Knowledge, Attitude, and Practices toward Artificial Intelligence among University Students in Lebanon

Kharroubi, Samer A.; Tannir, Iman; Abu El Hassan, Rasha; Ballout, Rouba

doi:10.3390/educsci14080863

Open AccessArticle

Knowledge, Attitude, and Practices toward Artificial Intelligence among University Students in Lebanon

¹

Office of Student Affairs, American University of Beirut, P.O. Box 11-0236, Riad El Solh, Beirut 1107-2020, Lebanon

²

Department of Nutrition and Food Sciences, Faculty of Agricultural and Food Sciences, American University of Beirut, P.O. Box 11-0236, Riad El Solh, Beirut 1107-2020, Lebanon

^*

Author to whom correspondence should be addressed.

Educ. Sci. 2024, 14(8), 863; https://doi.org/10.3390/educsci14080863

Submission received: 24 June 2024 / Revised: 26 July 2024 / Accepted: 2 August 2024 / Published: 9 August 2024

(This article belongs to the Special Issue Educational Technology’s Influence in Higher Education Teaching and Learning—Series 2)

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Abstract

:

Background: The expansion of artificial intelligence (AI) across diverse sectors worldwide demands an understanding of its impact on future generations. The studies of its influence on university students’ behavior and application in Lebanon are still limited. The present study aimed to explore the knowledge, attitudes, and practices (KAPs) of university students regarding AI and to identify factors affecting these dimensions. Methods: An online questionnaire (n = 457) was distributed to university students who were at least 18 years of age across Lebanon. Results: The results revealed that a significant majority (97.2%) of the participants were familiar with AI, from which 43% demonstrated a high level of knowledge. Furthermore, attitude toward AI role and integration in academic and professional paths was moderately satisfactory (43%), although it was reportedly used by 75% of students throughout their university years. There was a significant association between knowledge levels and sociodemographic factors such as age, sex, source of AI-related information, and knowledge rating (p < 0.05), whereas the academic major and knowledge rating affected attitudes toward AI (p < 0.05). Conclusion: These findings support the incorporation of AI education within the curriculum to increase acceptance of AI as a modern tool enhancing various sectors and serving as a facilitator for teaching and learning processes.

Keywords:

artificial intelligence; universities; knowledge; attitudes; practices; Lebanon

1. Introduction

Marvin Minsky, who pioneered artificial intelligence, defined AI as “the science of making machines do things that would require intelligence if done by men” [1]. AI has received significant attention in recent years and is frequently seen as heralding the fourth industrial revolution according to many experts in the field [2]. Its technologies have been integrated into various sectors, including finance, law, cybersecurity, manufacturing, computer science, and dentistry [3,4]. It has become an integral part of everyday life, specifically by technologies such as Siri, face recognition systems, and self-driving cars [5,6].

Universities, as key contributors to the development of a knowledgeable and responsible society, are also impacted by the disruptive force of AI [7]. These institutions must adapt to the evolving changes that this technology may bring. It is imperative to incorporate AI training into a broader range of academic programs, not just those focused on computer science, to ensure that students are equipped to fulfill the needs of the job market in their future professional careers [8]. Understanding how students perceive and interact with AI is crucial for the successful integration of AI technologies into the educational system, whether as a subject of study, a teaching tool, or a tool that can assist students during their academic years. For instance, a study carried out among university students in Spain suggested that the use of artificial intelligence tools in the classroom combined with human interactions helps in optimizing students’ educational experience [9]. Similarly, a study performed in the United States revealed that AI tools may complement human academic advisers and promote educational equity [5].

