and preserve ethical standards are at the center of a holistic AI governance.

A snapshot of Bulgarian school teachers’ familiarity with, use of, and opinions on artificial intelligence at the threshold of its incorporation into the educational process

The current study was designed within a survey-based quantitative research methodology in order to investigate Bulgarian teachers’ familiarity with, use of, and attitudes toward artificial intelligence (AI) at the nascent stage of its integration into the Bulgarian education system. The survey was completed by 2252 teachers, of whom 16.30% were men and 83.70% were women. The majority of the teachers (72%) reported some level of familiarity with AI technology. Higher levels of familiarity were significantly associated with younger teachers (aged 20 to 29), mathematics, sciences, and technology subject areas, as well as the male gender. More than 50% of the teachers reported using AI technology in their instruction. Familiarity with AI technology was the only significant predictor of AI use, overriding the effect of other variables. The teachers used AI technology to prepare teaching, assessment, and homework materials; design individual and team tasks; and grade students' work. The teachers who regularly used AI technology were more favorable towards AI in contrast to those who seldom or never employed AI. The majority of the respondents recognized the need for teacher training in AI technology and expressed willingness to participate in teacher training initiatives. Therefore, the current situation requires teacher education programs to harness teachers’ needs by equipping them with relevant AI literacy and skills that they could apply to the critical adoption of suitable AI tools. Educational institutions and stakeholders should provide guidelines and consultancy regarding technical, methodological, and ethical issues concerning the instructional use of AI technology.

Understanding Student Perceptions of Artificial Intelligence as a Teammate

AbstractThis article examines students' opinions regarding the use of artificial intelligence (AI) as a teammate in solving complex problems. The overarching goal of the study is to explore the effectiveness of AI as a collaborative partner in educational settings. In the study, 15 groups of grade 9 students (59 students total) were assigned a challenging problem related to space exploration and were given access to an AI teammate. Following the task, the students participated in focus group discussions to gain insight into their perspectives on collaborating with AI. These discussions were analysed using thematic analysis to identify key themes. Epistemic Network Analysis was then used to quantify and visualise this data. The results suggest that students perceive AI with regard to two main themes: Trust in AI and the Capability of AI. The study's outcomes shed light on how students perceive AI and provide practical recommendations for educators to effectively incorporate AI into classrooms. Specifically, the recommendations include strategies for building student trust in AI systems through Explainable AI processes. This, in turn, encourages collaboration between humans and AI and promotes the development of AI literacy among students. The findings of this study are a valuable addition to the ongoing discussion on AI in education and offer actionable insights for educators to navigate the integration of AI technologies in support of student learning and growth. The scientific contribution of this study lies in its empirical investigation of student-AI interaction, providing evidence-based insights for enhancing educational practices.

AI art education - artificial or intelligent? Transformative pedagogic reflections from three art educators in Singapore

ABSTRACT With growing interest in the role and integration of Artificial Intelligence (AI) and AI pedagogies in transformative art education this paper voices the pedagogic reflections of three art educators in Singapore who have engaged AI in their art provision. It discusses and exemplifies the advantages (intelligent), like interdisciplinary opportunity, enhanced student engagement and increased connectionism, and limitations (artificial), like ethical and moral dilemmas and positioning, of AI integration in three art education settings showcasing how educators and learners engage. Model of pedagogic reflection is used to reveal how the educators provided transformative learning with AI, whilst showing the influence of personal art interests, technique modelling and professional reflections on teaching and making involving AI. Paper contributions include insights into and examples of AI practices and pedagogies (like AI literacy and prompt engineering), that are and could be used in art education, reflections on their successes and limits and commentary on the position of AI integration in the circular economy of art education. The paper is positioned to mobilize educator and student voice as an influential driver in the transformation of art education given ongoing developments in digital education futures.

