On the Measurement of AI Literacy Among Students in Higher Education: A Scoping Review

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This scoping review systematically maps the landscape of AI literacy measurement among higher education students. Following Arksey and O’Malley’s ( 2005 ) five-stage framework, a systematic search of five databases yielded 190 records, with 39 studies meeting inclusion criteria. The analysis examined definitions of AI literacy, measurement methods, tools, constructs, theoretical frameworks, related variables, and geographical contexts. Findings reveal a growing consensus around AI literacy as a multidimensional construct encompassing knowledge, application, evaluation, and ethics. Research is dominated by quantitative methods. The Artificial Intelligence Literacy Scale (AILS) (Wang et al., 2022 ) emerged as the most frequently used instrument, alongside newer validated tools and several study-specific measures. Only a small number of studies employed objective knowledge tests or mixed-methods approaches, highlighting a reliance on perceptual rather than demonstrated competencies. AI literacy was commonly studied in relation to attitudes, self-efficacy, and technology adoption intentions. Geographically, research is concentrated in East Asia, with smaller representation from Europe, North America, and the Middle East. This review underscores the field’s progress toward standardized measurement while identifying critical gaps, including overreliance on self-report, limited use of qualitative and performance-based assessments, conceptual ambiguities between AI and generative AI literacy, and geographical imbalance. Addressing these gaps will strengthen the validity and global applicability of AI literacy measurement, enabling more effective educational practices in higher education. generative AI AI literacy higher education Figures Figure 1 Figure 2 Figure 3 Introduction Artificial intelligence (AI) literacy has rapidly emerged as a critical competence in contemporary society, necessitated by the pervasive integration of AI technologies into education, work, and daily life (Long & Magerko, 2020; Ng et al., 2021a ). In higher education, students must be prepared to navigate an increasingly AI-driven academic and professional environment (O’Dea et al., 2024; K. Chen et al., 2025 ). As a result, researchers have devoted growing attention to conceptualizing and fostering AI literacy. Several scoping reviews have aimed to synthesize aspects of this growing body of work. For example, Sperling et al. ( 2024 ) explored AI literacy in teacher education. Laupichler et al. ( 2022 ) focused on AI literacy in high and adult education around theme, definitions, and courses teaching AI content. And Tang and Zang (2025) addressed AI literacy instruments for teachers. These studies demonstrate the value of mapping the field, but their focus has not been on the measurement of AI literacy among students in higher education. Addressing this need, the present scoping review systematically maps the current landscape of AI literacy measurement in higher education. Specifically, it identifies how AI literacy is defined, the methods and instruments used for its assessment, the constructs being measured, the theoretical frameworks applied, and the geographical contexts of student populations. In doing so, this review contributes to standardizing measurement approaches, highlighting validated tools for broader use, and revealing gaps for future inquiry. It aims to answer the following questions: What are the current conceptualizations and definitions of AI literacy in higher education? What research methods (e.g., quantitative, qualitative, mixed-methods) are used to study AI literacy in higher education? What measurement tools and scales are being used to assess AI literacy among university students, and what is their nature (e.g., self-assessment versus objective knowledge tests)? What are the constructs or dimensions of AI literacy assessed in studies among university students? What theoretical frameworks and models are most employed in studies investigating AI literacy? What other variables are most frequently studied in relation to AI literacy? What are the geographical contexts of student populations in higher education primarily represented in existing research on AI literacy? Methods This study followed the five-stage scoping review framework proposed by Arksey and O’Malley ( 2005 ), which includes: (1) identifying the research questions, (2) identifying relevant studies, (3) study selection, (4) charting the data, and (5) collating, summarizing, and reporting the results. Identifying Relevant Studies A systematic search was conducted on June 12, 2025, across five academic databases: Scopus, Web of Science, IEEE Xplore, PubMed, and ERIC. The search was limited to titles, abstracts, and keywords using the following search string: (("AI literacy" OR "AI read*") AND ("higher education" OR "tertiary education")) AND (student* OR learner*)) AND (instrument OR question* OR measur* OR survey OR scale) The initial search yielded 190 records. Study Selection All records were imported into reference management software (EndNote). Duplicates ( n = 74) and records for entire conference proceedings ( n = 3) were removed. This left 113 unique records for a two-stage screening process (see Fig. 1 ). In the first stage, titles and abstracts were screened against the inclusion and exclusion criteria (see Table 1 ), which included empirical studies written in English that explicitly measured AI literacy in higher education students between January 1, 2021, and June 12, 2025. This stage excluded 55 papers, leaving 58 for full-text review. Table 1 Inclusion and exclusion criteria Inclusion Exclusion AI literacy in higher ed Used AI literacy measurement tools Explicitly measured AI literacy in current students Empirical studies Written in English Published between 01/01/2000-6/12/2025 Studies focusing on only student perceptions and/or behaviors Grey literature Lit reviews AI literacy for faculty Non-empirical studies In the second stage, the full texts of the remaining 58 articles were reviewed, and 19 were excluded for not meeting the inclusion criteria. This resulted in a final selection of 39 studies for analysis in this scoping review. Charting the Data A data charting table was created to extract relevant information from the final 39 articles. The table included bibliographic details, study design, research method, measurement tools used, AI literacy constructs assessed, theoretical frameworks employed, related constructs studied, and the geographical location where the study was performed. Collating, Summarizing, and Reporting Results In the final stage, the charted data from the 39 papers was collated and synthesized using a mixed-analytical approach. A descriptive quantitative analysis was performed to identify frequencies and trends related to publication years, research methods, definitions, and measurement tools. This was complemented by a qualitative analysis to identify broader conceptual themes and trends across the literature. The combined results were then summarized narratively to map the current state of AI literacy measurement in higher education, as presented below. Limitations This scoping review, while systematic, has several limitations. The search strategy was restricted to five major academic databases and excluded grey literature, such as dissertations and institutional reports, which may contain relevant data. The exclusion of non-English language articles may have introduced a language bias and omitted valuable research from other regions. Finally, as a scoping review, this study maps the existing literature without assessing the methodological quality of the included articles. Therefore, the findings represent the state of the field as it is published, not necessarily the quality of