Moreover, several studies worldwide have investigated students’ perceptions of AI, where some studied knowledge, attitudes, and practices (KAPs) toward AI among university students mainly in the healthcare field. A cross-sectional study performed in Pakistan among doctors and medical students revealed that almost 69% of the students possessed basic knowledge of AI, and 76.7% supported its inclusion in the curriculum [10]. Another study carried out among dental students in Riyadh highlighted a significant knowledge gap regarding AI, with 50.1% of the participants unaware of AI’s working principles, and almost half of them unaware of its usage in their field [11]. Also, a low level of knowledge was seen among medical students in Jordanian universities, where only 47% of them said they understood the fundamentals of artificial intelligence. Interestingly 71.4% of the students thought that teaching AI in the medical profession would be beneficial, and 89% said that AI was important in the medical field [12]. Despite limited current knowledge, students are willing to further their education in AI [6,13]. A positive perception toward the use of AI technologies in online classes was shown among students from a private university in Indonesia. In their opinion, this implementation can improve autonomous and fun learning [14]. In the US, a study assessing college students’ perceptions of AI demonstrated divergent opinions, with individuals who were both optimistic about the personal benefits from AI and concerned about its rapid development and potential impact on employment. Notably, individuals with greater knowledge and understanding of AI expressed higher uncertainty about its outcomes, emphasizing the major influence of participants’ information level on their impression of AI [15]. Similarly, diverse perceptions were viewed among students and teachers in the Philippines [16]. Additionally, a study carried out in Taiwan revealed significant disparities in how different college major groups perceived AI. Business majors viewed AI as being more virtuous than humanities majors did, which is consistent with previous studies. Thus, it is crucial to take disciplinary backgrounds into account when analyzing public attitudes regarding AI [17].

Studies focusing on AI among university students in the Middle East and North Africa region (MENA), specifically in Lebanon, are insufficient, resulting in a lack of comprehensive data on this topic. In Lebanon, one study was performed across the seven medical schools to assess knowledge and attitude of students toward AI in medical education. Interestingly, a higher level of knowledge was seen among students who have been introduced to the fundamentals of AI through their academic curriculum than their peers who did not. About 26.8% of the students believed that AI influenced their choice of specialty. Despite recognizing the objectivity of AI-based assessment methods, a small proportion of students (26.5%) desire to be evaluated by it [3]. To the best of our knowledge, this will be the first study in Lebanon investigating the KAPs toward AI among university students across different majors.

Therefore, the main goals of this study are to assess the KAPs regarding AI among university students in Lebanon and explore the associations between sociodemographic, university factors, and KAP levels.

2. Materials and Methods

2.1. Study Design and Sampling

The current study took place from October 2023 to January 2024. It involved university students from across Lebanon. Based on the World Health Organization (WHO) sample size calculator [18], a total of 384 respondents were required to estimate a prevalence of 50% with a 95% confidence interval (CI) and a margin of error of 5%, considering a design effect of 1.5. However, to accommodate a 20% refusal rate, this study aimed to recruit 480 participants.

2.2. Data Collection

The survey link was sent out via several social media platforms such as WhatsApp, Instagram, Twitter, LinkedIn, and Facebook. Once students read the consent form (Supplementary File S1) and approved to participate in this study, they began completing the survey (Supplementary File S2), which took approximately 5 min.

Participation was completely anonymous and voluntary. Moreover, the Institutional Review Board (IRB) at the American University Beirut approved this study, and the research team held Collaborative Institution Training Initiative (CITI) certification.

2.3. Survey Format

The questionnaire was formulated based on previous studies with similar objectives [3,19,20] and was intended to evaluate the knowledge, attitudes, and practices toward AI among a sample of university students. It was divided into five sections. The initial section encompassed questions regarding respondents’ sociodemographic attributes such as age, sex, and major. The second section focused on the respondents’ basic knowledge of AI. The next section included questions on the respondents’ attitudes toward the role and implementation of AI in their studies. The fourth section included questions on students’ application of AI during their university years. The last section included open-ended questions to identify advantages of AI and potential barriers that may prevent students from implementing AI. A pilot study was carried out on 20 students to assess the clarity of the questionnaire. However, the data gathered during this pilot testing phase were not incorporated into the analysis.

