The Use of Digital Libraries and Its Relationship to Digital Competence and AI Literacy Among Students of the College of Basic Education in Kuwait

Abstract

This study investigates how students at the College of Basic Education use digital libraries for information seeking and how this usage is influenced by their digital competence and AI literacy. A quantitative approach was employed, utilizing three validated questionnaires. These questionnaires were first administered in a pilot study (n = 176) to assess their psychometric properties and subsequently administered to the final sample (N = 334) of students, distributed across the first three academic years of the College of Basic Education, to test the research hypotheses. The three instruments included a questionnaire on students’ use of digital libraries, a digital competence scale, and an AI literacy scale. The results revealed statistically significant differences in digital library use based on varying levels of digital competence and AI literacy, as well as a strong positive correlation between these variables and the use of digital libraries. Furthermore, multiple regression analyses showed that both digital competence and AI literacy significantly predicted the extent to which students used digital libraries for information seeking. The study recommends integrating digital skills, AI literacy, and metacognitive strategies into teacher preparation programs at the College of Basic Education in Kuwait.

Keywords

digital libraries information seeking digital competence AI literacy College of Basic Education

Introduction

Due to the rapid development of technology, digital competence and AI literacy are now essential pillars in preparing future teachers, as possessing only technical skills is no longer sufficient. Students must also develop critical and ethical awareness for dealing with intelligent systems (Holmes et al., 2019; Zawacki-Richter et al., 2019). As essential academic infrastructures, digital libraries provide access to extensive information resources and support the development of research and learning skills (Tenopir et al., 2012). For students in Kuwait’s College of Basic Education (CBE), the ability to effectively find, evaluate, and use digital information is both an educational requirement and a professional necessity (Koehler et al., 2014). Digital competence is a multidimensional construct that encompasses technical, cognitive, and ethical skills for using digital technologies, enabling students to navigate digital environments, evaluate the reliability of information, and employ it effectively (Vuorikari et al., 2022). However, with the growing integration of artificial intelligence (AI) in education, digital competence alone is no longer sufficient. Students must also develop AI literacy, which includes not only technical knowledge but also ethical understanding and the ability to apply AI meaningfully in educational and professional contexts (Holmes et al., 2019; Stolpe & Hallström, 2024).

Despite the rising importance of these skills, most existing research has been conducted in Western or highly developed contexts, leaving limited empirical evidence from Arab or Middle Eastern settings. This gap is particularly relevant in Kuwait, where socio-pedagogical challenges and disparities in digital infrastructure shape students’ experiences. Therefore, this study aims to examine the relationship between digital competence and AI literacy in enhancing the use of digital libraries among CBE students in Kuwait. The findings are expected to provide valuable insights for curriculum designers, policymakers, and educational leaders for integrating AI literacy and digital research skills into teacher preparation programs, thereby supporting the digital transformation of higher education (Hossain et al., 2025).

Research Problem Statement

In today’s rapidly evolving digital teaching environment, digital libraries act as an important infrastructure for educational research, resource searching, and personal learning. These platforms are equipped with advanced search tools, sophisticated navigation systems, various multimedia collections, and increase access for students and scholars (Lo, 2025). Effective use of digital libraries is crucial for pre-service teachers in the CBE, requiring them not only to develop skills for retrieving information but also to engage with features like advanced search engines, metadata filtering, citation management tools, and AI-driven recommendation systems. Challenges in using these tools often stem from limited digital competence and insufficient AI literacy (Ru & Tang, 2025; Tang & Zhang, 2025).

Modern digital abilities encompass more than basic ICT skills; they include critical evaluation of sources, digital identity management, understanding of information ethics, and a technological perspective that includes meaningful participation in the teaching environment (Ilomäki et al., 2016; Vuorikari et al., 2022). Many students struggle not only to locate high-quality educational resources but also to critically interpret AI-curated search results. These challenges are amplified by a lack of awareness of how digital algorithms influence the use and misuse of information. As a result, students may succumb to automation bias, be vulnerable to confirmation bias, or limit themselves to narrow, personalized information feeds (Mehrabi et al., 2021).

AI literacy—defined as the ability to understand, interact with, and evaluate AI systems—has become increasingly important in the academic digital environment. In digital libraries, it improves awareness of how intelligent algorithms shape searching, ranking, filtering, and material recommendations (Ru & Tang, 2025). Without this understanding, students may have inappropriate reliance on algorithmic output, confuse convenience with reliability, and ignore built-in bias or opaque decision-making (Hämäläinen et al., 2021). Low levels of AI literacy can lead to algorithmic dependence, strengthen existing digital biases, and limit exposure to diverse information sources, thereby reducing critical engagement and research autonomy (Lo, 2025; Ndungu, 2024).

Current literature often examines digital competence (or ICT use) and AI literacy in education as separate domains. For example, Ilomäki and Rantanen (2007) explored how students develop ICT expertise through intensive school-based and home-based use, while more recent studies on AI-related digital capacity focus primarily on theoretical perspectives. However, few studies investigate their combined effect on practical educational functions such as information retrieval, evaluation, and synthesis (Ilomäki & Rantanen, 2007; Owoc et al., 2021).