the research being conducted. Results The analysis of the 39 included papers is organized below according to the seven research questions that guided this review. While not a research question, measurement of AI literacy among higher education students has grown in recent years (see Fig. 2 ). The search revealed the following concerning year of publication: RQ1: Conceptualizations and Definitions There is no one universally agreed upon definition of AI literacy, although this review found a few prominent sources for its definition and conceptualizations. The most frequently quoted definition is from Long and Magerko (2020), who define AI literacy as "a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace." Five papers quote them verbatim (Al- Abdullatif et al., 2024; Bewersdorff et al., 2025 ; K. Chen et al., 2024; Hornberger et al., 2023 ; and Ma & Chen, 2024 ) and 16 other papers cite, summarize, or synthesize their definition into their own. Other influential sources on the conceptualization of AI literacy come from Ng et al. ( 2021a , 2021b , 2023 )d Wang et al. ( 2022 ), which build upon this foundation by further specifying ethical and practical dimensions. Twenty-nine papers cite Ng and colleagues, who conceptualize AI literacy into four domains: know and understand AI, apply AI, evaluate and create AI, and AI ethics. Eighteen papers cite B. Wang et al. ( 2022 ), who similarly conceptualizes AI literacy into four components: awareness, usage, evaluation, and ethics. Ng and colleagues later created a framework with four new domains: affective, behavioral, cognitive, and ethical (Ng et al., 2024 ). RQ2: Research Methods The research landscape is dominated by quantitative approaches. Of the 39 studies, 31 were quantitative and 8 used a mixed-methods design. No purely qualitative studies were identified, although one mixed-methods study performed a quantitative sentiment analysis on qualitative data (Dong et al., 2025 ). The primary research aims of the included studies can be classified into three main categories (see Table 2 ). The largest group, comprising 26 studies, focused on measuring AI literacy and examining its relationship with other variables. The second category consists of studies focused on instrument development, with six studies aiming to validate a new AI literacy scale and one study focused on creating an objective knowledge test (Hornberger et al., 2023 ). The final group includes six studies that measured the effectiveness of a specific intervention designed to improve AI literacy. Table 2 Research aim classification Classification Count Author(s), Year Relationship of AI literacy to other variables 26 Al-Abdullatif et al., 2024; Asio, 2024 ; Bewersdorff et al., 2025 ; Bui et al, 2023; K. Chen et al., 2025 ; S. Y. Chen et al., 2024 ; Dadhich & Bhaumik, 2023 kçe et al., 2025 ; Hossain et al., 2025 ; Imjai et al., 2025 ; Lee et al., 2024 ; Lilje et al., 2024; O’Dea et al, 2024; Qi et al., 2025 ; Samngamjan et al., 2024 ; Sari et al., 2025 ; Schauer et al., 2025 ; Shi et al., 2025 ; Skalka et al., 2025 ; Sprigi & Seufert, 2025; Swartz et al., 2025 ; Syed et al., 2025 ; C. L. Wang et al., 2025 ; K. Wang et al., 2025 ; Wen et al., 2025 ; Xiao et al., 2024 AI literacy instrument development 7 Biagini et al., 2023 ; Han & Zhang, 2025 ; Hobeika et al., 2024 ; Hornberger et al., 2023 ; Ma & Chen, 2024 ; Topal et al., 2025 Z. Wang et al., 2025 Effectiveness of intervention on AI literacy 6 Dong et al., 2025 ; Kong et al., 2023 ; Korte et al., 2024 ; Lin et al., 2024; J. Wang, 2024 ; Younis, 2024 RQ3: Measurement Tools The primary method for assessing AI literacy and other variables is the use of a self-assessment scale, with 38 out of 39 studies using this approach in some capacity. The most utilized tool for AI literacy measurement is the Artificial Intelligence Literacy Scale (AILS) developed by B. Wang et al. ( 2022 ), which was used or adapted in ten studies. Other prominent instruments include the Scale for the Assessment of Non-Experts’ AI Literacy (SNAIL) by Laupichler et al. ( 2023 ), used in four studies, and a scale by Dai et al. (2020), used in three studies. The Meta AI Literacy Scale (MAILS) by Carolus et al. ( 2023 ) and the AI Literacy Questionnaire (AILQ) by Ng et al. ( 2024 ) were each used twice. In addition to these established tools, 13 papers reported using a scale developed by the researchers for their specific study. A smaller number of studies ( n = 5) employed objective knowledge tests to assess concrete understanding of AI concepts. One study was dedicated to the development and validation of such a test (Hornberger et al., 2023 ), which was then utilized in a subsequent study (Bewersdorff et al., 2025 ). Finally, several of the mixed-methods studies ( n = 8) incorporated qualitative tools to complement their quantitative data. These included open-ended survey questions, reflective diaries, and semi-structured interviews. RQ4: Measured Constructs AI literacy is consistently measured as a multi-dimensional construct (see Appendix). The appendix contains an exhaustive table of the AI constructs defined and measured by study. While specific terminology varies between studies, the measured constructs consistently converge around four core conceptual pillars: knowledge, application, evaluation, and ethics. The most common framework, used in ten studies and measured in the AILS scale (B. Wang et al., 2022 ), explicitly measures these four dimensions as awareness, usage, evaluation, and ethics. Other frameworks use different labels but capture similar ideas, such as technical understanding, practical application, critical appraisal (Laupichler et al., 2023 ). The cognitive component of these constructs ranges from deep technical AI knowledge (e.g., understanding machine learning steps and ability to create AI tools) (Hornberger et al., 2023 ) to general awareness of generative AI tools (O’Dea et al., 2024). RQ5: Theoretical Frameworks The studies included in this review draw upon a set of theoretical frameworks that can be broadly categorized into three main groups: psychology and technology adoption, psychology and learning, and AI-specific frameworks (see Table 3 ). The first group, psychology and technology adoption, includes models used to explain students' intentions to accept and use AI tools. The most prominent theory in this category is the theory of planned behavior (TPB), used in four studies (S. Y. Chen et al., 2024 ; Syed et al., 2025 ; C. L. Wang et al., 2025 ; Wen et al., 2025 ), This is followed by the technology acceptance model (TAM), used in two studies (Lijie et al., 2025 ; Syed et al., 2025 ). Other theories and models used once include the value-based model (VAM) (Al-Abdullatif et al., 2024), the control value theory of achievement emotions (Shi et al., 2025 ), the stage of change theory (Imjai et al., 2025 ), the interest development model (Bewersdorf et al., 2025), the information systems success model (ISSM) and the expectation confirmation model (ECM) (Qi et al., 2025 ), and the science, technology, and society framework (Sari et al., 2025 ) The second category consists of psychology and learning that frame the cognitive, motivational, and educational aspects of acquiring AI literacy. These include self-determination theory (SDT), used in three studies (Lijie et al., 2025 ; Shi et al., 2025 ; K. Wang et al., 2025 ), social cognitive theory, used in two studies (Bewersdorff et al., 2025 ; S. Y. Chen et al., 2024 ), and self-regulated learning (SRL), also used in two studies (Shi et al., 2025 ; K. Wang et al., 2025 ). Some studies utilized established educational frameworks like Bloom's Taxonomy (Han & Zhang, 2025 ; Kong et al., 2023 ; Korte et al., 2024 ) to structure the cognitive levels of AI literacy acquisition. The third category is AI-specific frameworks, which ground the research in a particular conceptualization of AI literacy. The framework