2.4. Statistical Analysis

Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 29.0 (SPSS Inc., Chicago, IL, USA). For continuous variables, descriptive statistics reported means and standard deviation (SD), whereas for categorical variables, it included frequencies and percentages. Each question related to knowledge and attitude toward AI had two possible responses: affirmative (yes/agree) or negative (no/disagree), with each affirmative response scored one point and each negative response scored zero point. Total knowledge and attitude scores were calculated for each student by summing the number of affirmative responses. These total scores were then dichotomized based on whether they were below or above the mean score. Participants with scores below or equal to the mean were categorized as having low to moderate knowledge and negative to moderate attitude, while those with scores above the mean were categorized as having a high level of knowledge and a positive attitude. Chi-square analysis was employed to explore relationships between categorical variables. Simple logistic regression and multiple logistic regression analyses were conducted to identify factors associated with knowledge and attitude levels. Sociodemographic characteristics were treated as independent variables, while total knowledge and attitude score ratings served as dependent variables. A p-value of less than 0.05 was considered statistically significant.

3. Results

3.1. Sociodemographic Characteristics of Participants

Table 1 presents the sociodemographic characteristics of the participants. Results showed that half of the respondents were between 18 and 20 years old (50.1%), and more than half of them were women (58.6%). Almost two-thirds of the students were pursuing a bachelor’s degree (65.9%). The study sample comprised students from different majors, mainly from health-related majors (27.8%) and engineering and computer science (18.4%). Furthermore, almost half of the participants obtain their AI-related information from the Internet and social media platforms (50.8%), while 15.3% obtain it from the curriculum, and 23% of the students had no source at all. Most of the students self-reported possessing a moderate level of AI knowledge (47.3%), whereas 22.8% perceived their knowledge as high and 30% low.

3.2. Basic AI Knowledge

Table 2 summarizes the basic AI knowledge of university students. The mean score of correct answers was 3.37 ± 1.34. The majority (97.2%) of the participants knew what AI is. Most of them (78.6%) have heard of AI chatbot, and the majority (83.4%) knew that for AI to “learn”, it necessitates a substantial amount of labeled data. Only 44.4% and 33.5% of the participants knew, respectively, about machine learning and deep learning, which are subtypes of AI.

3.3. Attitude toward AI

Table 3 summarizes students’ attitude toward the implementation of AI in their university years and later on in their careers. The mean score of positive attitudes was 2.91 ± 1.753. More than half of the participants were in favor of implementing AI in their field (56.9%). Half of them agreed that the development of AI affects their career orientation. Moreover, more than 60% of the participants saw that AI has a positive impact on students’ academic performance (65.2%), and that it should be incorporated in the curriculum/classroom (62.6%). About 55.6% of the students were in favor of using AI as an assessment tool. Interestingly, the majority of the participants agreed that AI poses security risks such as data privacy (85.3%).

3.4. AI Practices

Table 4 summarizes some AI practices among university students. Only 16% of the participants have ever completed an internship or work related to AI. Lower percentages were reported regarding being a member of any AI club at the university (7.4%). Furthermore, 17.3% of the students have taken elective courses or workshops focused on AI outside their regular curriculum. However, the majority of the participants have used AI technologies or applications during their university years (75.3%), such as ChatGPT. About 89.5% of them reported that AI made their task easy.

3.5. Chi-Square Analysis

The results of the chi-square analysis presented in Table 5 revealed that sex (p < 0.001), major (p < 0.001), source of AI-related information (p < 0.001), and the self-reported level of AI knowledge (p < 0.001) were all significantly associated with the level of basic AI knowledge.

The results of the chi-square analysis presented in Table 6 revealed that major (p < 0.001), source of AI-related information (p < 0.001), and the self-reported level of AI knowledge (p < 0.001) were all significantly associated with the level of AI attitude.

The results of the chi-square analysis also revealed that students’ major was significantly associated with the different AI practices as shown in Table 7, Table 8, Table 9 and Table 10.