This gap is particularly evident in teacher education programs, where pre-service teachers must not only develop digital capacity and AI literacy for their own academic success but also be able to model and teach these skills in their future classrooms. Without empirical insights into their current abilities and challenges, teacher preparation programs risk leaving graduates with significant skill gaps (Vuorikari et al., 2022). Many educational colleges operate in contexts where digital transformation is constrained by slow, uneven, or inadequate infrastructure. Locally grounded research on students’ engagement with AI-enabled systems can help inform effective course design, policymaking, and faculty development regarding digital library use.

Therefore, there is an urgent need for empirical examination of how digital competence and AI literacy interact to shape students’ educational engagement with digital library programs. These students are not merely consumers of educational technology; they are future educators who will play a critical role in shaping digital learning for the next generation. Understanding their behaviors, perceptions, and awareness of algorithmic processes is essential for preparing them to meet the demands of the 21st century.

Institutions can ensure fair and effective participation in the AI-era educational environment by addressing these skill gaps and promoting ethical, critical engagement with AI systems. Ultimately, empowering future teachers with these competencies not only improves their academic engagement but also enhances their ability to lead students in navigating rapidly complex digital and algorithmic landscapes (Hämäläinen et al., 2021; Ru & Tang, 2025).

Research Objectives

The present study has three key objectives: to identify the extent to which CBE students use digital libraries for information retrieval; to assess their levels of digital competence and AI literacy; and to explore the relationship between digital library use, digital competence, and AI literacy among these students. Finally, based on the findings, the study intends to propose guidelines for student engagement with digital libraries that are tailored to their levels of digital competence and AI literacy.

Research Hypotheses

(1) Hypothesis 1: There are statistically significant differences in students’ use of digital libraries based on their level of digital capacity (high, medium, low).

(2) Hypothesis 2: There are statistically significant differences in students’ use of digital libraries based on their level of AI literacy (high, medium, low).

(3) Hypothesis 3: Digital capacity among CBE students has a statistically significant positive relationship with their use of digital libraries.

(4) Hypothesis 4: AI literacy has a statistically significant positive relationship with the use of digital libraries among CBE students.

(5) Hypothesis 5: Students’ level of digital library use can be predicted based on their digital capacity and AI literacy.

Literature Review

Theoretical Framework and Literature Review

Digital Competence and Its Relevance

The EU’s DigComp 2.2 framework defines digital competence as a multidimensional construct integrating technical, cognitive, moral, and social skills necessary for confident, critical, and responsible engagement with digital technologies in learning, work, and society. The framework identifies five competence areas—information and data literacy, communication and collaboration, digital content creation, safety, and problem solving—each spanning proficiency levels from foundational to advanced (Vuorikari et al., 2022). For the purpose of this study, digital competence is specifically framed as students’ ability to effectively use institutional digital libraries, encompassing advanced search strategies, responsible digital information management, and the resolution of technical challenges.

AI Literacy and Educational Implications

AI literacy extends digital competence by integrating technical skills, conceptual understanding, and ethical awareness, enabling individuals to critically engage with AI systems (Gu & Ericson, 2025). It encompasses three interrelated domains: functional interaction with AI-powered platforms; critical awareness of algorithmic assumptions and biases; and ethical reflection concerning privacy, fairness, and academic integrity. In teacher education, AI literacy is a core element of modern digital competence (Walter, 2024). As AI becomes embedded in academic infrastructures, students must evaluate how intelligent systems shape information provision and personalization, thereby avoiding over-reliance on algorithmic outputs (Faruqe et al., 2022; Holmes et al., 2019).

Intersections with Digital Library Use

Recent research highlights that digital competence and AI literacy are complementary rather than separate. While digital competence enables students to search, evaluate, and produce digital content, AI literacy empowers them to understand algorithmic filtering, detect biases, and critically assess ranked outputs (Walter, 2024). Together, these competencies enhance effective digital library use, fostering autonomy, ethical judgment, and research quality. Students lacking either competence risk over-reliance on system-generated outputs or superficial engagement with sources (Zhang et al., 2025). Given AI’s capability to revolutionize how information is produced, communicated, and understood, a comprehensive grasp of its implications for information seeking is crucial (Adegboye, 2024).

The Role of Teacher Education

Teacher education programs play a pivotal role in cultivating digital competence and AI literacy, equipping pre-service teachers to model ethical and informed practices in AI-integrated learning environments (Akgun & Greenhow, 2022; Chiu et al., 2023). Embedding scaffolded AI literacy training—covering prompt engineering, ethical evaluation, and algorithmic transparency—has been shown to strengthen teachers’ digital preparedness and critical engagement with intelligent systems (Kohnke et al., 2025; Nyaaba, 2024; Tadimalla & Maher, 2024). Frameworks such as DigCompEdu (Redecker & Punie, 2017) and Generative AI Literacy (Zhang & Magerko, 2025) provide structured guidance for integrating these competencies into curricula.

