Abstract
The rapid integration of artificial intelligence (AI) into educational environments has created an urgent need for students to acquire the competencies required to effectively employ AI applications in teaching and learning contexts. However, limited evidence exists regarding the extent to which higher education students possess these competencies and the role of educational technology courses in developing them, particularly within the Kuwaiti context. Accordingly, this study examines the level of AI-related competencies among students at the College of Basic Education in the State of Kuwait and investigates the effectiveness of educational technology courses in enhancing these competencies. A descriptive-analytical methodology was adopted, employing a 35-item questionnaire distributed across three domains: cognitive, performance-based, and applied competencies. The sample comprised 445 students, including 83 students majoring in educational technology and 362 students from other specializations. The findings indicated that students demonstrated a moderate overall level of AI competencies (53.8%), with statistically significant differences favoring students enrolled in educational technology programs. These findings highlight the importance of revising educational technology curricula to incorporate advanced AI applications, as well as introducing dedicated AI modules across academic disciplines.
1 Introduction
The contemporary world is witnessing rapid transformations driven by the Fourth Industrial Revolution. Artificial intelligence (AI) has emerged as one of its most prominent outcomes, profoundly impacting various sectors, particularly education. AI has emerged as a major driver of innovation by enhancing human-machine interaction and initiating qualitative shifts in learning and teaching practices. Given the rapid pace of technological advancement, there is an increasing need to align educational systems with modern tools - particularly AI - due to its vital role in shaping learning environments, fostering creativity, and improving educational quality.
The importance of AI lies in its ability to simulate human thinking and perform complex cognitive tasks, which necessitates equipping learners with the knowledge and competencies required for its effective utilization. Several studies have highlighted students’ lack of readiness to use AI technologies in education due to deficiencies in both technical and cognitive competencies-factors that hinder the full utilization of AI capabilities (Al-Khaibari, 2020; Al-Saadi, 2022). In contrast, other studies (Luckin et al., 2016; Chakroun and Daelman, 2018; Joshi and Panigrahi, 2020; Al-Malki, 2023) emphasize the importance of preparing students and teachers through specialized educational programs to ensure the effective integration of AI in education.
Despite the potential of AI to foster analytical and critical thinking skills, some studies (Parsons et al., 2018; Karsenti, 2019) have reported weaknesses in educational curricula and technological infrastructure that limit the adoption of AI-based applications in educational settings. These challenges highlight the need to examine the role of educational technology courses in enhancing students’ competencies, particularly among students majoring in educational technology, compared to their peers in other academic disciplines.
In the context of the College of Basic Education in Kuwait, the integration of AI applications remains limited across most academic programs. AI-related content is largely confined to specific courses within the educational technology specialization, while students from other disciplines receive minimal exposure to AI applications or structured opportunities for practical training. As a result, concerns have been raised regarding students’ preparedness to employ AI tools effectively in educational practice.
AI technologies represent some of the most significant innovations in the education sector, representing effective tools for improving educational quality and enhancing learning outcomes. However, the effective use of these technologies depends on students’ possession of the necessary technical competencies. The International Conference on Artificial Intelligence and Education (UNESCO, 2019) emphasized the importance of empowering students with these competencies to achieve optimal educational outcomes.
The researcher observes that the implementation of AI applications still faces considerable challenges in higher education institutions, despite their significant potential in supporting the educational process. These challenges include limited usage, low awareness of AI capabilities, and students’ lack of readiness to engage with these technologies. Accordingly, the research problem lies in the insufficient ability of students across various specializations to employ AI applications in educational contexts.
Previous research in higher education contexts indicates that the adoption of artificial intelligence applications remains limited due to low awareness and insufficient student readiness (Zawacki-Richter et al., 2019). Accordingly, the current study examines this issue within the context of students at the College of Basic Education in Kuwait.
The research problem was supported by multiple sources of evidence, most notably a pilot survey conducted among 50 male and female students at the College of Basic Education in Kuwait. The results highlighted a lack of AI-related academic courses across most disciplines, limited student competencies in AI applications, and insufficient training opportunities. These findings are consistent with previous research indicating gaps in AI literacy and curricular integration within higher education institutions (Zawacki-Richter et al., 2019).
Although previous studies have reported challenges related to students’ AI competencies, attitudes, training, and infrastructure (Al-Khaibari, 2020; Al-Farani and Al-Hujaili, 2020), they have not specifically examined the role of educational technology courses in developing students’ ability to employ AI applications, nor compared students across different academic disciplines within the Kuwaiti context. Therefore, this study addresses this gap by investigating the impact of educational technology courses on students’ effective use of AI in educational settings.