3.6. Simple and Multiple Logistic Regressions

In the simple logistic regression analysis, five predictors were found to be significantly associated with respondents’ knowledge levels (Table 11), including 1—age (OR = 2.792, p = 0.025); 2—sex (OR = 0.362, p < 0.001), where women were less likely to have better knowledge than men; 3—major (engineering, CS: OR = 5.152, p < 0.001; other: OR= 0.399, p = 0.014); 4—the source of AI-related information (Internet: OR = 0.066, p < 0.001; other: OR = 0.119, p < 0.001; no source: OR = 0.01, p < 0.001); and 5—self-rated AI knowledge (moderate: OR = 3.651, p < 0.001; high: OR = 61.75, p < 0.001). The results of the multiple regression analysis revealed that respondents aged 27 or above were more likely to have a better understanding of AI than those aged between 18 and 20 (OR = 4.059, p = 0.013). Female students were less likely to have higher knowledge scores compared with men (OR = 0.382, p < 0.001). Moreover, students with no source of information were less likely to show a higher knowledge level than those who obtain their information from the curriculum (OR = 0.101, p < 0.001). Students who described having a moderate or high level of AI knowledge were more likely to have a higher knowledge score than those who described having a weak level (moderate: OR = 2.596, p = 0.004; high: OR = 21.554, p < 0.001).

Similarly, in the simple logistic regression analysis, variables significantly correlated with the likelihood of having a positive attitude included the major (engineering, CS: OR = 2.714, p = 0.007; health-related: OR = 0.289, p = 0.008; other: OR = 0.343, p = 0.004); the source of AI-related information (Internet: OR = 0.281, p < 0.001; other: OR = 0.346, p = 0.007; no source: OR = 0.067, p < 0.001); and the self-rated AI knowledge (moderate: OR = 3.493, p < 0.001; high: OR = 17.549, p < 0.001). In the multiple logistic regression analysis, students who described having a moderate or high level of AI knowledge were more likely to have a positive attitude toward the role and implementation of AI than those who described having a weak level of knowledge (moderate: OR = 2.984, p < 0.001; high: OR = 10.463, p < 0.001). Additionally, major was significantly correlated with the likelihood of having a positive attitude (OR = 0.41, p = 0.029).

These findings highlight the importance of taking demographic factors into account when assessing knowledge and perceptions of AI in education.

4. Discussion

To the best of our knowledge, the present study is the first conducted in the MENA region, including Lebanon, that investigates KAP levels toward AI among university students across all academic majors.

The findings from this study revealed a low to moderate level of knowledge regarding AI among the survey participants. About 97.2% of the participants stated that they understood the term “artificial intelligence”, but only 44.4% and 33.5% knew about its subtypes machine learning and deep learning, respectively. This finding was in line with other studies performed in Pakistan and Vietnam among healthcare students [10,21]. On the other hand, students demonstrated strong understanding of other aspects of AI. Specifically, 83.4% were aware that for AI to “learn”, it necessitates a substantial amount of labeled data, which is similar to studies carried out in Lebanon and Jordan among medical students [3,22]. As to the sources of AI knowledge, 15% of the participants reported receiving their information from their academic curriculum, whereas half of the participants (50.8%) reported receiving their information from the Internet and social media. When it comes to social media as the source of information, higher percentages were observed in several studies such as one performed in the US (72%) and the Lebanese study (81.1%) [3,23]. This can be justified by the fact that the present study explored a knowledge level across several academic majors as opposed to the other studies that only included medical students.

The results of the regression analysis showed that the source of AI information was significantly correlated with the level of knowledge, which demonstrates the importance of incorporating AI into the curriculum to ensure better quality and reliable sources. Moreover, our study revealed a statistically significant disparity in knowledge between male and female respondents, where men were more likely to show a better level of knowledge than women (p < 0.001). These results correspond with a study carried out in Germany [24].

Interestingly, a moderate level of knowledge was observed giving that the majority of the participants (>80%) had never participated in internships, or trainings related to AI, nor taken any elective courses outside their regular curriculum (p < 0.001). Similar findings were observed in a study performed in India by Kansal et al. [25], whereas significant association was not found in a study carried out in Lebanon [3].