From literacy to learning: The sequential mediation of attitudes and enjoyment in AI-assisted EFL education

Artificial intelligence (AI) has become increasingly prominent in English as a Foreign Language (EFL) education. However, research on the factors influencing learners' sustained engagement with AI-assisted learning is scarce. This study bridges this gap by investigating the impact of AI literacy, learner attitudes toward AI-assisted learning, and foreign language enjoyment (FLE) on Chinese EFL learners' continuance intention (CI) for AI-assisted EFL learning. A survey of 417 EFL learners was conducted, and structural equation modeling was utilized to elucidate the relationships between these variables. The findings reveal that AI literacy, learner attitudes, and FLE significantly predict the CI. Moreover, AI literacy indirectly influences CI by mediating learner attitudes and FLE. Finally, FLE mediates the relationship between learner attitudes and CI. The study verifies a sequential mediation model, providing new insights into EFL learners' cognitive and affective traits and extending current models on the continuance intention in AI-assisted EFL learning. The research underscores the necessity for educators and technology developers to consider learners' interests and feedback, which are crucial for the sustainable progress of AI-assisted EFL learning.

Will generative AI replace teachers in higher education? A study of teacher and student perceptions

This paper evaluates the potential of generative artificial intelligence (AI) in higher education, specifically its capacity to replace or assist human teachers. By reviewing relevant literature and analysing survey data from students and teachers, this mixed-methods study provides a comprehensive perspective on the future role of educators in the face of advancing generative AI technologies. An online survey was conducted to explore the perceptions of 399 university students and 184 teachers across different disciplines in eight higher education institutions in Hong Kong concerning the use of generative AI technologies. Findings suggest that although some believed generative AI may eventually replace teachers, the majority of participants argued that human teachers possess unique qualities, including critical thinking and emotions, which make them irreplaceable. Similarly, findings also emphasized the importance of social-emotional competencies developed through human interactions, something which generative AI technologies cannot currently replicate. Crucially, this study further found that students value and respect their human teachers, even as generative AI becomes more prevalent. As such, the authors propose that teachers can seek to effectively integrate generative AI to enhance teaching and learning without viewing it as their replacement. To do so, they must understand how generative AI can work well with teachers and students, avoid potential pitfalls, develop AI literacy, and address practical issues including ethics and privacy. Recommendations are offered on how universities, teachers, and students can adopt generative AI technologies in an approach that balances the strengths of human educators with generative AI technologies. As the future of education lies in the synergy between human teachers and generative AI, teachers, students, and universities should all understand and refine their unique qualities in order to effectively navigate the integration of generative AI, ensuring well-rounded and impactful learning experiences.

AI-enhanced education: exploring the impact of AI literacy on generation Z’s academic performance in Northern India

Purpose Artificial intelligence (AI) has the potential to address significant challenges in education, innovate learning and teaching practices and achieve SDG 4. However, existing literature often overlooks the behavioural aspects of students regarding AI in education, focusing predominantly on technical and pedagogical dimensions. Hence, this study aims to explore the significant relationships among AI literacy, AI usage, learning outcomes and academic performance of generation Z students in the Indian educational context. Design/methodology/approach The study used structural equation modelling (SEM) on Gen Z students born in the years 1997–2012 as a sample population for the research in the north Indian states like Punjab, Haryana, Himachal and regions like Chandigarh and N.C.R. Delhi. Findings The results established significant positive relationships between AI literacy, AI usage, AI learning outcomes and academic performance. Specifically, higher levels of AI literacy were associated with increased engagement with AI technologies and tools for learning purposes, leading to better learning outcomes and academic performance. The findings demonstrated that AI literacy plays a crucial role in providing effective learning experiences and fostering skills such as problem-solving and critical thinking among Gen Z students. Research limitations/implications The implications of the study include the significance of integrating AI education initiatives into curricula, prioritising professional development programmes for educators and making sure that every student has equitable access to AI technologies. Originality/value The study introduces a novel perspective by examining variables such as AI literacy, AI usage, AI learning outcomes and academic performance and developing a model that has not been previously studied. It provides a new discourse and proposes a framework uniquely combining AI-infused curriculum design, educator empowerment, robust assessment mechanisms and sustainable practices.