developed by Ng and colleagues was most frequently utilized (e.g., O’Dea et al., 2024; Shi et al., 2025 ; Spirgi & Seufert, 2025 ; Skalka et al., 2025 ). Other studies drew upon models like the diamond model of AI literacy (Swartz et al., 2025 ), AI-TPACK (Younis, 2024 ), and E-GPPE-C intelligent learning model (J. Wang, 2024 ). Skalka et al. ( 2025 ) drew upon several models: the UNESCO AI competency framework for Students, the ED-AI lit framework, and the K-12 AI competency framework. Biagini et al. ( 2023 ), Dong et al. ( 2025 ), Kong et al. ( 2023 ), and Younis ( 2024 ) each drew upon unique AI frameworks. Table 3 Theoretical frameworks employed Framework Category Specific Theory/Model (Count) Psychology and technology adoption Theory of planned behavior ( n = 4) Technology acceptance model ( n = 2) Other single technology usage models ( n = 7) Psychology and learning Self-determination theory ( n = 3) Social cognitive theory ( n = 2) Self-regulated learning ( n = 2) Bloom’s taxonomy ( n = 3) AI-specific frameworks Ng et al.’s AI literacy framework ( n = 4) Other single AI literacy frameworks ( n = 10) RQ6: Related Constructs Outside of demographic data, which nearly every study collected, researchers frequently investigated AI literacy in conjunction with other variables. These can be organized into three broad categories. The first category is attitudes and perceptions, which captures students' feelings and beliefs about AI. This includes constructs such as attitudes toward AI, perceived usefulness, perceived enjoyment, interest in AI, and AI anxiety (K. Chen et al., 2025 ; Hobeika et al., 2024 ; Hornberger et al., 2023 ; Qi et al., 2025 ; Schauer et al., 2025 ; Syed et al., 2025 ; C. L. Wang et al., 2025 ). The second category involves cognitive and behavioral skills, which are personal attributes and competencies that may influence or be influenced by AI literacy. Key constructs in this area include self-efficacy (both general and AI-specific), self-competence, critical thinking, self-regulated learning, self-determination, digital skills, discipline-specific skills, GAI-driven wellbeing; writing performance, educational attainment, pedagogical knowledge, adaptability, and motivation (Asio, 2024 ; Bewersdorff et al., 2025 ; Bui et al., 2025 ; Dadhich & Bhaumik, 2023 ; Hornberger et al., 2023 ; Imjai et al., 2025 ; Lilje et al., 2025; Qi et al., 2025 ; Shi et al., 2025 l Wang et al., 2025 ; Z. Y. Wang et al., 2025 ; Xiao et al., 2024 ). The final group relates to technology use and intentions, which focuses on the practical application of AI. This includes variables such as use intention, continuous use, frequency of use, device ownership, and the direct outcomes of educational interventions designed to improve AI literacy (Al-Abdullatif et al., 2024; Bewersdorff et al., 2025 ; S. Y. Chen et al., 2024 ; Dong et al., 2025 kçe et al., 2025; Kong et al., 2023 ; Korte et al., 2024 ; Lilje et al., 2025; Lin et al., 2021 ; Qi et al., 2025 ; Sari et al., 2025 ; Spirgi et al., 2025; Syed et al., 2025 ; C. L. Wang et al., 2025 ; J. Wang, 2024 ; Wen et al., 2025 ; Younis, 2024 ). RQ7: Geographical Contexts of Student Populations The research on AI literacy measurement among students is geographically concentrated, with a significant majority of student populations originating from Asia. As illustrated in Fig. 3 , China (including Hong Kong) is the most represented country ( n = 13). The next most frequently represented countries are Germany ( n = 4), the United States ( n = 3), and Turkey ( n = 3). Students from the United Kingdom, Italy, Saudi Arabia, Taiwan, Malaysia, South Korea, Palestine, India, Thailand, and Indonesia were participants in two unique studies. And students from Vietnam, Philippines, Iran, Poland, Czech Republic, Slovakia, Lebanon, Morocco, Bangladesh, Pakistan, Lithuania, France, and Ukraine count were participants in one unique study. While this indicates that AI literacy is a topic of global interest, the current body of empirical research on its measurement is predominantly situated in East Asia, with a smaller but significant presence in Europe, the Middle East, and North America. The participants in all studies were higher education students from a wide range of disciplines, ages, and genders. Discussion This scoping review mapped the current landscape of AI literacy measurement in higher education, revealing a rapidly maturing yet uneven field. A central finding is the increasing convergence of definitions around AI literacy as a multi-dimensional construct, typically encompassing knowledge, application, evaluation, and ethics (Ng et al., 2021a ; B. Wang et al., 2022 ). This conceptual alignment has supported the growing adoption of standardized self-assessment instruments, particularly the Artificial Intelligence Literacy Scale (AILS), which has been heavily utilized in empirical studies (e.g., Hobeika et al., 2024 ; Ma & Chen, 2024 ). However, as the field coalesces around these core concepts, important questions about the boundaries of each construct emerge. For instance, future research will need to clarify whether ethics should be treated as a unique, standalone dimension or as a cross-cutting theme that is integrated within all other aspects of AI literacy. Despite this progress, several limitations in the literature constrain the robustness of current knowledge. First, the heavy reliance on self-assessment scales raises questions of validity. Self-perceptions may not accurately reflect students’ demonstrated competencies, particularly as generative AI tools shift expectations of what “literacy” entails. While objective instruments such as Hornberger et al.’s ( 2023 ) AI literacy test exist, their adoption remains limited. This reliance on perceptual measures risks reinforcing a narrow, potentially inflated understanding of AI literacy. Second, the methodological dominance of quantitative approaches reflects a field focused more on validation than exploration. The few examples of qualitative and mixed-methods research limits insights into students’ lived experiences with AI, their sociocultural contexts, and the meaning-making processes underlying AI use. Without these perspectives, the field risks privileging surface-level generalizations over deeper, situated understandings of AI literacy development. Third, conceptual ambiguities exist. Many measurement tools focus on broad technical knowledge of AI, while the rise of generative AI tools allows for engagement with AI without technical knowledge. O’Dea et al (2024) posits that a distinction should be made between AI literacy and generative AI literacy. This lack of clarity hinders the development of curricula and interventions tailored to today’s rapidly evolving AI landscape. Additionally, while many studies explored the relationship between AI literacy and other variables, there remains significant room to investigate how AI literacy interacts with a wider range of cognitive, affective, and behavioral factors. Finally, the geographical concentration of studies, predominantly in East Asia, with smaller representation from Europe and North America, raises concerns about the cultural generalizability of findings. Cultural and educational contexts may significantly shape how AI literacy is conceptualized, assessed, and enacted. The current imbalance risks reinforcing region-specific perspectives as universal benchmarks. Taken together, these trends suggest that while AI literacy measurement in higher education has advanced, it remains at a formative stage. Future progress will depend on expanding methodological diversity, clarifying constructs, and broadening global representation. Conclusion This scoping review provides a comprehensive overview of how AI literacy is being measured among students in higher education. The findings highlight an emerging consensus around core constructs and a growing reliance on validated self-assessment