Cognitive and Metacognitive Strategies

In addition to digital and AI literacies, effective digital library use requires cognitive and metacognitive strategies, such as clearly defining research goals, critically evaluating sources, and monitoring comprehension. These reflective skills are particularly crucial in AI-mediated environments, where algorithmic personalization can obscure alternative perspectives. Integrating metacognitive training into teacher education can therefore reinforce digital competence and AI literacy, supporting critical and ethical information practices (Singh et al., 2025).

Research Methodology

This study adopts a quantitative research design to examine the relationship between CBE students’ digital library use, their digital competence, and their AI literacy. This approach aligns closely with the study’s objectives and research questions, enabling systematic empirical analysis and the generation of generalizable insights.

Research Design

Research Design and Variables

A descriptive correlational design was employed to examine the relationship between the frequency and patterns of digital library use and students’ levels of digital competence and AI literacy. This design was selected to capture natural variations in behavior and ability within the student population without manipulating any variables (Creswell & Creswell, 2018).

Table 1 summarizes the key study variables and their measurement methods. The dependent variable (Digital Library Use) is measured using practical indicators such as usage frequency and effectiveness. The independent variables (Digital Competence and AI Literacy) are measured via validated scales assessing technical, informational, and AI-related skills. This framework, therefore, provides clarity in variable definition and establishes a solid basis for analyzing their interrelationships.

Table 1.

Study Variables and Measurement Approach

Variable	Type	Measurement description
Digital library use	Dependent	Measured by self-reported frequency and effectiveness of use
Digital competence	Independent	Assessed via scale measuring technical and information literacy skills
AI literacy	Independent	Assessed by literacy of AI tools, concepts, and applications

Population and Sample

The study population consisted of students enrolled in the College of Basic Education (CBE) under Kuwait’s Public Authority for Applied Education and Training (PAAET). A stratified random sampling method was adopted to ensure proportional representation across both academic years and specializations. The final sample size was 334 participants, which satisfies the minimum statistical requirements for reliable quantitative analysis (Field, 2018).

Table 2 shows the distribution of CBE students by academic year and specialization. The data indicate a clear decline in the number of participants from the first year (155) to the third year (49), suggesting reduced enrollment or attrition over time. Among the specializations, Library and Information Science recorded the highest number of students (65), followed by Computer Science (58), while Art Education had the lowest representation (11). These differences highlight a stronger student tendency toward technology- and information-oriented disciplines compared to arts-related fields.

Table 2.

Distribution of Students’ Sample by Academic Year and Specialization

Academic year	Library and information	Educational technology	Computer science	Mathematics	Sciences	Special education	Art education	Total
First year	30	25	28	25	27	12	8	155
Second year	25	18	20	17	18	5	2	105
Third year	10	7	10	8	7	6	1	49
Total	65	50	58	50	52	23	11	334

Data Collection Instrument

The study employed three structured instruments, which were developed through a scientifically grounded process based on an extensive review of literature, established measurement tools, and prior empirical research to ensure their validity and contextual appropriateness for the CBE in Kuwait. The first instrument, the Digital Library Use Scale, was adapted from theoretical models and prior studies (Chopra et al., 2024; Tyagi et al., 2022) that examined digital library use across dimensions such as frequency of use, ease of use, perceived quality, perceived utility, and satisfaction. The final scale comprised three main sections (digital library use, perceived value and experience, and intention to continue use) and was organized into five dimensions, each containing seven items, for a total of 35 items. The second instrument, the Digital Competence Scale, was designed specifically for CBE students based on the European Digital Competence Framework (DigComp 2.2) (Vuorikari et al., 2022) and informed by recent studies (Mejías-Acosta et al., 2024). This scale measured five core areas of digital competence, comprising seven items per dimension, for a total of 35 items.

The third instrument, the AI Literacy Scale, aimed to measure students’ awareness, attitudes, and skills in using AI technologies. Its development was guided by contemporary literature, particularly the work of Wang et al. (2025), which emphasized the cognitive, practical, and ethical dimensions of AI in education. This scale also consisted of five dimensions, each with seven items, totaling 35 items. All three instruments utilized a five-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), a format that allowed for consistent and accurate measurement of students’ perceptions and behaviors. A pilot study was conducted with 175 CBE students in Kuwait to assess the construct validity, internal consistency, and overall reliability of the instruments.

Reliability and Internal Consistency of the Digital Library Use Scale

The reliability of the scale measuring students’ digital library use was evaluated using Cronbach’s Alpha and the split-half (Spearman–Brown) method for both each subscale and the overall score. Cronbach’s Alpha coefficients were 0.794 (Information Seeking), 0.775 (Evaluation of Digital Information), 0.768 (Information Quality), 0.773 (Knowledge Production), and 0.741 (Digital Participation). The overall reliability for the 36-item scale was α = 0.752. Split-half reliability coefficients (Spearman–Brown) ranged from 0.827 to 0.967, further demonstrating high internal consistency. These findings indicate that the scale possesses acceptable to good reliability across its dimensions, supporting its suitability for use in the context of this study.