Accordingly, this study seeks to identify the current level of students’ competencies at the College of Basic Education in employing AI applications within educational contexts. It further evaluates the effectiveness of educational technology courses in enhancing students’ abilities to utilize AI tools for pedagogical purposes. In addition, the study aims to measure the effect size of studying educational technology courses on the development of students’ technical and digital competencies. Finally, it analyzes the extent to which academic specialization–educational technology versus other academic disciplines–influences students’ competencies in employing AI applications in education.
The theoretical significance of this study emphasis on the role of educational technology courses in developing students’ competencies related to the use of AI applications. By examining this relationship, the study contributes to Strengthening the academic understanding of how educational technology influences the development of students’ technical, digital, and cognitive competencies. Moreover, it underscores the importance of integrating AI into educational practices as a critical approach to promote modern, effective and technology-driven learning outcomes that align with contemporary educational demands.
From a practical perspective, this study provides academic program developers with a structured framework for identifying students’ training needs, thereby supporting the design of curricula that enable learners to acquire the knowledge and skills necessary to effectively employ AI applications in educational setting. Additionally, the findings assist higher education policymakers in identifying educational content that enhances students’ readiness to utilize AI applications efficiently. The study also offers evidence-based recommendations to decision-makers aimed at strengthening AI integration within higher education institutions and improving the overall quality and effectiveness of educational processes.
2 Research questions
The research seeks to answer two key questions. Question 1: To what extent do students at the College of Basic Education possess the competencies needed to employ artificial intelligence applications in education? And Question 2: How effective are educational technology courses in empowering students majoring in educational technology with the competencies to employ artificial intelligence applications in education? What is the effect size of studying these courses?
3 Research hypothesis
The study tests the null hypothesis which states that: “there are no statistically significant differences in the level of competencies required to employ AI applications in education between students majoring in educational technology and those in other disciplines at the College of Basic Education in Kuwait.”
4 Literature review
Previous literature examined the integration of artificial intelligence in education and its role in enhancing learning processes and student competencies. Several studies have emphasized that AI-supported educational environments can improve attention, information processing, and knowledge retention by aligning with cognitive and information-processing principles (Al-Mungdi and Al-Sudi, 2024). In addition, recent research has shown that embedding AI tools within educational technology courses contributes to increased student engagement, interaction, and the development of higher-order thinking skills (Holmes et al., 2022). These findings provide a relevant foundation for examining the role of educational technology courses in supporting students’ effective use of AI applications within higher education contexts.
4.1 Educational technology
According to the Association for Educational Communications and Technology (AECT), educational technology is defined as “a complex process involving individuals, procedures, tools, and organizations for solving, analyzing, implementing, and evaluating educational problems” (Olayan and Al-Dabbas, 2003). UNESCO (2019) considers it “a systematic approach to designing and implementing education according to specific objectives, through human and non-human means to improve the efficiency and effectiveness of education.”
The objectives of educational technology include improving the educational process, analyzing challenges, proposing forward-looking solutions, and preparing qualified professionals in the field (Al-Bajawi and Al-Masoudi, 2020). Educational technology plays a pivotal role in advancing the education system, fostering interactive student engagement and enhances motivation. It also accommodates diverse learning styles and individual differences. Furthermore, it empowers teachers to employ innovative digital tools that present content in visual and realistic formats, helping to clarify abstract concepts and make them more accessible to learners (Al-Eidan and Al-Jabr, 2018).
4.2 Artificial intelligence (AI)
Artificial intelligence (AI) is a branch of computer science concerned with developing systems capable of simulating human intelligence in tasks such as learning, reasoning, decision-making, and interacting with the environment (Dai et al., 2023; Dirar, 2019; Abu Zayed, 2017).
Key AI applications include machine learning, which enables systems to learn from experience; deep learning, which uses neural networks to analyze large datasets; expert systems, which simulate expert-level decision-making; and intelligent agents capable of performing autonomous tasks (Al-Kawar, 2023). These technologies are embedded in everyday tools such as search engines, smart systems, and mobile devices (Guan et al., 2020).
AI enhances information retrieval through natural language processing, machine translation, and speech recognition. It also supports knowledge representation through rule-based systems and semantic networks. AI is characterized by high processing speed and accuracy, efficient data management, and the ability to reason, perceive, and represent knowledge. AI’s advanced capabilities include problem-solving, experiential learning, environmental adaptation, creativity, data analytics, symbolic representation, and handling incomplete information (Russell and Norvig, 2020; Al-Muraikhi, 2023).