scales, particularly the AILS. At the same time, the field is marked by critical gaps: an overreliance on self-report, limited use of objective and performance-based measures, a lack of qualitative insight, conceptual ambiguities, and significant geographical imbalances. Future researchers can address these gaps to advance both scholarship and practice. There is a need to prioritize the development and validation of objective assessments that capture demonstrated knowledge and skills, alongside qualitative approaches that illuminate students’ lived experiences and cultural contexts. Future studies should also differentiate between technical AI literacy and the specific competencies required for engaging with generative AI. Expanding research beyond East Asia to include underrepresented regions, such as Central and South America, Africa, and Australia, is particularly important for building a more global and inclusive understanding of AI literacy in higher education. By addressing these methodological, conceptual, and geographical gaps, future research can move the field beyond fragmented measures toward more reliable and meaningful assessments of AI literacy. A stronger measurement foundation will not only improve research comparability but also inform the design of educational practices that prepare students for an AI-driven world Declarations Disclosure of potential conflicts of interest: The author declares no conflicts of interest. Research involving human participants and/or animals: This article is a scoping review of existing literature and does not contain any new studies with human participants or animals performed by the author. Informed consent: For this study, formal consent is not required as no new data was collected from human participants. Funding: The author did not receive support from any organization for the submitted work. 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3","display":"","copyAsset":false,"role":"figure","size":72421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeographical distribution\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8031490/v1/0134aaaf1f5db09c87b5ca96.jpg"},{"id":104515951,"identity":"dd809cbe-540d-4fb3-934d-56d66fda9f49","added_by":"auto","created_at":"2026-03-12 17:30:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8031490/v1/de37f5bf-4313-4643-b854-4841f88c54d3.pdf"},{"id":97107833,"identity":"2417b2b4-1770-4966-adf1-c89879976b38","added_by":"auto","created_at":"2025-12-01 05:35:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21513,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8031490/v1/275380b908e9de0c6271c4df.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"On the Measurement of AI Literacy Among Students in Higher Education: A Scoping Review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArtificial intelligence (AI) literacy has rapidly emerged as a critical competence in contemporary society, necessitated by the pervasive integration of AI technologies into education, work, and daily life (Long \u0026amp; Magerko, 2020; Ng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). In higher education, students must be prepared to navigate an increasingly AI-driven academic and professional environment (O\u0026rsquo;Dea et al., 2024; K. Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As a result, researchers have devoted growing attention to conceptualizing and fostering AI literacy.\u003c/p\u003e\u003cp\u003eSeveral scoping reviews have aimed to synthesize aspects of this growing body of work. For example, Sperling et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) explored AI literacy in teacher education. Laupichler et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) focused on AI literacy in high and adult education around theme, definitions, and courses teaching AI content. And Tang and Zang (2025) addressed AI literacy instruments for teachers. These studies demonstrate the value of mapping the field, but their focus has not been on the measurement of AI literacy among students in higher education.\u003c/p\u003e\u003cp\u003eAddressing this need, the present scoping review systematically maps the current landscape of AI literacy measurement in higher education. Specifically, it identifies how AI literacy is defined, the methods and instruments used for its assessment, the constructs being measured, the theoretical frameworks applied, and the geographical contexts of student populations. In doing so, this review contributes to standardizing measurement approaches, highlighting validated tools for broader use, and revealing gaps for future inquiry. It aims to answer the following questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are the current conceptualizations and definitions of AI literacy in higher education?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat research methods (e.g., quantitative, qualitative, mixed-methods) are used to study AI literacy in higher education?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat measurement tools and scales are being used to assess AI literacy among university students, and what is their nature (e.g., self-assessment versus objective knowledge tests)?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are the constructs or dimensions of AI literacy assessed in studies among university students?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat theoretical frameworks and models are most employed in studies investigating AI literacy?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat other variables are most frequently studied in relation to AI literacy?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat are the geographical contexts of student populations in higher education primarily represented in existing research on AI literacy?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study followed the five-stage scoping review framework proposed by Arksey and O\u0026rsquo;Malley (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which includes: (1) identifying the research questions, (2) identifying relevant studies, (3) study selection, (4) charting the data, and (5) collating, summarizing, and reporting the results.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eIdentifying Relevant Studies\u003c/h2\u003e\u003cp\u003eA systematic search was conducted on June 12, 2025, across five academic databases: Scopus, Web of Science, IEEE Xplore, PubMed, and ERIC. The search was limited to titles, abstracts, and keywords using the following search string:\u003c/p\u003e\u003cp\u003e((\"AI literacy\" OR \"AI read*\") AND (\"higher education\" OR \"tertiary education\")) AND (student* OR learner*)) AND (instrument OR question* OR measur* OR survey OR scale)\u003c/p\u003e\u003cp\u003eThe initial search yielded 190 records.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Selection\u003c/h3\u003e\n\u003cp\u003eAll records were imported into reference management software (EndNote). Duplicates (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;74) and records for entire conference proceedings (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) were removed. This left 113 unique records for a two-stage screening process (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the first stage, titles and abstracts were screened against the inclusion and exclusion criteria (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which included empirical studies written in English that explicitly measured AI literacy in higher education students between January 1, 2021, and June 12, 2025. This stage excluded 55 papers, leaving 58 for full-text review.