AI-Powered Curricula: Unpacking the Potential and Progress of Generative Technologies in Education

The aim of this narrative review is to understand how Artificial Intelligence (AI), specifically generative AI (e.g. ChatGPT) is thought to be used to support teachers in content authoring and curriculum production. Our findings indicate that Generative AI is envisioned as a co-design partner in enhancing educational content such as rubrics, lesson plans, interactive exercises, analogies, reflective questions, and case studies tailored to learning goals and instructional needs. The literature suggests that the key issues with the use of ChatGPT in education can be summarised mainly around its accuracy, and reliability, and its potential to lead to plagiarism. The analysis of empirical studies on the use of generative AI in higher education revealed first insights into themes such as ChatGPT's role in administrative tasks, varied perceptions of AI, generational differences in attitudes, multifunctional roles of ChatGPT, concerns about academic integrity and need for AI literacy, and the call for teaching philosophy reform, around assessment.

Development of a Home Economics Education Program for the Consumer Life Area to Enhance Artificial Intelligence Literacy in Middle School Students

This study aims to develop and validate a home economics education program focused on the consumer life area to enhance artificial intelligence (AI) literacy. To achieve this objective, a 10-session AI-consumer life integration education program was developed by analyzing literature on AI Literacy, AI curriculum, consumer education, and the home economics curriculum for the middle school consumer life area. The program’s validity was assessed by nine teachers using a four-point Likert scale. The average scores for each item and the content validity index (CVI) were calculated. Based on expert feedback, the program was revised and improved accordingly. The expert validity assessment of the lesson plans, teaching materials and learning resources resulted in an average score of 3.78 for all items and an average CVI of 0.96. For the overall program, the expert validity assessment yielded an average score of 3.72 for all items and an average CVI of 0.97. Since the content validity index for all questions was above 0.78, the program demonstrated high validity across achievement standards integration, learning objectives, content and teaching methods, motivation, and volume areas. This confirms its effectiveness as an educational program for enhancing AI literacy. This study is significant in terms of defining and identifying the components of AI literacy, developing an AI-integrated program encompassing the entire consumer life area and confirming the suitability of the home economics curriculum for enhancing digital consumer competencies and promoting sustainable consumption. Additionally, it highlights the potential to integrate AI into the home economics and consumer life area.

How AI Literacy Affects Students’ Educational Attainment in Online Learning: Testing a Structural Equation Model in Higher Education Context

Artificial intelligence (AI) has contributed to various facets of human lives for decades. Teachers and students must have competency in AI and AI-empowered applications, particularly when using online electronic platforms such as learning management systems (LMS). This study investigates the structural relationship between AI literacy, academic well-being, and educational attainment of Iranian undergraduate students. Using a convenience sampling approach, we selected 400 undergraduate students from virtual universities equipped with LMS platforms and facilities. We collected data using three instruments—an AI literacy scale, an academic well-being scale, and educational attainment scale—and analyzed the data using Smart-PLS3 software. Results showed that the hypothetical model had acceptable psychometrics (divergent and convergent validity, internal consistency, and composite reliability). Results also showed that the general model had goodness of fit. The study thus confirms the direct effect of AI on academic well-being and educational attainment. By measuring variables of academic well-being, we also show that AI literacy in China and Iran significantly affects educational attainment. These findings have implications for students, teachers, and educational administrators of universities and higher education institutes, providing knowledge about the educational uses of AI applications.