As a result, AI has emerged as a transformative digital force reshaping cognitive and decision-making processes. Its expanding integration into the education sector is vital for fostering more interactive and effective learning environments.
4.2.1 Artificial intelligence and education
The integration of AI technologies into education contributes significantly to educational development. It serves as a promising tool driving a qualitative transformation in learning environments, improving the quality of the educational process, and enhancing interaction between teachers and learners (Al-Roumi and Al-Qahtani, 2023). In this context, the UNESCO (2019) report identified five main domains for employing AI in education, including enhancing computational thinking, supporting critical thinking, processing large datasets, adapting to dynamic environments, and re-evaluating foundational concepts such as creativity and expertise.
AI systems can analyze curricula and student behavior, enabling the design of individualized learning environments tailored to student needs. Moreover, they offer accurate performance assessment tools, enabling teachers to focus on engaging with students, especially in labs and interactive lectures that encourage open participation. AI contributes to increasing motivation and reducing passive learning (Guan et al., 2020). This is supported by a study (Saleem, 2017), which demonstrated that AI is reshaping the student’s role from a passive recipient into an active participant capable of responding to societal changes, thereby enhancing educational quality. In addition, AI offers paths tailored to individual needs, along with accurate performance reports that enhance adaptive learning (Chassignol et al., 2018). Consequently, the use of AI in education has grown.
To harness the potential of AI, it is essential to develop qualified human resources, including faculty members and students, who possess the technical skills required to effectively operate AI systems. A lack of qualifications can limit the effectiveness of the application and hinder the achievement of its educational objectives.
4.2.2 Technological competencies and the integration of AI
Technological competencies refer to the knowledge and skills that enable individuals to effectively use technology in performing educational tasks and interacting with AI technologies (Tsankov and Damyanov, 2019). They include cognitive, performance-based, and applied competencies required to use AI efficiently. Asiri (2022) categorizes these competencies into: general competencies involving the use of computers and multimedia; internet-related competencies such as online searching and email communication; and E-course design competencies, including content development and platform management.
4.3 Levels of competencies for employing AI applications
The competencies required for employing AI applications in education can be classified into three levels:
4.3.1 Cognitive competencies
Form the foundational understanding necessary for the effective use of AI in educational settings. They include intellectual skills that help students understand basic technical concepts such as machine learning and large data processing, analyzing them to extract patterns, and make informed educational decisions. They also include the ability to integrate AI tools into curricula, interact positively with technology, and develop skills in creative thinking, problem-solving, and critical evaluation of intelligent tools. These also include awareness of the ethical and legal implications of AI usage, including privacy protection and data security (Miao et al., 2024; Ahmad and Rahmat, 2023).
4.3.2 Performance competencies
Refer to practical skills that empower educators to effectively perform technical tasks. These include operating smart educational platforms such as Google Classroom, analyzing performance data using tools like Tableau, utilizing educational robots, and organizing content through intelligent applications like MindMeister. These competencies represent a practical embodiment of theoretical knowledge, contributing to improved educational quality and enhanced student engagement (Ahmad and Rahmat, 2023; Muhammad et al., 2024).
4.3.3 Applied competencies
Reflect the ability to translate theoretical knowledge into practical and effective educational applications; examples include designing digital learning environments that take individual differences into account and incorporating AI into curricula to enhance adaptive learning. They also include developing educational applications, designing interactive content using tools such as Articulate 360, and creating virtual learning environments. Furthermore, these competencies extend to using intelligent assessment systems like Gradescope and analyzing educational data using tools like SPSS (Crompton and Burke, 2023; Muhammad et al., 2024).
Equipping students with these competencies enhances their ability to effectively utilize AI applications. It enables them to keep pace with digital developments, think critically and creatively, and improve educational quality through smart content design and performance evaluation tools. Furthermore, it contributes to building an interactive learning environment that encourages innovation and empowers students as active participants in the educational process, particularly in context of the current rapid technological transformation.
4.4 Educational technology and equipping students with AI competencies
Studying educational technology courses aims to equip students with competencies necessary to engage with AI applications in educational environments. This includes the ability to design intelligent educational content suitable for university-level learners; implement adaptive learning systems that consider students’ individual characteristics and cognitive needs and develop interactive environments to enhance student motivation. Additionally, it involves updating digital and pedagogical knowledge and applying theoretical concepts in practical scenarios, thereby enhancing students’ ability to use modern technologies to improve education quality (Al-Mungdi and Al-Sudi, 2024).