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInclusion and exclusion criteria\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInclusion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExclusion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI literacy in higher ed\u003c/p\u003e\u003cp\u003eUsed AI literacy measurement tools\u003c/p\u003e\u003cp\u003eExplicitly measured AI literacy in current students\u003c/p\u003e\u003cp\u003eEmpirical studies\u003c/p\u003e\u003cp\u003eWritten in English\u003c/p\u003e\u003cp\u003ePublished between 01/01/2000-6/12/2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudies focusing on only student perceptions and/or behaviors\u003c/p\u003e\u003cp\u003eGrey literature\u003c/p\u003e\u003cp\u003eLit reviews\u003c/p\u003e\u003cp\u003eAI literacy for faculty\u003c/p\u003e\u003cp\u003eNon-empirical studies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn the second stage, the full texts of the remaining 58 articles were reviewed, and 19 were excluded for not meeting the inclusion criteria. This resulted in a final selection of 39 studies for analysis in this scoping review.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharting the Data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA data charting table was created to extract relevant information from the final 39 articles. The table included bibliographic details, study design, research method, measurement tools used, AI literacy constructs assessed, theoretical frameworks employed, related constructs studied, and the geographical location where the study was performed.\u003c/p\u003e\n\u003ch3\u003eCollating, Summarizing, and Reporting Results\u003c/h3\u003e\n\u003cp\u003eIn the final stage, the charted data from the 39 papers was collated and synthesized using a mixed-analytical approach. A descriptive quantitative analysis was performed to identify frequencies and trends related to publication years, research methods, definitions, and measurement tools. This was complemented by a qualitative analysis to identify broader conceptual themes and trends across the literature. The combined results were then summarized narratively to map the current state of AI literacy measurement in higher education, as presented below.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis scoping review, while systematic, has several limitations. The search strategy was restricted to five major academic databases and excluded grey literature, such as dissertations and institutional reports, which may contain relevant data. The exclusion of non-English language articles may have introduced a language bias and omitted valuable research from other regions. Finally, as a scoping review, this study maps the existing literature without assessing the methodological quality of the included articles. Therefore, the findings represent the state of the field as it is published, not necessarily the quality of the research being conducted.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis of the 39 included papers is organized below according to the seven research questions that guided this review. While not a research question, measurement of AI literacy among higher education students has grown in recent years (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The search revealed the following concerning year of publication:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRQ1: Conceptualizations and Definitions\u003c/h2\u003e\u003cp\u003eThere is no one universally agreed upon definition of AI literacy, although this review found a few prominent sources for its definition and conceptualizations. The most frequently quoted definition is from Long and Magerko (2020), who define AI literacy as \"a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.\" Five papers quote them verbatim (Al- Abdullatif et al., 2024; Bewersdorff et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; K. Chen et al., 2024; Hornberger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; and Ma \u0026amp; Chen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and 16 other papers cite, summarize, or synthesize their definition into their own. Other influential sources on the conceptualization of AI literacy come from Ng et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)d Wang et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which build upon this foundation by further specifying ethical and practical dimensions. Twenty-nine papers cite Ng and colleagues, who conceptualize AI literacy into four domains: know and understand AI, apply AI, evaluate and create AI, and AI ethics. Eighteen papers cite B. Wang et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who similarly conceptualizes AI literacy into four components: awareness, usage, evaluation, and ethics. Ng and colleagues later created a framework with four new domains: affective, behavioral, cognitive, and ethical (Ng et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRQ2: Research Methods\u003c/h3\u003e\n\u003cp\u003eThe research landscape is dominated by quantitative approaches. Of the 39 studies, 31 were quantitative and 8 used a mixed-methods design. No purely qualitative studies were identified, although one mixed-methods study performed a quantitative sentiment analysis on qualitative data (Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The primary research aims of the included studies can be classified into three main categories (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The largest group, comprising 26 studies, focused on measuring AI literacy and examining its relationship with other variables. The second category consists of studies focused on instrument development, with six studies aiming to validate a new AI literacy scale and one study focused on creating an objective knowledge test (Hornberger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The final group includes six studies that measured the effectiveness of a specific intervention designed to improve AI literacy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResearch aim classification\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassification\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAuthor(s), Year\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship of AI literacy to other variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAl-Abdullatif et al., 2024; Asio, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bewersdorff et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bui et al, 2023; K. Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; S. Y. Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dadhich \u0026amp; Bhaumik, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003ek\u0026ccedil;e et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hossain et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Imjai et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lilje et al., 2024; O\u0026rsquo;Dea et al, 2024; Qi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Samngamjan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sari et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Schauer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Skalka et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sprigi \u0026amp; Seufert, 2025; Swartz et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Syed et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; C. L. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; K. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI literacy instrument development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBiagini et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Han \u0026amp; Zhang, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hobeika et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hornberger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ma \u0026amp; Chen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Topal et al., 2025\u003c/p\u003e\u003cp\u003eZ. