Mapping AI Competencies in Library and Information Science Education: Evidence From Gen Z Students

Purpose This opinion essay aims to propose a human-centered, developmental framework for integrating artificial intelligence (AI) literacy into Library and Information Science (LIS) education and practice. It addresses the growing need for structured AI competency among LIS students and professionals, particularly in regions facing systemic educational and infrastructural challenges. Design/methodology/approach This paper draws on empirical findings from two interconnected studies led and co-led by the author: one explores the AI literacy (AiL) of LIS students across 11 countries in South Asia, Africa and the Middle East, while the other examines the AiL of LIS professionals in 6 countries within South Asia and the Middle East. These insights are synthesized with global policy guidance, existing AiL frameworks and practical teaching models to propose a four-stage human-centered AiL framework consisting of Foundational, Operational, Critical and Transformational domains. Findings The findings from the studies, along with related literature highlight widespread use of generative AI tools for basic academic and professional tasks, but limited understanding of ethical, technical and critical dimensions. Structural barriers – including inadequate curricula, unprepared faculty/professional and limited institutional infrastructure – further hinder effective AiL. The proposed human-centered AiL framework offers a practical roadmap for designing scalable, inclusive and reflective AiL strategies in LIS education and practice. Originality/value This essay presents a context-specific model grounded in underexplored regions and real-world professional environments. While conceptually comprehensive, the framework requires further empirical validation and adaptation based on institutional resources and country/regional educational policies.

Purpose The purpose of this study is to investigate generative artificial intelligence (AI) literacy integration within library and information science (LIS) educational programs, examining how academic curricula prepare future professionals to engage critically and competently with AI technologies. Design/methodology/approach A dual methodological approach combining a systematic scoping review of AI literature in LIS and a detailed analysis of syllabi from leading international LIS programs is used. The review identifies emerging trends and frameworks, while the syllabi analysis examines practical implementation. Findings The investigation reveals significant pedagogical fragmentation and global inequities in the integration of AI literacy in LIS programs, characterized by a lack of consensus around the frameworks used, institutional disparities and false dichotomies between technical and critical pedagogies. Research limitations/implications This study highlights the risks of stratification, where some programs offer sophisticated interdisciplinary training, while others provide only technical instruction, potentially fragmenting the global LIS community, at a time when coordinated responses to AI’s implications are crucial. Practical implications While many programs have implemented basic AI literacy, there is uneven global integration of critical AI literacy, which more closely aligns with the profession’s values of social justice and democratic well-being (ALA, 2023; IFLA, 2025). Social implications Research addresses the profession’s obligation to serve as ethical information stewards in an algorithmic age, emphasizing that graduates must leverage AI capabilities while critically examining implications for information equity, privacy and democratic participation (ALA, 2023; IFLA, 2025). Originality/value This research bridges the gap between conceptual developments in LIS research and practical educational implementation, providing the first comprehensive analysis of AI literacy operationalization across international LIS curricula.

Purpose This study aims to examine the perspectives of Library and Information Science (LIS) scholars in Nigerian universities on artificial intelligence (AI) literacy and the ethical use of generative AI in academic writing. Four (4) specific objectives guided the study. Design/methodology/approach A cross-sectional survey design was used, and data were gathered from 217 returned questionnaires completed by LIS scholars, representing a 60.5% response rate from 359 academics on the NALISE WhatsApp platform. Descriptive statistics, an independent-samples t-test, and Pearson correlation analysis were used to evaluate differences in AI literacy and the relationship between AI literacy and ethical prompting. Findings Findings revealed a generally low level of AI literacy, with respondents reporting challenges in evaluating AI-generated content and detecting AI-assisted plagiarism. Ethical prompting practices were not consistently adopted, although many scholars expressed concern about issues such as plagiarism, ghostwriting, and misinformation. Institutional support, including policies, training and formal guidelines, was widely perceived as inadequate. Significant differences existed between those who had used AI tools and those who had not, and AI literacy showed a strong positive correlation with ethical prompting. Originality/value Based on the authors’ knowledge and literature search, this is the first empirical study to comprehensively explore AI literacy and ethical use of Generative AI among LIS scholars in the context of Nigeria. It addresses a critical gap in the literature, which has predominantly focused on technologically advanced regions. The study introduces a unified framework that simultaneously investigates AI literacy, ethical prompting, perceived ethical risks and institutional practices, offering a holistic view of the challenges. It also contributes original empirical evidence on the relationship between AI literacy and ethical behavior.

Nursing students' artificial intelligence (AI) literacy, AI self-efficacy and AI self-competency: A cross-sectional design and structural equation model analysis.