4.5 Educational technology domains contributing to AI application competencies
Educational technology plays a critical role in supporting the development of students’ competencies in employing AI applications across several core domains (Abu Khutwa, 2022; Castro-Schez et al., 2021). One of the primary domains is Intelligent Tutoring Systems (ITSs), which consists of five key components that enable personalized learning tailored to individual students. The student Model monitors cognitive progress, analyzes learning behavior, and tracks errors and interaction patterns, while the Tutoring Model customizes instructional strategies to address individual learning gaps. The Assessment Model generates exercises and evaluates student responses based on cognitive reasoning rather than final answers, and the Domain Model organizes subject-specific knowledge and governs its instructional structure. Finally, the User Interface Model facilitates natural and adaptive interactions using natural language processing technologies.
Educational technology enhances the development and implementation of ITSs through three main dimensions. Technical - integrating big data and learning analytics, pedagogical which helps Promoting personalized and adaptive learning approaches, and developmental by utilizing intelligent agents and smart learning management systems.
Another significant domain is robotics and chatbots, which represent a tangible integration of technology and engineering into the educational field while offering intelligent support that enhances student interaction and engagement. Educational technology contributes to the development of these tools by improving user interfaces (UIs) for chatbots to optimize voice and text input capabilities, refining graphical and auditory aspects of natural language simulations, creating adaptive and context-sensitive responses tailored to learners’ inquiries, and analyzing the effectiveness of conversational tools in relation to different learning styles (e.g., auditory and visual preferences).
Digital reality and metaverse technologies represent a further domain that is reshaping the educational environment into an integrated and interactive learning space that transcends temporal and spatial boundaries. These technologies are applied through the integration of mobile devices into smart classrooms, the stimulation of future digital competencies via projective augmented reality designs, the simulation of science laboratories in safe and cost-effective virtual environments, the development of affordable smart tools to enhance visual and auditory cognition, and the establishment of standards to ensure the effective and sustainable integration of these technologies into the educational system.
Finally, the Internet of Things (IoT) constitutes a domain in which a network of interconnected devices collects and exchanges data through various sensors to facilitate data-driven decision-making in educational contexts. Key components include sensors that measure environmental variables, connectivity tools such as Wi-Fi and Bluetooth, data processing and analysis systems, and user interfaces to interact with the system. IoT applications in education encompass interactive e-books, tablets, smart boards, 3D printing technologies, virtual laboratories, augmented reality tools, and intelligent security systems. Educational technology supports IoT integration by linking e-learning laboratories to smart systems that enable remote interaction, automate classroom operations, develop assistive tools for learners with special needs, and create adaptive learning environments that respond to students’ individual differences.
5 Materials and methods
To achieve the study’s objectives, a descriptive-analytical research approach was adopted, as it is appropriate for examining existing phenomena and analyzing relationships between variables without experimental intervention. The study sample consisted of 445 male and female students enrolled at the College of Basic Education in Kuwait. Data were collected using a questionnaire designed to assess students’ technological competencies related to employing artificial intelligence applications in educational contexts. Sample items included statements such as “I am able to use AI applications effectively in my academic studies,” which were rated using a Likert-scale format. The collected data were statistically analyzed, and comparisons were conducted between students who had completed educational technology courses and those who had not, enabling the identification of differences attributable to prior exposure to such coursework and allowing an evaluation of their effectiveness in supporting AI use in university-level educational settings.
5.1 Research instrument
Based on an in-depth review of relevant literature and prior studies addressing the current research topic (Ramadan, 2021; Al-Saadi, 2022; Al-Suhaim, 2023), the researcher developed a questionnaire to measure the students’ competencies in employing AI in education at the College of Basic Education in Kuwait from the students’ perspectives. The instrument was constructed to reflect the conceptualization of AI employment competencies adopted in this study. The questionnaire consisted of 35 items distributed across three key dimensions: cognitive competencies, performance-based competencies, and applied competencies.
5.2 Validity and reliability
The questionnaire’s face validity was reviewed by a panel of nine experts in educational technology at the College of Basic Education in Kuwait. The experts assessed the alignment of the items with the research objectives and their clarity and relevance to the targeted domains. Based on their feedback, minor revisions were made, resulting in a final version of the instrument containing 35 items. Construct validity was further examined through a pilot study conducted with 40 female students, who were not included in the main study sample. Pearson correlation coefficients were calculated between each item and its corresponding dimension, as well as between each dimension and the overall score. The correlation coefficients ranged from 0.567 to 0.872, and were statistically significant at the 0.01 level indicating acceptable to strong construct validity.