Calculating the Reliability and Internal Consistency of the Digital Competence Scale

The reliability of the Digital Competence Scale was assessed using Cronbach’s Alpha and split-half reliability for each dimension and the total score, revealing a good level of reliability and internal consistency. The Cronbach’s Alpha coefficients were 0.782 (Information Literacy), 0.746 (Digital Communication and Collaboration), 0.793 (Digital Citizenship), 0.789 (Digital Safety), and 0.792 (Computational Thinking). The overall reliability for the 36-item scale was α = 0.756. Split-half reliability results further supported these findings: Spearman–Brown coefficients ranged from 0.933 to 0.972, and Guttman split-half coefficients ranged from 0.719 to 0.857. These results confirm that the scale has acceptable to good reliability, supporting its suitability for use in both academic and field research.

Calculating the Reliability and Internal Consistency of AI Literacy Scale

The reliability and internal consistency of the AI Literacy Scale were assessed using Cronbach’s Alpha and the split-half method, revealing acceptable levels of reliability across all dimensions. Cronbach’s Alpha coefficients were 0.774 (Conceptual Knowledge), 0.762 (Applied Knowledge), 0.756 (Awareness of AI Impacts), 0.772 (Ethical Knowledge), and 0.757 (Skill Knowledge). The overall reliability for the 36-item scale was α = 0.749. The split-half reliability coefficients further confirmed these results, with Spearman–Brown coefficients across all dimensions ranging from 0.899 to 0.950, indicating a high level of internal consistency. The scale, therefore, demonstrates good reliability and internal consistency, supporting its suitability for research purposes. Overall, all three instruments used in the study demonstrated high levels of validity, reliability, and internal stability, as further detailed in Appendix [1].

Data Collection Procedures

Data were collected over a three-week period using a mixed-mode approach to maximize accessibility. The questionnaire was distributed in both electronic format, through secure online survey platforms, and paper-based format, administered on campus during scheduled class times. This dual-mode strategy was adopted to ensure that students with varying levels of digital access and preferences were equally able to participate, thereby enhancing the representativeness of the sample. Participation was strictly voluntary, and informed consent was obtained from all respondents prior to their involvement in the study. Confidentiality and anonymity were guaranteed by assigning numerical codes to responses rather than collecting personal identifiers. Ethical approval for the study was secured from the CBE, and all procedures were aligned with institutional ethical guidelines for research involving human participants. The instruments were administered to both male and female students, with sampling stratified according to academic year (first, second, and third year) and field of specialization across the departments of the CBE. This stratified sampling ensured proportional representation of the student population and allowed for more accurate comparisons across demographic and academic variables. The administration of the questionnaire was monitored by trained research assistants to provide clarification when needed and to ensure the reliability and consistency of the data collection process.

Data Analysis

All collected data were subjected to analysis using the statistical software package SPSS (version 27). Initially, descriptive statistics were calculated to summarize the variables, and this was then followed by Pearson correlation analyses to precisely examine the linear relationships among students’ digital library use, digital competence, and AI literacy. Statistical significance was specifically determined at a critical level of p < 0.05. The analyses were explicitly guided by the research questions, aiming to provide both valid and explanatory insights. This comprehensive approach not only measured the strength of associations but also sought to clarify how students demonstrated ability to effectively use digital libraries is influenced by their digital competence and AI literacy, as well as how these critical factors may potentially predict future performance in sophisticated AI-enhanced academic environments.

Results

This subsequent section presents the empirical statistical findings related to the study’s stated five research hypotheses. Specifically, the data analysis procedures involved examining the potential relationships between the core constructs of digital competence, AI literacy, and effective digital library use. Furthermore, the Analysis of Variance (ANOVA) procedure was strategically employed to test the mean differences between measured variables and across specified demographic groups. A Pearson product-moment correlation was specifically utilized to assess the magnitude and direction of linear associations among the study’s three key variables. In addition, a hierarchical multiple regression analysis was conducted to empirically determine the combined predictive power of both digital competence and AI literacy on students’ demonstrated use of digital libraries.

Hypothesis 1

A one-way Analysis of Variance (ANOVA) revealed a statistically significant effect of students’ digital competence level (high, medium, or low) on digital library use, F(2,331) = 145.16, p < 0.001. Post hoc Tukey analysis indicated that students with high digital competence reported significantly greater and more efficient use of digital libraries compared to both the moderate and low competence groups (Table 3).

Table 3.

One-Way ANOVA for the Use of Digital Library by Digital Capacity Level

Dimension	Source	df	Sum of squares	Mean square	F (p-value)
Frequency of use	Between groups	2	2387.46	1193.73	351.53 (p = 0.000)
	Within groups	331	1124.01	3.4
	Total	333	3511.47	3.4
Ease of use	Between groups	2	2269.76	1134.88	1206.64 (p = 0.000)
	Within groups	331	311.32	0.94
	Total	333	2581.08	0.94
Perceived information quality	Between groups	2	2718.07	1359.03	1037.57 (p = 0.000)
	Within groups	331	433.55	1.31
	Total	333	3151.62	1.31
Perceived usefulness	Between groups	2	3795.27	1897.64	1184.27 (p = 0.000)
	Within groups	331	530.38	1.6
	Total	333	4325.65	1.6
Satisfaction and continued intention	Between groups	2	1890.0	945.0	925.90 (p = 0.000)
	Within groups	331	337.83	1.02
	Total	333	2227.83	1.02
Total score	Between groups	2	29106.64	14553.32	145.16 (p = 0.000)
	Within groups	331	33184.18	100.25
	Total	333	62290.83	100.25

The analysis revealed significant differences in the utilization and perception of digital library services based on students’ level of digital competence. Specifically, students with higher digital competence reported not only increased frequency of use but also greater perceived utility, ease of navigation, and overall satisfaction. This finding underscores that digital competence is a central factor shaping both the quality and depth of students’ engagement with digital library resources (Table 4).