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffectiveness of intervention on AI literacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Korte et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lin et al., 2024; \u003c/p\u003e\u003cp\u003eJ. Wang, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Younis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eRQ3: Measurement Tools\u003c/h3\u003e\n\u003cp\u003eThe primary method for assessing AI literacy and other variables is the use of a self-assessment scale, with 38 out of 39 studies using this approach in some capacity. The most utilized tool for AI literacy measurement is the Artificial Intelligence Literacy Scale (AILS) developed by B. Wang et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which was used or adapted in ten studies. Other prominent instruments include the Scale for the Assessment of Non-Experts\u0026rsquo; AI Literacy (SNAIL) by Laupichler et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), used in four studies, and a scale by Dai et al. (2020), used in three studies. The Meta AI Literacy Scale (MAILS) by Carolus et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the AI Literacy Questionnaire (AILQ) by Ng et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were each used twice. In addition to these established tools, 13 papers reported using a scale developed by the researchers for their specific study.\u003c/p\u003e\u003cp\u003eA smaller number of studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) employed objective knowledge tests to assess concrete understanding of AI concepts. One study was dedicated to the development and validation of such a test (Hornberger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which was then utilized in a subsequent study (Bewersdorff et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, several of the mixed-methods studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) incorporated qualitative tools to complement their quantitative data. These included open-ended survey questions, reflective diaries, and semi-structured interviews.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRQ4: Measured Constructs\u003c/h2\u003e\u003cp\u003eAI literacy is consistently measured as a multi-dimensional construct (see Appendix). The appendix contains an exhaustive table of the AI constructs defined and measured by study. While specific terminology varies between studies, the measured constructs consistently converge around four core conceptual pillars: knowledge, application, evaluation, and ethics. The most common framework, used in ten studies and measured in the AILS scale (B. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), explicitly measures these four dimensions as awareness, usage, evaluation, and ethics. Other frameworks use different labels but capture similar ideas, such as technical understanding, practical application, critical appraisal (Laupichler et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The cognitive component of these constructs ranges from deep technical AI knowledge (e.g., understanding machine learning steps and ability to create AI tools) (Hornberger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to general awareness of generative AI tools (O\u0026rsquo;Dea et al., 2024).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRQ5: Theoretical Frameworks\u003c/h2\u003e\u003cp\u003eThe studies included in this review draw upon a set of theoretical frameworks that can be broadly categorized into three main groups: psychology and technology adoption, psychology and learning, and AI-specific frameworks (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The first group, psychology and technology adoption, includes models used to explain students' intentions to accept and use AI tools. The most prominent theory in this category is the theory of planned behavior (TPB), used in four studies (S. Y. Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Syed et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; C. L. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), This is followed by the technology acceptance model (TAM), used in two studies (Lijie et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Syed et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Other theories and models used once include the value-based model (VAM) (Al-Abdullatif et al., 2024), the control value theory of achievement emotions (Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the stage of change theory (Imjai et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), the interest development model (Bewersdorf et al., 2025), the information systems success model (ISSM) and the expectation confirmation model (ECM) (Qi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and the science, technology, and society framework (Sari et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe second category consists of psychology and learning that frame the cognitive, motivational, and educational aspects of acquiring AI literacy. These include self-determination theory (SDT), used in three studies (Lijie et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; K. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), social cognitive theory, used in two studies (Bewersdorff et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; S. Y. Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and self-regulated learning (SRL), also used in two studies (Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; K. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Some studies utilized established educational frameworks like Bloom's Taxonomy (Han \u0026amp; Zhang, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Korte et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) to structure the cognitive levels of AI literacy acquisition.\u003c/p\u003e\u003cp\u003eThe third category is AI-specific frameworks, which ground the research in a particular conceptualization of AI literacy. The framework developed by Ng and colleagues was most frequently utilized (e.g., O\u0026rsquo;Dea et al., 2024; Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Spirgi \u0026amp; Seufert, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Skalka et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Other studies drew upon models like the diamond model of AI literacy (Swartz et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), AI-TPACK (Younis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and E-GPPE-C intelligent learning model (J. Wang, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Skalka et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) drew upon several models: the UNESCO AI competency framework for Students, the ED-AI lit framework, and the K-12 AI competency framework. Biagini et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Dong et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), Kong et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Younis (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) each drew upon unique AI frameworks.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTheoretical frameworks employed\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFramework Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecific Theory/Model (Count)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychology and technology adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTheory of planned behavior (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\u003cp\u003eTechnology acceptance model (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003cp\u003eOther single technology usage models (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsychology and learning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-determination theory (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003cp\u003eSocial cognitive theory (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003cp\u003eSelf-regulated learning (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003cp\u003eBloom\u0026rsquo;s taxonomy (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI-specific frameworks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNg et al.