Reliability was assessed using Cronbach’s alpha coefficient. The reliability values ranged from 0.894 to 0.915 across the three dimensions, with an overall reliability coefficient of 0.954, indicating a very high level of internal consistency and exceeding the commonly accepted threshold for educational research instruments.
5.3 Scoring and interpretation
Participants responded to the questionnaire using a five-point Likert scale, rating each item from 1, representing a very low degree, to 5, a very high degree. This scoring system was used to determine the extent to which students possess the competencies required to employ AI in educational context. Competency levels were interpreted based on the following scale, where a score between 1.00 and 1.80 indicates a very low level of competency, a score between 1.81 and 2.60 indicates a low level of competency, a score between 2.61 and 3.40 indicates a moderate level of competency, a score between 3.41 and 4.20 indicates a high level of competency, and a score between 4.21 and 5.00 indicates a very high level of competency.
5.4 Participants and data collection
The study population consisted of students enrolled at the College of Basic Education in Kuwait, including both male and female campuses, during the second semester of the 2024/2025 academic year, totaling 23,674 students. The questionnaire was distributed electronically through social media platforms and official student groups. Participation was voluntary, and responses were collected anonymously. Participants received clear instructions to complete the instrument. A total of 457 responses were received within a 2-weeks period, of which 12 were excluded due to incomplete data. The final valid sample comprised 445 students, including 83 students majoring in Educational Technology and 362 from other academic disciplines. The sample included both male and female students from different academic levels. Based on Stephen Thompson’s sample size formula, this sample size was statistically sufficient to represent the target population.
5.5 Statistical methods
Descriptive statistics, including arithmetic means, were used to determine the overall level of competencies among students in employing AI applications in education. An independent samples t-test was conducted to determine whether there were statistically significant differences in competency between students majoring in educational technology and those from other disciplines. To assess the magnitude of the observed differences, effect size measures were calculated using: Eta squared (η2) to determine the proportion of variance attributable to the independent variable, and Cohen’s d to estimate the practical significance of the difference between the two groups. Effect size interpretation followed conventional benchmarks reported in the educational research literature.
6 Operational definitions
For the purpose of the study, the following terms are operationally defined:
Educational Technology refers to the use of information technologies, such as computers and networks, to collect, store, process, and retrieve information in ways that support the educational process and enhance its overall quality.
Artificial Intelligence (AI) is defined as the ability of computer-based systems to simulate human cognitive functions, including perception, reasoning, problem solving, learning, and language comprehension, in order to perform various educational tasks.
Artificial Intelligence Applications in Education are defined as tools or systems that employ AI technologies to achieve educational objectives, including, but not limited to, chatbots, augmented reality, virtual reality, virtual laboratories, and educational robots.
Artificial Intelligence Employment Competencies refer to a set of knowledge, skills, experiences, and behaviors that the students possess, enabling them to effectively employ AI applications such as smart content, robotics, and intelligent systems. These competencies contribute to improving learning quality, increasing student engagement, and achieving educational goals, and their level is assessed based on the students’ scores on the instrument adopted in this study.
7 Results
To address the first research question concerning the extent to which the students at the College of Basic Education possess the competencies needed to employ artificial intelligence applications in education, Arithmetic mean scores were calculated for each of the three competency domains. The results are presented in Table 1.
TABLE 1
| Competency domain | Mean | Level | Rank |
|---|---|---|---|
| Cognitive competencies | 2.95 | Moderate | 1 |
| Performance-based competencies | 2.60 | Low | 2 |
| Applied competencies | 2.52 | Low | 3 |
| Overall mean score | 2.69 | Moderate | – |
Arithmetic means of students’ responses across the three competency domains.
The results indicate that students demonstrated a moderate level of competency in employing AI in education. Among the three domains, cognitive competencies recorded the highest mean score (M = 2.95), followed by performance-based competencies (M = 2.60), while applied competencies ranked lowest (M = 2.52). The overall mean score of 2.69 out of 5 corresponds to a relative weight of 53.8%., indicating a generally moderate level of AI-related competency among students. These results reflect variations across competency domains without implying casual explanations at this stage. That while students have a basic understanding of AI concepts, their ability to implement AI tools in practical contexts remains limited.
To address the second research question regarding the effectiveness of educational technology courses in empowering students majoring in educational technology with the competencies to employ artificial intelligence applications in education, and what is the impact of studying these courses, an independent samples t-test was conducted. The results are presented in Table 2.