Table 4.

Scheffé Post Hoc Test—Mean Differences by Competence Level

Dimension	Comparison	Mean difference	95% CI
Frequency of use	High vs low	7.075	[6.23–7.92]
	High vs mid	4.663	[4.11–5.21]
	Mid vs low	2.412	[1.52–3.31]
Ease of use	High vs low	6.198	[5.89–6.51]
	High vs mid	3.587	[3.23–3.95]
	Mid vs low	2.611	[2.29–2.94]
Perceived information quality	High vs low	6.978	[6.60–7.36]
	High vs mid	3.539	[3.14–3.94]
	Mid vs low	3.439	[3.07–3.80]
Perceived usefulness	High vs low	9.047	[8.59–9.50]
	High vs mid	4.796	[4.38–5.22]
	Mid vs low	4.251	[3.85–4.66]
Satisfaction and continued intention	High vs low	5.989	[5.63–6.34]
	High vs mid	3.849	[3.53–4.16]
	Mid vs low	2.139	[1.79–2.49]
Total score	High vs low	33.488	[31.18–35.80]
	High vs mid	20.991	[18.97–23.01]
	Mid vs low	12.498	[10.48–14.52]

The post hoc analysis confirmed that students with high digital competence consistently demonstrated stronger engagement and more positive perceptions of digital library services compared to their peers with moderate or low competence. Specifically, they reported greater perceived utility, ease of use, and overall information quality. These results underscore the conclusion that higher digital competence is directly associated with richer and more meaningful interactions with digital library resources (Table 5).

Table 5.

One-Way ANOVA Results by AI Literacy Level

Dimension	Source	df	Sum of squares	Mean square	F (p-value)
Literacy and understanding	Between groups	2	1653.62	826.81	461.48 (p = .000)
	Within groups	331	593.04	1.79
	Total	333	2246.66	1.79
Use and apply	Between groups	2	2121.07	1060.53	1012.11 (p = .000)
	Within groups	331	346.84	1.05
	Total	333	2467.9	1.05
Evaluate and reflect	Between groups	2	2400.38	1200.19	1428.68 (p = .000)
	Within groups	331	278.06	0.84
	Total	333	2678.44	0.84
Ethics and awareness	Between groups	2	2651.94	1325.97	1174.59 (p = .000)
	Within groups	331	373.66	1.13
	Total	333	3025.6	1.13
Autonomy and self-efficacy	Between groups	2	2747.39	1373.7	899.16 (p = .000)
	Within groups	331	505.69	1.53
	Total	333	3253.08	1.53
Total score	Between groups	2	44795.15	22397.57	1116.79 (p = .000)
	Within groups	331	6638.29	20.06
	Total	333	51433.44	20.06

As illustrated in Figure 1, students with higher levels of digital competence demonstrate significantly greater utilization of digital library resources compared to those with moderate or low competence. This consistent pattern confirms the core hypothesis that digital competence significantly influences the quality and extent of students’ engagement with digital library services.

Figure 1.

Mean Digital Library Use by Digital Competence Level

Hypothesis 2

A one-way Analysis of Variance (ANOVA) revealed a statistically significant difference in students’ digital library use across the three AI literacy levels (high, moderate, or low). The analysis indicated significant differences in digital library utilization across these three AI literacy levels (p < 0.001), with this effect being consistently observed across all six AI literacy dimensions. These findings confirm the core hypothesis that AI literacy level significantly impacts students’ engagement with and effective use of digital library resources.

The analysis conclusively revealed significant differences in digital library utilization based on students’ levels of AI literacy. Specifically, students with higher AI literacy consistently demonstrated both greater frequency and depth of engagement with digital library resources across all measured dimensions (e.g., understanding, application, reflection, morality, and self-efficacy). These findings affirm that AI literacy is a critical factor determining the effectiveness of students’ digital library use, thereby underscoring its vital role in shaping contemporary patterns of digital engagement (Table 6).

Table 6.