\u0026rsquo;s AI literacy framework (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\u003cp\u003eOther single AI literacy frameworks (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRQ6: Related Constructs\u003c/h2\u003e\u003cp\u003eOutside of demographic data, which nearly every study collected, researchers frequently investigated AI literacy in conjunction with other variables. These can be organized into three broad categories. The first category is attitudes and perceptions, which captures students' feelings and beliefs about AI. This includes constructs such as attitudes toward AI, perceived usefulness, perceived enjoyment, interest in AI, and AI anxiety (K. Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Hobeika et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hornberger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Schauer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Syed et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; C. L. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe second category involves cognitive and behavioral skills, which are personal attributes and competencies that may influence or be influenced by AI literacy. Key constructs in this area include self-efficacy (both general and AI-specific), self-competence, critical thinking, self-regulated learning, self-determination, digital skills, discipline-specific skills, GAI-driven wellbeing; writing performance, educational attainment, pedagogical knowledge, adaptability, and motivation (Asio, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bewersdorff et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bui et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dadhich \u0026amp; Bhaumik, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hornberger et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Imjai et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Lilje et al., 2025; Qi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003el Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Z. Y. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe final group relates to technology use and intentions, which focuses on the practical application of AI. This includes variables such as use intention, continuous use, frequency of use, device ownership, and the direct outcomes of educational interventions designed to improve AI literacy (Al-Abdullatif et al., 2024; Bewersdorff et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; S. Y. Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003ek\u0026ccedil;e et al., 2025; Kong et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Korte et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lilje et al., 2025; Lin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Qi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sari et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Spirgi et al., 2025; Syed et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; C. L. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; J. Wang, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Younis, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eRQ7: Geographical Contexts of Student Populations\u003c/h2\u003e\u003cp\u003eThe research on AI literacy measurement among students is geographically concentrated, with a significant majority of student populations originating from Asia. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, China (including Hong Kong) is the most represented country (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13). The next most frequently represented countries are Germany (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), the United States (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), and Turkey (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3). Students from the United Kingdom, Italy, Saudi Arabia, Taiwan, Malaysia, South Korea, Palestine, India, Thailand, and Indonesia were participants in two unique studies. And students from Vietnam, Philippines, Iran, Poland, Czech Republic, Slovakia, Lebanon, Morocco, Bangladesh, Pakistan, Lithuania, France, and Ukraine count were participants in one unique study. While this indicates that AI literacy is a topic of global interest, the current body of empirical research on its measurement is predominantly situated in East Asia, with a smaller but significant presence in Europe, the Middle East, and North America. The participants in all studies were higher education students from a wide range of disciplines, ages, and genders.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis scoping review mapped the current landscape of AI literacy measurement in higher education, revealing a rapidly maturing yet uneven field. A central finding is the increasing convergence of definitions around AI literacy as a multi-dimensional construct, typically encompassing knowledge, application, evaluation, and ethics (Ng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; B. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This conceptual alignment has supported the growing adoption of standardized self-assessment instruments, particularly the Artificial Intelligence Literacy Scale (AILS), which has been heavily utilized in empirical studies (e.g., Hobeika et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ma \u0026amp; Chen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, as the field coalesces around these core concepts, important questions about the boundaries of each construct emerge. For instance, future research will need to clarify whether ethics should be treated as a unique, standalone dimension or as a cross-cutting theme that is integrated within all other aspects of AI literacy.\u003c/p\u003e\u003cp\u003eDespite this progress, several limitations in the literature constrain the robustness of current knowledge. First, the heavy reliance on self-assessment scales raises questions of validity. Self-perceptions may not accurately reflect students\u0026rsquo; demonstrated competencies, particularly as generative AI tools shift expectations of what \u0026ldquo;literacy\u0026rdquo; entails. While objective instruments such as Hornberger et al.\u0026rsquo;s (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) AI literacy test exist, their adoption remains limited. This reliance on perceptual measures risks reinforcing a narrow, potentially inflated understanding of AI literacy.\u003c/p\u003e\u003cp\u003eSecond, the methodological dominance of quantitative approaches reflects a field focused more on validation than exploration. The few examples of qualitative and mixed-methods research limits insights into students\u0026rsquo; lived experiences with AI, their sociocultural contexts, and the meaning-making processes underlying AI use. Without these perspectives, the field risks privileging surface-level generalizations over deeper, situated understandings of AI literacy development.\u003c/p\u003e\u003cp\u003eThird, conceptual ambiguities exist. Many measurement tools focus on broad technical knowledge of AI, while the rise of generative AI tools allows for engagement with AI without technical knowledge. O\u0026rsquo;Dea et al (2024) posits that a distinction should be made between AI literacy and generative AI literacy. This lack of clarity hinders the development of curricula and interventions tailored to today\u0026rsquo;s rapidly evolving AI landscape. Additionally, while many studies explored the relationship between AI literacy and other variables, there remains significant room to investigate how AI literacy interacts with a wider range of cognitive, affective, and behavioral factors.