Artificial Intelligence (AI) provides new tools and approaches for English as a Foreign Language (EFL) learning, yet it also brings new risks and challenges, such as AI anxiety. With the gradual adoption of AI in EFL learning, AI anxiety has brought about a variety of issues. In order to help educators understand students’ concerns and promote the use of AI for secondary school students’ EFL learning, this study took the Unified Theory of Acceptance and Use of Technology (UTAUT) and relevant aspects of AI anxiety as the theoretical foundation. Subsequently, this study analyzed the situation of AI anxiety among secondary school students and its relationship with students’ Behavioral Intention to use AI EFL learning tools. Data were collected through an online platform, with 293 valid responses from secondary school students in Beijing, China. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used to analyze the data. The validity and reliability of the scale were satisfied with nine constructs: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Behavioral Intention, AI Learning Anxiety, Job Replacement Anxiety, Sociotechnical Blindness Anxiety, and AI Configuration Anxiety. The results indicated: (1) Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions could all positively predict Behavioral Intention in different degrees, and Social Influence had the strongest effect; and (2) AI Learning Anxiety and Job Replacement Anxiety might indirectly and negatively predict Behavioral Intention through intermediate variables. Based on the analysis, the study suggests that educators should not only cultivate students’ AI literacy through comprehensive AI education, but also guide students to form correct emotions through scientific psychological interventions so that they can better use AI EFL learning tools.

This study investigates the evolving landscape of Artificial Intelligence (AI) literacy, acknowledging AI's transformative impact across various sectors in the twenty-first century. Starting from AI's inception to its current pervasive role in education, everyday life, and beyond, this paper explores the relevance and complexity of AI literacy in the modern world. To evaluate the current state of the literature on AI literacy, a systematic literature review was conducted with the objective of identifying thematic and recent research trends. Through a rigorous selection process involving 323 records from databases such as Web of Science, SCOPUS, ERIC, and IEEE Xplore, 87 high-quality studies have been analysed to identify central themes and definitions related to AI literacy. Our findings reveal that AI literacy extends beyond technical proficiency to encompass ethical considerations, societal impacts, and practical applications. Key themes identified include the ethical and social implications of AI, AI literacy in K-12 education, AI literacy curriculum development, and the integration of AI in education and workplaces. The study also highlights the importance of AI literacy models and frameworks for structuring education across diverse learning environments, as well as the significance of AI and digital interaction literacy. Additionally, our analysis of publication trends indicates a strong growth in AI literacy research, particularly in China and the United States, reflecting the global urgency of addressing AI literacy in policy and education. Conclusively, the research underscores the importance of an adaptable, comprehensive educational paradigm that incorporates AI literacy, reflecting its diverse interpretations and the dynamic nature of AI. The study advocates for interdisciplinary collaboration in developing AI literacy programs, emphasizing the need to equip future generations with the knowledge, skills, and ethical discernment to navigate an increasingly AI-driven world.

Artificial intelligence (AI) literacy plays a significant role in effectively communicating with AI generative chatbots for seeking health information or advice, as well as communicating with healthcare professionals and public health officials. The study aims to identify the factors influencing AI literacy and its subsequent influence on consumers’ behavioral intentions toward seeking health information using generative AI chatbots. A nine-factor measurement model (INS, ISE, IFO, AIH, ANX, AVD, AIL, BI, AIC) was developed with the help of the Theory of Planned Behavior (TPB) and tested using structural equation modeling. The results of SEM analysis demonstrate that IFO (β = 0.138, CR = 2.731, p < .05), ANX (β = 0.175, CR = 3.103, p < .05), and AVD (β = 0.158, CR = 3.855, p < .05) had a statistically positive significant influence on AI literacy. Similarly, BI (β = 0.128, CR = 2.423, p < .05) had a statistically positive significant influence on AIC. A positive but non-significant effect of INS on AIL was also observed (β=0.058, CR = 1.006, p > .05). AIL also had a positive but non-significant effect on BI (β=0.050, CR = 1.598, p > .05). Additionally, the results of model fit indices show adequate model fit values: χ2 = 1.679 DF = 1867; p = .000; IFI = 0.954; TLI = 0.951; CFI = 0.953; RMSEA = 0.031. The findings conclude that information overload, anxiety, and avoidance significantly influence AI literacy. On the other hand, information source exposure, AI hallucination, and information seeking do not significantly influence consumers’ AI literacy level. The study suggests that health professionals and staff could be equipped with better knowledge and skills related to AI applications in healthcare settings.