Scheffé Post Hoc Test—Mean Differences by AI Literacy Level

Dimension	Comparison	Mean difference	95% CI
Literacy and understanding	High vs low	5.304	[4.84–5.76]
	High vs mid	3.439	[3.02–3.86]
	Mid vs low	1.865	[1.36–2.37]
Use and apply	High vs low	6.877	[6.49–7.27]
	High vs mid	4.339	[4.01–4.66]
	Mid vs low	2.539	[2.19–2.89]
Evaluate and reflect	High vs low	6.261	[5.97–6.55]
	High vs mid	3.881	[3.57–4.19]
	Mid vs low	2.38	[2.07–2.69]
Ethics and awareness	High vs low	7.0	[6.64–7.36]
	High vs mid	4.057	[3.68–4.43]
	Mid vs low	2.943	[2.61–3.28]
Autonomy and self-efficacy	High vs low	6.767	[6.37–7.16]
	High vs mid	3.908	[3.46–4.36]
	Mid vs low	2.859	[2.44–3.27]
Total score	High vs low	28.873	[27.36–30.39]
	High vs mid	16.14	[14.48–17.80]
	Mid vs low	12.733	[11.31–14.16]

The post hoc analysis confirmed that students with higher AI literacy consistently demonstrated stronger and more effective engagement with digital library tools across all measured dimensions. Crucially, even students demonstrating moderate AI literacy achieved significantly better outcomes than those with low competence, thus highlighting a distinct, linear relationship between increasing AI literacy and improved digital library utilization. Ultimately, these findings fully confirm the study’s core hypothesis and emphatically underscore the importance of integrating comprehensive AI literacy development into current educational programs.

As demonstrated in Figure 2, students exhibiting higher levels of AI literacy utilize the digital library more actively than their peers with moderate or low literacy. This consistent pattern strongly reinforces the hypothesis that AI literacy is a significant factor in shaping the quality and frequency of students’ engagement with digital library resources.

Figure 2.

Mean Digital Library Use by AI Literacy Level

Hypothesis 3

Pearson correlation analysis revealed a statistically significant, consistently strong positive relationship between students’ overall digital competence and their utilization of digital libraries within the CBE. All specific digital competencies (information literacy, digital communication, content creation, digital safety, and problem solving) were significantly and positively correlated with all aspects of digital library engagement, including functional use, perceived utility, and system navigation. As all correlations were statistically significant at the p < 0.01 level (two-tailed), the findings strongly support the hypothesis that higher digital competence is directly associated with more efficient and effective use of digital libraries (Table 7).

Table 7.

Correlation Matrix: Digital Library Use and Digital Competence

Digital literacy use	Digital competence
Digital literacy use	Information and data literacy	Digital communication and collaboration	Digital content creation	Digital safety	Technical problem solving	Total score
Frequency of use	0.68	0.66	0.76	0.74	0.71	0.76
Ease of use	0.56	0.57	0.60	0.58	0.54	0.61
Information quality	0.54	0.57	0.60	0.57	0.54	0.60
Perceived usefulness	0.61	0.58	0.67	0.61	0.60	0.66
Satisfaction and continued intention	0.64	0.60	0.66	0.64	0.60	0.67
Total score	0.68	0.67	0.74	0.7	0.67	0.74

The analysis revealed a statistically significant positive relationship between students’ digital competence and their utilization of digital library services. Specifically, students exhibiting higher digital skills reported greater frequency of use, enhanced satisfaction, and a stronger intention to continue utilizing these resources. Ultimately, these findings underscore the crucial role of digital competence in maximizing students’ academic engagement with digital library platforms.

As depicted in Figure 3, there is a strong positive correlation between students’ digital competence and their utilization of digital library resources. This analysis shows that students with stronger digital skills consistently exhibit higher levels of engagement, emphatically underscoring the critical role of digital competence in maximizing the educational benefits of digital libraries.

Figure 3.

Correlation Between Total Digital Competence and Total Digital Library Use

Hypothesis 4

Correlation analysis revealed a statistically significant, positive relationship between students’ AI literacy and their utilization of digital libraries within the CBE. Specifically, the total AI literacy score showed strong positive correlations with key digital library components, including frequency of use (r = 0.75), use of advanced tools (r = 0.65), and the overall digital library use score (r = 0.73). Consistently positive associations were found across all dimensions of AI literacy. Notably, conceptual and ethical dimensions (e.g., emotional awareness and ethical reasoning) exhibited substantial correlations with digital library use, ranging from r = 0.60 to r = 0.75. Despite moderate correlations with factors like ease of use and information quality, the overall correlational pattern strongly supports the core hypothesis that higher AI literacy significantly contributes to the effective utilization of digital academic resources (Table 8).

Table 8.

Correlation Between AI Literacy and Digital Library Use

Digital library use	AI literacy
Digital library use	Knowledge and understanding	Use and apply	Evaluate and reflect	Ethics and awareness	Autonomy and self-efficacy	Total score
Frequency of use	0.59	0.53	0.7	0.75	0.69	0.75
Ease of use	0.45	0.39	0.55	0.55	0.57	0.57
Information quality	0.41	0.35	0.55	0.58	0.59	0.58
Perceived usefulness	0.52	0.49	0.6	0.62	0.63	0.65
Satisfaction and continued intention	0.58	0.53	0.65	0.68	0.67	0.72
Total score	0.57	0.51	0.68	0.71	0.71	0.73

The analysis revealed a strong and consistent positive relationship between students’ AI literacy and their utilization of digital library resources. Students exhibiting higher AI literacy engaged more actively with these platforms, demonstrating not only increased frequency and access but also deeper levels of ethical and reflective utilization. This evidence underscores that the conceptual and ethical dimensions of AI literacy—specifically emotional awareness, ethical reasoning, and self-efficacy—are crucial in maximizing students’ effective engagement with digital academic resources.