TABLE 2
| Group | N | M | SD | t | df | P | η2 | d |
|---|---|---|---|---|---|---|---|---|
| Educational technology | 83 | 124.6 | 5.23 | 50.83 | 443 | 0.000 | 0.8536 | 4.83 |
| Other majors | 362 | 87.18 | 6.22 |
Independent samples t-test results for AI competency scores.
The results above reveal a statistically significant difference between the two groups at α ≤ 0.05. Students majoring in educational technology achieved significantly higher competency scores (M = 124.6) than students from other disciplines (M = 87.18). The effect size was large as indicated by Eta squared (η2 = 0.8536) and Cohen’s d (d = 4.83). This suggests that 85.36% of the variance in AI competency scores is attributable to students’ exposure to educational technology courses.
8 Discussion
The findings of this study indicate that students at the College of Basic Education in Kuwait demonstrate a moderate level of cognitive competencies related to artificial intelligence, while performance-based and applied competencies remain low. This suggests that although students possess basic theoretical knowledge of AI concepts, they encounter difficulties in translating this knowledge into practical educational applications. This gap may be attributed to the dominance of theory-oriented instruction within academic curricula, which often limits opportunities for experiential and hands-on learning.
Additionally, insufficient technical skills–such as programming and data analysis–along with limited technological infrastructure may further hinder the development of applied AI competencies. Similar challenges have been reported in previous studies, which emphasized the discrepancy between conceptual understanding and practical application in AI-supported education (Roll and Wylie, 2016; Sadiku et al., 2021).
The results also reveal a substantial impact of educational technology courses on enhancing students’ AI-related competencies. Students majoring in educational technology demonstrated significantly higher competency levels compared to those from other disciplines, leading to the rejection of the null hypothesis. This effect can be explained by the practice-oriented nature of educational technology curricula, which integrate AI tools and applications into instructional activities.
Overall, these findings highlight the importance of incorporating educational technology courses into teacher preparation programs to support the effective use of AI in education. This conclusion aligns with previous literature emphasizing the role of AI competencies in improving educational quality and innovation (UNESCO, 2019; Al-Roumi and Al-Qahtani, 2023).
9 Conclusion
In view of the study’s findings, several recommendations are proposed to enhance students’ competencies in employing artificial intelligence (AI) within educational settings. First, educational technology curricula should be revised to incorporate advanced AI-related topics and applications, including machine learning, data analytics, and intelligent systems. Introducing dedicated AI modules or standalone courses across academic programs is essential to ensure students gain adequate exposure to AI tools and practices. Strengthening the integration of educational technology courses with other disciplines through applied, field-based activities is also essential to foster practical and interdisciplinary earning experiences.
The study further highlights the importance of encouraging students to participate in specialized training programs and workshops beyond the formal curriculum and motivating them to conduct research projects that apply AI to address real-world educational challenges. Concurrently, higher education institutions should design digital training programs aimed at developing both technical competencies and practical skills among students and educators. Enhancing digital infrastructure providing AI-supportive tools and software, and adopting clear institutional policies to integrate AI into strategic planning are additional measures critical for the sustainable development of technological competencies.
Based on these findings, the researcher recommends revising academic curricula to include AI-focused courses covering programming, data analytics, and the design of intelligent learning environments; Practical activities using external reality (XR) technologies and educational robotics should be prioritized to support experiential learning. Moreover, professional development programs for faculty members are essential to strengthen their ability to effectively integrate AI into teaching practices.
Building upon these recommendations, future research should further explore AI competencies in educational contexts by addressing several related avenues. Comparative studies across academic disciplines can help identify differences in AI-related technical competencies, while investigations into the relationship between students’ attitudes and their actual use of AI applications may provide insights for targeted inventions. Additional research should examine the challenges faced by faculty members in integrating AI into teaching, as well as propose institutional or training-based solutions. Assessing the extent to which AI concepts are incorporated into higher education curricula in Kuwait and benchmarking these practices internationally would offer valuable perspectives. Finally, longitudinal studies evaluating the impact of AI-focused educational technology programs on student performance, motivation, and career readiness are needed to inform sustainable curriculum development and policy making.
10 Delimitations of the study
The research is delimited topically by its focus on examining the role of educational technology courses in developing students’ competencies in employing AI applications in education. In terms of human delimitations, the study was conducted on a randomly selected sample of students drawn from various academic disciplines. Spatially, the research was confined to the College of Basic Education in the State of Kuwait, encompassing both male and female campuses. Temporally, the study was carried out during the second semester of the 2024/2025 academic year.