Regarding their attitude toward AI, almost half of the participants agreed that it affected their career choices, which is close to the results obtained by Park et al. who demonstrated that 44% of participants agreed that development of AI is influencing their choice of specialty [26]. Furthermore, more than half of the participants were in favor of incorporating AI into the classroom whether as a subject or an assessment tool giving its positive impact on their academic performance. However, higher percentages were shown in studies performed in Pakistan and the US, where the majority of the respondents agreed on the inclusion of AI in the medical school curriculum [23,27]. About 85.3% of the students agreed that AI can pose security risks such as invasion of data privacy. Those beliefs were shared with other studies [21,28].

Our findings did not reveal a statistically significant difference in the attitude score between the students who obtained their AI information from their curriculum and those who did not. This may suggest that incorporating AI into the curriculum alone is not enough; further research is warranted to investigate other factors that can affect students’ perceptions of AI. Moreover, sex did not affect the attitude level among students, which may support the concept that both men and women are interested in AI [21].

The current study highlighted that students across several majors have started to incorporate AI in their studies. The most commonly reported AI applications were essay writing, grammar checking, and generating Power Point, which focuses mainly on research assistance.

When diving into the challenges associated with AI, the mostly common answer between students was the fear of replacing humans in many work fields, which would lead to the increase in unemployment rates. Besides, some believe that students are becoming very dependent on AI such as the excessive use of ChatGPT during academic years, limiting human creativity. Extracurricular academic events can be made in order to create awareness among students about both the advantages and limitations of AI.

5. Conclusions

In conclusion, to ensure reliable and comprehensive understanding, more integration of AI education into university curricula is required. Sex variations in knowledge levels highlight the need to address disparities in AI education and training opportunities, and efforts should be made to ensure equal access to AI for all students. Additionally, even though students share concerns about the risks associated with AI, they also show support for its incorporation into the classroom.

In addition to providing students the skills they will need for the future workforce, integrating AI into academic programs encourages ethical decision-making and critical thinking in the creation and application of AI technology. By addressing these challenges and opportunities, universities and policymakers can better prepare the next generation of leaders to deal with the complexities of an AI-driven world.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/educsci14080863/s1, Supplementary File S1: Consent form; Supplementary File S2: The questionnaire.

Author Contributions

Conceptualization, S.A.K., I.T., R.A.E.H. and R.B.; methodology, S.A.K.; software, S.A.K. and R.B.; validation, S.A.K., I.T., R.A.E.H. and R.B.; formal analysis, S.A.K. and R.B.; investigation, S.A.K. and R.B.; resources, S.A.K., I.T., R.A.E.H. and R.B.; data curation, S.A.K. and R.B.; writing—original draft preparation, S.A.K., I.T., R.A.E.H. and R.B.; writing—review and editing, S.A.K., I.T., R.A.E.H. and R.B.; visualization, S.A.K. and R.B.; supervision, S.A.K.; project administration, S.A.K.; funding acquisition, S.A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the University Research Board (Grant number 104397) and the Board Designated Professorship (Grant number 514028) at the American University of Beirut, Lebanon.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the American University of Beirut (protocol code SBS-2023-0299; 26 October 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy of participants and ethical concerns.

Acknowledgments

S.A.K. would like to thank the University Research Board and the Board Designated Professorship at the American University of Beirut for funding this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Sociodemographic characteristics of participants (n = 457).

Characteristics		n (%)
Age	18–20	229 (50.1)
	21–23	158 (34.6)
	24–26	47 (10.3)
	27+	23 (5)
Sex	Men	185 (40.5)
	Women	268 (58.6)
	Prefer not to respond	4 (0.9)
Education level	Freshman	15 (3.3)
	Bachelor	301 (65.9)
	Master, PhD	141 (30.9)
Major	Business	53 (11.6)
	Engineering, CS	84 (18.4)
	Health-related	127 (27.8)
	Arts	39 (8.5)
	Sciences	74 (16.2)
	Other	80 (17.5)
Source of information regarding AI	Curriculum	70 (15.3)
	Internet/Social media	232 (50.8)
	Other	50 (10.9)
	No source	105 (23)
Rate your level of knowledge about AI	Low	137 (30)
	Moderate	216 (47.3)
	High	104 (22.8)

Table 2. Questions related to students’ AI knowledge.