As clearly evidenced in Figure 4, students exhibiting higher AI literacy demonstrate significantly greater and more effective utilization of digital library resources. This result underscores the essential role of AI literacy in fostering meaningful digital engagement, thus reinforcing the critical need to integrate AI-related content into teacher education curricula.

Figure 4.

Linear Relationship Between AI Total Score (AI-TS) and Digital Library Total Score (DL-TS)

Hypothesis 5

To test the predictive hypothesis, a stepwise multiple regression analysis was conducted, using the Digital Library Use Total Score (DL-TS) as the dependent variable and the Digital Competence Total Score (DC-TS) and AI Literacy Total Score (AI-TS) as the predictors. Model 1, which included only DC-TS, explained 55.1% of the variance in DL-TS (R2 = 0.551; SE = 9.175). The addition of AI-TS in Model 2 led to an increase in explained variance to 58.5% (R2 = 0.585) and a decrease in the standard error (SE = 8.833), signifying an improved model fit. Both models were statistically significant (ANOVA p < 0.001). In the final model, both DC-TS (β = 0.437) and AI-TS (β = 0.357) were significant predictors (p < 0.001), with digital competence exhibiting a slightly stronger predictive effect. These findings affirm the strong, combined influence of digital competence and AI literacy on students’ engagement with digital academic resources (Tables 9-11).

Table 9.

Model Summary for Predicting Digital Library Use Based on Digital Competence and AI Literacy

Model	Predictors	R	R²	Adjusted R²	Std. error
1	Digital library use	0.743	0.551	0.550	9.175
2	AI literacy	0.765	0.585	0.583	8.833

Table 10

ANOVA Summary for Regression Models Predicting Digital Library Use

Model	Sum of squares	df	Mean square	F (sig.)
1	34342.349	1	34342.349	407.953 (.000)
2	36466.920	2	18233.460	233.709 (.000)

Table 11.

Regression Coefficients for Predicting Digital Library Use (Model 2)

Predictor	B	Std. error	Beta	t	Sig.
Constant	20.456	4.398	—	4.651	.000
Digital library use	0.430	0.067	0.437	6.384	.000
AI literacy	0.393	0.075	0.357	5.218	.000

As clearly demonstrated in Figure 5, students possessing both higher digital competence and stronger AI literacy achieve broader and more effective utilization of digital library resources. This finding affirms the combined predictive value of these two skill sets and emphatically underscores the necessity of fostering both to enhance students’ educational engagement with digital resources.

Figure 5.

3D Scatter Plot of Digital Competence, AI Literacy, and Digital Library Use

Discussion

Collectively, the empirical findings of this investigation unequivocally confirm that students possessing higher levels of digital competence and stronger AI literacy garner more significant advantages from digital library services. This significant result powerfully supports the principal hypotheses, clearly showing that technical skills in isolation are insufficient to ensure the ultimate effective engagement with digital resources. Consequently, critical awareness, ethical responsibility, and reflective judgment must necessarily accompany all digital and AI-related skills to maximize their inherent educational value. Furthermore, these results are highly consistent with the current body of research emphasizing that digital competence is a construct that involves considerably more than simple functional abilities. For instance, Vuorikari et al. (2022) specifically highlighted the fundamental role of digital competence in fostering meaningful and critical engagement, whereas Adegboye (2024) empirically demonstrated that AI literacy helps students to effectively evaluate the credibility of information and accurately interpret algorithmic outputs. However, in comparison, some studies such as Hossain et al. (2025) reported only limited differences between students with varying AI literacy levels, thereby suggesting that various contextual factors, including institutional support and infrastructure, may also play a moderating role.

Commencing from a theoretical perspective, the empirical results align precisely with the DigCompEdu framework (Redecker & Punie, 2017), which conceptualizes digital competence as a multidimensional construct that integrates key elements such as technical proficiency, critical thinking, and metacognitive skills. In a similar vein, the AI Literacy Framework (Long & Magerko, 2020) stresses the paramount importance of combining technical fluency with ethical awareness and reflective practice. In a critical contribution, the present study explicitly reinforces these established frameworks by demonstrating empirically that effective digital library use is fundamentally dependent on students’ ability to critically evaluate information, to effectively regulate their learning, and to successfully apply digital tools appropriately in varied contexts. A deeper interpretation of the findings conclusively suggests that students with stronger digital and AI competencies are demonstrably more capable of selecting appropriate tools, of efficiently verifying the credibility of information, and of expertly applying self-regulated learning strategies. Crucially, this directly reflects the profound importance of metacognitive capacity as a third overarching essential factor alongside digital competence and AI literacy. Furthermore, in line with Flavell’s pioneering work (1979) on metacognition, the results unequivocally indicate that students’ inherent ability to reflect on and regulate their own learning processes is decisively integral in enabling meaningful engagement with digital resources.