A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education

Artificial intelligence (AI) and algorithmic systems influence health workers' access, interpretation, and action on clinical and public health information, positioning them as intermediaries between algorithmically mediated outputs and patients, communities, and decision makers. This study examines how AI and algorithmic literacy are conceptualized and measured among health workers through a digital health literacy (DHL) lens. Using Arksey and O'Malley's scoping review framework, we searched Ovid MEDLINE, Ovid Embase, Scopus, IEEE Xplore, ACM Digital Library, Europe PMC, and arXiv for English language sources published between January 2020 and May 2025. Two reviewers screened records and extracted data using a theory informed charting framework grounded in Nutbeam's model (functional: basic understanding and use; critical: evaluation and ethics; communicative: interacting with AI systems and explaining AI-mediated information). We synthesized findings using descriptive statistics and a narrative synthesis. Twelve studies published between 2021 and 2025 met inclusion criteria. Evidence was concentrated in health professions education (10/12), primarily among medical (6/12) and nursing students (2/12), with no studies exploring public health practice. Explicit, theory-grounded definitions of AI literacy were uncommon, and links to DHL were only implied. AI literacy was frequently operationalized through self-reported instruments, commonly the Artificial Intelligence Literacy Scale (AILS; 3 studies), Meta Artificial Intelligence Literacy Scale (MAILS; 2 studies) and the Scale for the Assessment of Non-Experts' AI Literacy (SNAIL), alongside self-developed tools. Only one study explicitly defined and measured algorithmic literacy as a distinct construct; in other studies, algorithmic considerations appeared indirectly through recognizing AI presence in systems or evaluating AI generated content. Across studies, competencies aligned mainly with functional and critical dimensions of DHL, particularly awareness, use, evaluation, and ethics, while communicative literacies were infrequently assessed. AI and algorithmic literacy among health workers is underdeveloped, weakly integrated with digital health literacy, and inconsistently measured. Research prioritizes AI literacy using non-health-specific self-report tools and largely overlooks communicative competencies essential to clinical and public health practice. These findings point to the need for clearer conceptual alignment, health-specific measurement, and systems-based approaches to workforce readiness as AI-enabled tools expand across healthcare and public health.

Artificial intelligence is driving a new industrial revolution sweeping all aspects of human life, including technology, economy, science, politics, and art, like an unstoppable wave. This paper adopts a literature analysis methodology and extensively searches Chinese and foreign academic databases to analyse the relevant literature. A set of dimensions for AI literacy among LIS undergraduates has been developed in four first-level dimensions (AI knowledge, AI skills, AI attitude, AI ethics) and twelve second-level dimensions. Finally, this paper proposes continuing in-depth research on AI literacy and joining forces with multiple parties to promote universal education in AI literacy skills.

This study aims to develop a comprehensive competency framework for artificial intelligence (AI) literacy, delineating essential competencies and sub‐competencies. This framework and its potential variations, tailored to different learner groups (by educational level and discipline), can serve as a crucial reference for designing and implementing AI curricula. However, the research on AI literacy by target learners is still in its infancy, and the findings of several existing studies provide inconsistent guidelines for educational practices. Following the 2020 PRISMA guidelines, we searched the Web of Science, Scopus, and ScienceDirect databases to identify relevant studies published between January 2012 and October 2024. The quality of the included studies was evaluated using QualSyst. A total of 29 studies were identified, and their research findings were synthesized. Results show that at the K‐12 level, the required competencies include basic AI knowledge, device usage, and AI ethics. For higher education, the focus shifts to understanding data and algorithms, problem‐solving, and career‐related competencies. For general workforce, emphasis is placed on the interpretation and utilization of data and AI tools for specific careers, along with error detection and AI‐based decision‐making. This study connects the progression of specific learning objectives, which should be intensively addressed at each stage, to propose an AI literacy education pathway. We discuss the findings, potentials, and limitations of the derived competency framework for AI literacy, including its theoretical and practical implications and future research suggestions. Practitioner notes What is already known about this topic AI literacy is becoming increasingly important as AI technologies are integrated into various aspects of life and work. Research on AI literacy competencies across diverse learner groups and disciplines remains fragmented and inconsistent to guide educational practices. Studies providing a coherent pathway for AI literacy development throughout educational and working life are lacking. What this paper adds A comprehensive AI literacy competency framework consisting of 8 competencies and 18 sub‐competencies. Variations in AI literacy competencies with tailored configuration and prioritization across different learner groups by school levels and disciplines. A proposed pathway for developing AI literacy from K‐12 to higher education and workforce levels. Implications for practice and policy The framework can guide the design and implementation of AI curricula tailored to different learner characteristics and needs. Education should shift focus from teaching how to use AI to fostering competencies for critical, strategic, responsible and ethical integration of AI. Policies are needed to support a systematic pathway for lifelong AI literacy development from K‐12 education to workforce training.

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notes What is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy. Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy. Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes. What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions. Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy. Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy. Practitioners are able to use the AILQ to assess students' AI literacy development.