Statements
Data availability statement
The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
AA-E: Data curation, Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Acknowledgments
We expresses sincere gratitude to the College of Basic Education for facilitating access to the research environment and providing the necessary support throughout the study.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
1
Abu KhutwaS. A. (2022). Applications of artificial intelligence in education and their implications for educational technology research.J. Egypt. Assoc. Educ. Comput.10145–162. 10.21608/eaec.2022.155589.1100
2
Abu ZayedA. A. (2017). The Role of Expert Systems in Improving High-Level Decision-Making Quality in the Palestinian Ministry of Health.Master’s thesis. Gaza: Al-Aqsa University.
3
AhmadG.RahmatH. (2023). Confirmation bias in our opinions on social media: A qualitative approach.J. Commun. Lang. Cult.347–56. 10.33093/jclc.2023.3.1.4
4
Al-BajawiS. A.Al-MasoudiM. H. (2020). Contemporary Educational Technology: Ideas and Applications.Amman: Dar Safa for Publishing and Distribution.
5
Al-EidanA. A.Al-JabrH. S. (2018). An Introduction to Educational Technology (in Arabic).Kuwait: Al-Khat Printing Press.
6
Al-FaraniL. A.Al-HujailiS. A. (2020). Factors affecting teacher acceptance of the use of AI in education in light of the unified theory of technology acceptance and use (UTAUT).J. Educ. Psychol. Sci.4215–252. 10.33850/jasep.2020.73217
7
Al-KawarM. (2023). Artificial intelligence and contemporary applications.Arab Int. J. Inf. Data Technol.3297–302. 10.21608/aijtid.2023.196615.1042
8
Al-KhaibariS. M. (2020). The degree of possession of secondary school teachers in Al-Kharj Governorate of skills for employing artificial intelligence in education.Arab Stud. Educ. Psychol.119, 119–152.
9
Al-MalkiW. F. (2023). The role of artificial intelligence applications in enhancing educational strategies in higher education (Literature Review).J. Educ. Psychol. Sci.793–107. 10.26389/AJSRP.K190922
10
Al-MungdiA. M.Al-SudiM. S. (2024). Artificial intelligence technologies and their role in developing education in higher education institutions: An analytical study.J. Arab. Peninsula Cent. Educ. Humanity Res.226–50. 10.56793/pcra2213202
11
Al-MuraikhiM. H. (2023). Improving the administrative performance of secondary school principals in Hafr Al-Batin governorate in light of the requirements of artificial intelligence.J. Arab. Peninsula Cent. Educ. Humanity Res.266–95. 10.56793/pcra2213174
12
Al-RoumiA. A.Al-QahtaniH. M. (2023). The skills of utilizing artificial intelligence applications in improving the learning outcomes among high school students in light of international experiences.J. Educ. Stud.33251–358.
13
Al-SaadiS. A. (2022). The availability of technical competencies for employing AI in education among computer teachers in Makkah.J. Fac. Educ. Mansoura Univ.117206–255. 10.21608/maed.2022.228282
14
Al-SuhaimA. B. (2023). The reality of technological competencies for producing AI applications among computer supervisors and teachers.J. Arts Lit. Humanit. Soc. Sci.277–297. 10.33193/JALHSS.96.2023.918
15
AsiriM. A. (2022). Electronic educational platforms and their role in developing the teachers’ digital competencies: Madrasati platform as a model.Arab J. Spec. Educ.6437–464. 10.21608/ejev.2022.233131
16
Castro-SchezJ.Glez-MorcilloC.AlbusacJ.VallejoD. (2021). An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning.Inf. Sci.544446–468. 10.1016/j.ins.2020.08.079
17
ChakrounB.DaelmanK. (2018). “Lifelong learning examined from a rights-based perspective: The road not yet travelled,” in The Right to Education Movements and Policies: Promises and Realities, edsTerwayA.GerhardP.DioA. P. D.HendersonA. (Geneva: NORRAG).
18
ChassignolM.KhoroshavinA.KlimovaA.BilyatdinovaA. (2018). Artificial intelligence trends in education: A narrative overview.Procedia Comput. Sci.13616–24. 10.1016/j.procs.2018.08.23
19
CromptonH.BurkeD. (2023). Artificial intelligence in higher education: The state of the field.Int. J. Educ. Technol. High. Educ.20:22. 10.1186/s41239-023-00392-8
20
DaiW.LinJ.JinH.LiT.TsaiY. S.GaševiæD.et al. (2023). “Can large language models provide feedback to students? A case study on ChatGPT,” in Proceedings of the 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) (Orem, UT: IEEE), 323–325. 10.1109/ICALT58122.2023.00100
21
DirarK. (2019). Ethics of artificial intelligence and robot: Analytical studies.Int. J. Libr. Inf. Sci.6237–371.