\u003c/p\u003e\u003cp\u003eFinally, the geographical concentration of studies, predominantly in East Asia, with smaller representation from Europe and North America, raises concerns about the cultural generalizability of findings. Cultural and educational contexts may significantly shape how AI literacy is conceptualized, assessed, and enacted. The current imbalance risks reinforcing region-specific perspectives as universal benchmarks.\u003c/p\u003e\u003cp\u003eTaken together, these trends suggest that while AI literacy measurement in higher education has advanced, it remains at a formative stage. Future progress will depend on expanding methodological diversity, clarifying constructs, and broadening global representation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis scoping review provides a comprehensive overview of how AI literacy is being measured among students in higher education. The findings highlight an emerging consensus around core constructs and a growing reliance on validated self-assessment scales, particularly the AILS. At the same time, the field is marked by critical gaps: an overreliance on self-report, limited use of objective and performance-based measures, a lack of qualitative insight, conceptual ambiguities, and significant geographical imbalances.\u003c/p\u003e\u003cp\u003eFuture researchers can address these gaps to advance both scholarship and practice. There is a need to prioritize the development and validation of objective assessments that capture demonstrated knowledge and skills, alongside qualitative approaches that illuminate students\u0026rsquo; lived experiences and cultural contexts. Future studies should also differentiate between technical AI literacy and the specific competencies required for engaging with generative AI. Expanding research beyond East Asia to include underrepresented regions, such as Central and South America, Africa, and Australia, is particularly important for building a more global and inclusive understanding of AI literacy in higher education.\u003c/p\u003e\u003cp\u003eBy addressing these methodological, conceptual, and geographical gaps, future research can move the field beyond fragmented measures toward more reliable and meaningful assessments of AI literacy. A stronger measurement foundation will not only improve research comparability but also inform the design of educational practices that prepare students for an AI-driven world\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure of potential conflicts of interest:\u003c/strong\u003e The author declares no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch involving human participants and/or animals:\u003c/strong\u003e This article is a scoping review of existing literature and does not contain any new studies with human participants or animals performed by the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e For this study, formal consent is not required as no new data was collected from human participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The author did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e Jeffrey R. Jones was the sole contributor to the work’s conception and design.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Abdullatif, A. M., \u0026amp; Alsubaie, M. A. (2024). ChatGPT in learning: Assessing students\u0026rsquo; use intentions through the lens of perceived value and the influence of AI literacy. \u003cem\u003eBehavioral Sciences\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(9), 845. https://doi.org/10.3390/bs14090845\u003c/li\u003e\n\u003cli\u003eArksey, H., \u0026amp; O\u0026rsquo;Malley, L. (2005). Scoping studies: towards a methodological framework. \u003cem\u003eInternational Journal of Social Research Methodology\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1), 19-32. https://doi.org/10.1080/1364557032000119616\u003c/li\u003e\n\u003cli\u003eAsio, J. M. R. (2024). 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How AI Literacy affects students\u0026rsquo; educational attainment in online learning: Testing a structural equation model in higher education context. \u003cem\u003eThe International Review of Research in Open and Distributed Learning, 25\u003c/em\u003e(3), 179\u0026ndash;198. https://doi.org/10.19173/irrodl.v25i3.7720\u003c/li\u003e\n\u003cli\u003eYounis, B. (2024). Effectiveness of a professional development program based on the instructional design framework for AI literacy in developing AI literacy skills among pre-service teachers. \u003cem\u003eJournal of Digital Learning in Teacher Education, 40\u003c/em\u003e(3), 142-158. https://doi.org/10.1080/21532974.2024.2365663\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"generative AI, AI literacy, higher education","lastPublishedDoi":"10.21203/rs.3.rs-8031490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8031490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) literacy has become an essential competence in higher education as students prepare for AI-driven academic and professional environments. This scoping review systematically maps the landscape of AI literacy measurement among higher education students. Following Arksey and O\u0026rsquo;Malley\u0026rsquo;s (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) five-stage framework, a systematic search of five databases yielded 190 records, with 39 studies meeting inclusion criteria. The analysis examined definitions of AI literacy, measurement methods, tools, constructs, theoretical frameworks, related variables, and geographical contexts.\u003c/p\u003e\u003cp\u003eFindings reveal a growing consensus around AI literacy as a multidimensional construct encompassing knowledge, application, evaluation, and ethics. Research is dominated by quantitative methods. The Artificial Intelligence Literacy Scale (AILS) (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) emerged as the most frequently used instrument, alongside newer validated tools and several study-specific measures. Only a small number of studies employed objective knowledge tests or mixed-methods approaches, highlighting a reliance on perceptual rather than demonstrated competencies. AI literacy was commonly studied in relation to attitudes, self-efficacy, and technology adoption intentions. Geographically, research is concentrated in East Asia, with smaller representation from Europe, North America, and the Middle East.\u003c/p\u003e\u003cp\u003eThis review underscores the field\u0026rsquo;s progress toward standardized measurement while identifying critical gaps, including overreliance on self-report, limited use of qualitative and performance-based assessments, conceptual ambiguities between AI and generative AI literacy, and geographical imbalance. Addressing these gaps will strengthen the validity and global applicability of AI literacy measurement, enabling more effective educational practices in higher education.\u003c/p\u003e","manuscriptTitle":"On the Measurement of AI Literacy Among Students in Higher Education: A Scoping Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 05:35:51","doi":"10.21203/rs.3.rs-8031490/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1b28003-93c5-40b9-9688-426951eeb1d2","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-12T17:30:09+00:00","versionOfRecord":{"articleIdentity":"rs-8031490","link":"https://doi.org/10.46787/ijaipil.v2026i1.6920","journal":{"identity":"international-journal-of-ai-in-pedagogy-innovation-and-learning-futures","isVorOnly":true,"title":"International Journal of AI in Pedagogy, Innovation, and Learning Futures"},"publishedOn":"2026-02-17 00:00:00","publishedOnDateReadable":"February 17th, 2026"},"versionCreatedAt":"2025-12-01 05:35:51","video":"","vorDoi":"10.46787/ijaipil.v2026i1.6920","vorDoiUrl":"https://doi.org/10.46787/ijaipil.v2026i1.6920","workflowStages":[]},"version":"v1","identity":"rs-8031490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8031490","identity":"rs-8031490","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}