From a practical standpoint, these empirical results strongly call for teacher education programs to adopt robust interdisciplinary approaches that effectively integrate digital competence, AI literacy, and metacognitive training. Consequently, educational institutions should strategically move beyond teaching merely basic tool usage to fully embedding ethics, critical thinking, and reflective judgment within all relevant curricula. Specifically, the establishment of pilot projects, structured professional development programs, and strategic partnerships with digital libraries could provide effective and scalable pathways for implementing these critical recommendations. Nevertheless, while the present study provides highly important insights, certain methodological limitations must be formally acknowledged. The primary reliance on self-reported data introduces the inherent possibility of response bias, and the focus on a single educational context may substantially restrict the generalizability of the findings. To overcome these issues, future research could address these limitations through conducting longitudinal studies, multi-institutional comparisons, and the use of more reliable objective usage data derived from digital library systems. Ultimately, despite these identified limitations, the study contributes significantly to ongoing scholarly discussions about the integration of digital competence and AI literacy into teacher education and provides a strong foundation for subsequent inquiry.

Conclusion

Overall, the empirical findings of this study powerfully underscore the central role of both digital competence and AI literacy in decisively shaping students’ effective engagement with digital library systems at the CBE. Consequently, the observed statistically significant differences and consistently strong positive correlations unequivocally indicate that students possessing high levels of both digital and AI-related skills are optimally equipped to carry out meaningful information-seeking activities. Furthermore, the multiple regression analysis conclusively confirmed that digital library use can be substantially predicted by the strategic combining of technical proficiency with conceptual and ethical understanding, thereby explicitly highlighting the inherent interactive and complementary nature of these two core competencies. The accumulated results suggest that successful engagement with academic digital resources is no longer solely dependent on foundational operational ICT skills but is now contingent upon an informed understanding of how intelligent systems fundamentally function. In effect, successful use of digital libraries now mandates a heightened awareness of concepts such as algorithmic influence, robust information security, and adaptive research strategies. Therefore, the study formally calls for educational institutions to move beyond basic ICT training by comprehensively embedding digital competence and AI literacy into all relevant teacher education curricula. Doing so will not only prepare current students to effectively navigate complex digital environments responsibly and strategically but will also decisively equip future teachers to model and promote these indispensable skills. This ultimately reflects a broader educational shift toward cultivating reflective, autonomous, adaptive, and digitally fluent professionals—who are fully capable of critical engagement and collaborative participation in the rapidly evolving landscape of modern education and professional life.

Recommendations

In direct response to the study’s empirical findings, the following actionable recommendations are formally proposed.

(1) Comprehensive Integration of Digital Competence and AI Literacy into Teacher Education Curricula—Teacher preparation programs should strategically embed structured training that combines foundational technical ICT skills with advanced AI literacy, specifically focusing on ethical reasoning, algorithmic awareness, and critical evaluation of AI-generated content.

(2) Systematic Adoption of a Metacognitive Approach—Curricula should explicitly incorporate metacognitive strategies to substantially enhance students’ inherent ability to plan, monitor, and evaluate their information-seeking activities, thereby ensuring reflective and autonomous engagement with digital libraries.

(3) Strategic Enhancement of Digital Library Infrastructure—Educational institutions should judiciously invest in advanced, AI-powered digital library systems with sophisticated search capabilities, personalized recommendations, and universally accessible interfaces, while simultaneously ensuring complete transparency and fairness in algorithm design.

(4) Mandatory Provision of Ongoing Professional Development—Faculty and librarians should proactively receive continuous training on integrating emerging AI tools and digital library resources into their daily teaching, research, and critical academic support services.

(5) Active Promotion of Ethical and Critical Digital Practices—Institutional policies should explicitly emphasize the responsible use of AI and digital resources, including vital aspects such as data privacy protection, maintaining academic integrity, and ensuring equitable access to information.

By diligently implementing these strategic recommendations, teacher education institutions can optimally prepare future educators to navigate complex, AI-enhanced learning environments not only responsibly, but also ethically, and with demonstrably greater effectiveness.

Practical Implications

The present investigation offers several highly pertinent practical implications. Foremost, it provides educational policymakers and curriculum designers in colleges with actionable insights to strengthen students’ digital competence and AI literacy, thereby enabling the creation of more targeted improvements in instructional materials and digital skills training. Moreover, the conclusions also directly support university libraries and academic support centers in designing effective, learner-centered programs and developing fully updated services that actively foster a deeper level of engagement with digital research tools. Significantly, the findings assist faculty members in the successful integration of digital and AI competencies into their courses and scholarly research activities, consequently and substantially enriching teacher preparation programs. Regarding professional development, the study identifies highly specific skill gaps and literacy needs among educators, which can subsequently guide the design of more tailored and customized training initiatives. Finally, by offering a rigorously locally grounded, data-driven investigation that reflects global educational priorities, the study contributes meaningfully to both national and international efforts to advance future-ready digital education for all stakeholders.

Footnotes

ORCID iD

Fahad Alenezi

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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