BACKGROUND Artificial intelligence (AI) literacy is increasingly essential for medical students. However, without systematic characterization of the subsidiary components and relevant drivers, designing targeted medical education interventions may be challenging. OBJECTIVE Systematically describe (1) the levels of and (2) the drivers of multidimensional AI literacy among Chinese medical students. METHODS A cross-sectional, descriptive analysis was conducted using data from a nationwide survey of Chinese medical students (n = 80,335) across 109 medical schools in 2024. AI literacy was assessed with a multidimensional instrument comprising three domains: knowledge, evaluating students’ self-reported proficiency in core areas of medical AI applications; attitude, reflecting their views on using AI for teaching and learning; and behavior, capturing the frequency and patterns of AI use. Factors associated with AI literacy included individual factors (i.e., demographic characteristics, family background, and enrollment motivation) and environmental factors (i.e., educational phase, type of education program, and tier of education program). RESULTS Respondents showed moderate to high levels of AI knowledge (mean, 76.0 [SD, 26.9]), followed by moderate AI attitude scores (mean, 71.6 [SD, 24.4]). In contrast, AI behavior scores were much lower (mean, 32.5 [SD, 28.5]), indicating little usage of AI tools. Of the individual factors, male students reported higher levels of AI attitude and behavior; both intrinsic and extrinsic motivation were positively associated with all three dimensions; advantaged family background was positively related to AI attitude and behavior, but not knowledge. Among the environmental factors, attending prestigious Double First-Class universities was positively associated with higher AI usage. Enrollment in long-track medical education programs was associated with higher AI attitude and behavior, while being in the clinical phase was negatively associated with both AI knowledge and behavior. Environmental factors moderated the associations between individual characteristics and AI literacy, potentially attenuating disparities. CONCLUSIONS Medical students reported moderate to high AI knowledge, moderate AI favorability, and low AI use. Individual characteristics and environmental factors were significantly associated with AI literacy, and environmental factors moderated the associations. The moderate AI literacy overall highlights the need for AI-related medical education, ideally with practical use and nuanced by drivers of inequitable distribution. CLINICALTRIAL This study is a cross-sectional observational analysis and does not involve a clinical trial; therefore, trial registration is not applicable.

This study explores variables associated with teachers’ Artificial Intelligence (AI) literacy, a key competency for effective and responsible AI integration in education. A total of 270 teachers completed an online survey including measures of AI literacy, AI acceptance, computational thinking, AI anxiety, and digital divide. Results revealed that all AI acceptance variables were positively associated with AI literacy, with hedonic motivation and willingness to use AI emerging as the strongest predictors. Computational thinking, AI anxiety, and digital divide also showed significant associations with AI literacy. The findings highlight the central role of teachers’ attitudes and motivational variables over technical and demographic variables. The study contributes to the understanding of how teachers engage with AI technologies and provides practical implications for designing professional development programs that enhance AI literacy and reduce barriers to AI adoption in educational contexts.

Artificial intelligence (AI) transforms healthcare data collection, analysis, and application, making AI proficiency a growing necessity across health professions.ObjectiveThis study aimed to examine the influence of demographic factors on AI literacy among Health Information (HI) professionals, identify key knowledge gaps and inform workforce-aligned training recommendations. This mixed-methods study analysed convenience-sampled survey data on AI literacy among HI professionals. Quantitative responses were examined with descriptive statistics, t-tests, Analysis of Variance (ANOVA), Spearman rank-order correlation, linear regression, geospatial analysis and a random forest to examine AI knowledge across demographic groups. The one qualitative open-ended response was analysed with latent Dirichlet allocation (LDA) topic modelling to identify themes.ResultsA total of 128 valid responses were analysed, including 22 participants who completed the technical knowledge section and 48 who responded to the open-ended question on AI education. Higher educational attainment and geographic location significantly predicted greater general AI literacy. However, no significant associations were found between AI literacy (general or technical) and age group, possession of non-health informatics credentials or prior AI experience. The cross-validated Random Forest models were assessed with and without oversampling. Accuracy was identical across both models (0.95), indicating that the overall prediction correctness of low versus high AI literacy was not affected by oversampling. The oversampled model had a superior ability to detect the minority class, making it more suitable for imbalanced classification tasks where recall is critical. This study identified several important knowledge gaps on the influence of demographic factors on AI literacy, which informs workforce-aligned training recommendations. These findings underscore the need for competency-based education to strengthen AI readiness within the health information workplace.Implications for health information management practice:The thematic analysis demonstrated the urgent need for AI knowledge, training and literacy for HI professionals and students. Themes from the LDA topic modelling informed the development of AI educational frameworks, structured into domains, subdomains and specific components of educational competencies. With multidisciplinary collaboration and further research, standardised AI core competencies for HI professionals could be created, validated by experts and adopted across educational programs to improve AI literacy in the HI field.