Question	Yes n (%)	No n (%)
Do you know what artificial intelligence (AI) is?	444 (97.2)	13 (2.8)
Do you know about machine learning (subtype of AI)?	203 (44.4)	254 (55.6)
Do you know about deep learning (subtype of AI)?	153 (33.5)	304 (66.5)
Have you ever heard of AI Chatbot?	359 (78.6)	98 (21.4)
For AI to “learn”, it requires a large amount of labeled data (information already processed by a human and clearly labeled)	381 (83.4)	76 (16.6)

Table 3. Questions related to students’ attitude toward AI.

Question	Agree n (%)	Disagree n (%)
Are you in favor of implementing AI in your field?	260 (56.9)	197 (43.1)
The development of AI affects my career orientation	230 (50.3)	227 (49.7)
AI has a positive impact on students’ academic performance	298 (65.2)	159 (34.8)
AI should be incorporated in the curriculum/classroom	286 (62.6)	171 (37.4)
AI should be used as an assessment tool	254 (55.6)	203 (44.4)
AI poses security risks	390 (85.3)	67 (14.7)

Table 4. Questions related to students’ AI practices.

Question	Yes n (%)	No n (%)
Have you ever completed an internship/work related to AI?	73 (16)	384 (84)
Are you a member of any AI or machine-learning clubs or student groups at your university?	34 (7.4)	423 (92.6)
Have you taken any elective course or workshop/training focused on AI, outside your regular curriculum?	79 (17.3)	378 (82.7)
Have you used AI technologies or applications during your university years?	344 (75.3)	113 (24.7)
Did AI make your task easy (n = 344)	308 (89.5)	36 (10.5)

Table 5. Association between AI knowledge level and sociodemographic characteristics.

Characteristics		n (%)	High Knowledge n (%)	Low-Moderate Knowledge n (%)	χ²
Age	18–20	229 (50.1)	92 (49.2)	137 (59.8)	0.133
	21–23	158 (34.6)	71 (44.9)	87 (55.1)
	24–26	47 (10.3)	20 (42.6)	27 (57.4)
	27+	23 (5)	15 (65.2)	8 (34.8)
Sex	Men	185 (40.5)	107 (57.8)	78 (42.2)	<0.001
	Women	268 (58.6)	89 (33.2)	179 (66.8)
	Prefer not to respond	4 (0.9)	2 (50)	2 (50)
Education level	Freshman	15 (3.3)	6 (40)	9 (60)	0.964
	Bachelor	301 (65.9)	131 (43.5)	170 (56.5)
	Master, PhD	141 (30.9)	61 (43.3)	80 (56.7)
Major	Business	53 (11.6)	25 (47.2)	28 (52.8)	<0.001
	Engineering, CS	84 (18.4)	69 (82.1)	15 (17.9)
	Health-related	127 (27.8)	42 (33.1)	85 (66.9)
	Arts	39 (8.5)	16 (41)	23 (59)
	Sciences	74 (16.2)	25 (33.8)	49 (66.2)
	Other	80 (17.5)	21 (26.3)	59 (73.8)
Source of information regarding AI	Curriculum	70 (15.3)	64 (91.4)	6 (8.6)	<0.001
	Internet/social media	232 (50.8)	96 (41.4)	136 (58.6)
	Other	50 (10.9)	28 (56)	22 (44)
	No source	105 (23)	10 (9.5)	95 (90.5)
Rate your level of knowledge about AI	Low	137 (30)	20 (14.6)	117 (85.4)	<0.001
	Moderate	216 (47.3)	83 (38.4)	133 (61.6)
	High	104 (22.8)	95 (91.3)	9 (8.7)