22
GuanC.MouJ.JiangZ. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis.Int. J. Innov. Stud.4134–147. 10.1016/j.ijis.2020.09.001
23
HolmesW.BialikM.FadelC. (2022). Artificial Intelligence in Education: Promise and Implications for Teaching and Learning.Boston, MA: Center for Curriculum Redesign.
24
JoshiV.PanigrahiA. (2020). Use of artificial intelligence in education.SSRN Electron. J.5564–67. 10.18162/fp.2019.a166
25
KarsentiT. (2019). Artificial intelligence in education: The urgent need to prepare teachers for tomorrow’s schools.Form. Prof.27112–116. 10.18162/fp.2019.a166
26
LuckinR.HolmesW.GriffithsM.ForcierL. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Available online at: http://discovery.ucl.ac.uk/1475756(accessed October 25, 2024).
27
MiaoF.ShiohiraK.LaoN. (2024). AI Competency Framework for Students.Paris: UNESCO.
28
MuhammadY. M.AhmedT.GeorgiosL.RonghuaiH.MohammedS. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): A roadmap to a future research agenda.Smart Learn. Environ.11:59. 10.1186/s40561-024-00350-5
29
OlayanR. M.Al-DabbasM. A. (2003). Communication Tools and Educational Technology.Amman: Dar Safa for Publishing.
30
ParsonsS. A.VaughnM.ScalesR. Q.GallagherM. A.ParsonsA. W.DavisS. G.et al. (2018). Teachers’ instructional adaptations: A research synthesis.Rev. Educ. Res.88205–242. 10.3102/0034654317743198
31
RamadanE. G. (2021). A proposed training program to employ artificial intelligence applications to the educational process in the light of training needs of secondary school teachers in the Kingdom of Saudi Arabia.Alger. J. Educ. Ment. Health15118–167.
32
RollI.WylieR. (2016). Evolution and revolution in artificial intelligence in education.Int. J. Artif. Intell. Educ.26582–599. 10.1007/s40593-016-0110-3
33
RussellS.NorvigP. (2020). Artificial Intelligence: A Modern Approach, 4th Edn. Harlow: Pearson.
34
SadikuM. N. O.AshaoluT. J.Ajayi-MajebiA.MusaS. M. (2021). Artificial intelligence in education.Int. J. Sci. Adv.25–11. 10.51542/ijscia.v2i1.4
35
SaleemR. Y. (2017). The reality of employing the secondary stage female teachers for educational technology innovations in light of the total quality standards in Jeddah.J. Arab Stud. Educ. Psychol.90225–277. 10.12816/0041978
36
TsankovN.DamyanovI. (2019). The digital competence of future teachers: Self-assessment in the context of their development.Int. J. Interact. Mob. Technol.134–18. 10.3991/ijim.v13i12.10990
37
UNESCO (2019). Beijing Consensus on Artificial Intelligence and Education: Outcome document of the International Conference on Artificial Intelligence and Education: Planning Education in the AI Era – Lead the Leap, held in Beijing, China, from 16–18 May 2019.Paris: UNESCO.
38
Zawacki-RichterO.MarínV. I.BondM.GouverneurF. (2019). Systematic review of research on artificial intelligence applications in higher education.Int. J. Educ. Technol. High. Educ.16:39. 10.1186/s41239-019-0171-0
Summary
Keywords
applied competencies, artificial intelligence, cognitive competencies, educational technology, performance-based competencies
Citation
AL-Eidan AA (2026) The impact of educational technology courses on developing artificial intelligence (AI) competencies among students at the college of basic education in Kuwait. Front. Educ. 11:1756986. doi: 10.3389/feduc.2026.1756986
Received
29 November 2025
Revised
01 February 2026
Accepted
26 February 2026
Published
23 April 2026
Volume
11 - 2026
Edited by
Sergio Ruiz-Viruel, University of Malaga, Spain
Reviewed by
Eko Risdianto, University of Bengkulu, Indonesia
Desy Kumala Sari, Universitas Musamus Merauke, Indonesia
Updates
Copyright
© 2026 AL-Eidan.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ayda Abdulkareem AL-Eidan, aa.aleidan@paaet.edu.kw
Disclaimer
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