AI and the Future of Teaching: Preservice Teachers’ Reflections on the Use of Artificial Intelligence in Open and Distributed Learning

The rapid advancement of artificial intelligence (AI) in education underscores transformative prospects for open and distributed learning, encompassing distance, hybrid, and blended learning environments. This qualitative study, grounded in narrative inquiry, investigates the experiences and perceptions of 141 preservice teachers engaged with AI, mainly through ChatGPT, over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies. Employing Strauss and Corbin’s methodological approach of open, axial, and selective coding to analyze reflective narratives, the study unveils significant themes that underscore the dual nature of AI in education. Key findings reveal ChatGPT’s role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage. Specifically, ChatGPT was found to empower personalized learning and streamline procedures, yet challenges involving information accuracy and data security remained. The study significantly contributes to teacher education discourse by revealing AI’s complex educational impacts, highlighting an urgent need for comprehensive ethical AI literacy in teacher training curricula. However, critical ethical considerations and practical challenges involving academic integrity, information accuracy, and balanced AI use are also brought to light. The research also spotlights the need for responsible AI implementation in open and distributed learning to optimize educational outcomes while addressing potential risks. The study’s insights advocate for future-focused AI literacy frameworks that integrate technological adeptness with ethical considerations, preparing teacher candidates for an intelligent digital educational landscape.

Impact of Artificial Intelligence on Learning Management Systems: A Bibliometric Review

The field of artificial intelligence is drastically advancing. This study aims to provide an overview of the integration of artificial intelligence into learning management systems. This study followed a bibliometric review approach. Specifically, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, 256 documents from the Scopus and Web of Science (WoS) databases over the period of 2004–2023 were identified and examined. Besides an analysis of the documents within the existing literature, emerging themes and topics were identified, and directions and recommendations for future research are provided. Based on the outcomes, the use of artificial intelligence within learning management systems offers adaptive and personalized learning experiences, promotes active learning, and supports self-regulated learning in face-to-face, hybrid, and online learning environments. Additionally, learning management systems enriched with artificial intelligence can improve students’ learning outcomes, engagement, and motivation. Their ability to increase accessibility and ensure equal access to education by supporting open educational resources was evident. However, the need to develop effective design approaches, evaluation methods, and methodologies to successfully integrate them within classrooms emerged as an issue to be solved. Finally, the need to further explore education stakeholders’ artificial intelligence literacy also arose.

Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT)

The use of artificial intelligence (AI) tools, such as chatbots, has significantly increased in academia and research. The present study seeks to determine the key factors influencing chatbot adoption, as well as attempts to validate the unified theory of acceptance and use of technology (UTAUT) in the context of AI chatbots adoption among research scholars. The data for this study were collected through purposive sampling using a cross-sectional survey. The population of the study comprised research scholars enrolled in three public sector universities in Pakistan. The eight-factor proposed measurement model was estimated using confirmatory factor analysis (CFA) based on 30 valid items. The goodness of fit indices suggest a favourable fit for the model χ2 = 1.710, DF = 381; p = 0.000; IFI = .902; TLI = 0.886, CFI = 0.900, RMSEA = 0.056. Our research affirms that social influence, trust, and facilitating conditions play pivotal roles as primary predictors of behavioural intentions for AI chatbots adoption among scholars. The study suggests that the perceived risks associated with using AI chatbots due to their potential misuse can be minimized by effectively implementing AI user guidelines, and developing AI literacy among scholars. Information professionals and ethical libraries can play an important role in “building the bridge” between cutting-edge technology capabilities and information users’ needs and rights. The proposed eight-factor AI chatbots adoption model holds substantial potential in understanding the influence of performance expectancy, effort expectancy, social influence, trust, perceived risk, and facilitating conditions on behavioural intention to AI chatbots adoption. This study contributes to the limited body of research investigating the factors influencing AI chatbot adoption among research scholars using the UTAUT model with additional constructs for trust and perceived risk.

Artificial intelligence literacy, attitude, and teaching efficacy among Chinese university professors

Artificial intelligence literacy, attitude, and teaching efficacy among Chinese university professors