Individuals’ awareness of GenAI tools involves multiple dimensions. A foundational layer of awareness of GenAI is understanding the basic concepts of this technology, such as deep learning, neural networks, and machine learning techniques (Foster, 2019) that represent how GenAI tools create new content through learning patterns from existing data. Besides understanding the basic concepts of GenAI, individuals’ awareness of GenAI involves understanding how GenAI models operate. A prominent example of GenAI is ChatGPT, which operates based on deep learning, particularly transformer neural networks, to create bodies of writing that are like human writing. The process includes data preparation and transformer-based model training (Roumeliotis and Tselikas, 2023; Gupta et al., 2024). The other phases are input processing; context understanding; text generation; and sequence generation; decoding, and output processing (Roumeliotis and Tselikas, 2023; Gupta et al., 2024).

Educators’ awareness includes knowing GenAI’s teaching and research applications. In higher education settings, awareness includes recognizing GenAI’s potential for content development, including its ability to assist in curriculum design and instructional material creation (Michel-Villarreal et al., 2023; Chan and Colloton, 2024). In addition, educators’ awareness also includes understanding the potential research-related functionalities of GenAI (Aithal and Aithal, 2023; Alshami et al., 2023). However, awareness of GenAI tools should involve understanding ethical implications and challenges associated with these tools in educational settings, such as issues related to bias and fairness (Eckert, 2024), academic integrity (Michel-Villarreal et al., 2023), privacy and data security (Gupta et al., 2023), transparency of use (Camacho-Zuñiga et al., 2024), and content quality.

Educators’ awareness of GenAI tools involves their beliefs about the significant potential impact of these tools on the teaching and learning process. For example, educators’ awareness of GenAI includes understanding its potential to enhance student engagement and personalize learning experiences. This awareness extends to recognizing how GenAI tools can be applied in educational settings to automate content creation, promote and streamline administrative tasks (Michel-Villarreal et al., 2023; Chan and Colloton, 2024). In the educational environment, discussions among educators about the implications and applications of GenAI in the educational setting represent an indication of their awareness of these tools, where such conversations would facilitate knowledge sharing, identify opportunities, and promote critical thinking about the GenAI’s capabilities and limitations in the educational settings (Al-Mughairi and Bhaskar, 2024). Educators’ confidence in using GenAI tools reflects their awareness of the technology, when they believe that they can master using and employing GenAI tools, they are more likely to invest time and effort into learning about them (Wang et al., 2021). In addition, high self-efficacy regarding using GenAI might increase their engagement with such technology, reduce resistance to using this technology, encourage continuous learning about this technology, and enable taking the right decision regarding employing GenAI tools in their educational practice (Wang et al., 2021).

There are several possible benefits of using GenAI tools in education. Using GenAI tools in education would increase student engagement in different ways (Meli et al., 2024). For example, GenAI can facilitate educational role-playing scenarios (Salinas-Navarro et al., 2024). In addition, GenAI tools can be used to increase student engagement through facilitating virtual labs (Borah et al., 2024), adaptive quizzing (Klein and Kovacs, 2024), scenario-based learning (Bai et al., 2024), and engaging discussions. Furthermore, GenAI tools would help enable personalized learning experiences for students (Bdoor and Habes, 2024; Graefen and Fazal, 2024; Leelavathi and Surendhranatha, 2024). Furthermore, GenAI tools have been used to develop 21st-century skills for university students that are necessary for the future workforce (Chiu, 2023).

GenAI tools can save faculty time by handling grading, feedback, and administrative tasks (Michel-Villarreal et al., 2023). In addition, GenAI tools can provide faculty members with valuable help in conducting research, where they can be used in analyzing qualitative and quantitative data and writing literature reviews (Aithal and Aithal, 2023). Furthermore, in the teaching process, GenAI tools can help faculty members design and deliver curriculum and educational content (Adeshola and Adepoju, 2023; Lo, 2023). GenAI tools support the design and delivery of blended and online courses (Nikolopoulou, 2024).

Using GenAI tools in higher education environments might raise several concerns. One concern is the quality and accuracy of GenAI content, which may be inconsistent, biased, or outdated (Pathak, 2023). Another possible concern is the financial costs of implementing and maintaining GenAI tools in higher education systems.

Faculty members have a common concern regarding the implementation of GenAI in education that the students may become overly reliant on these tools for their assignments and learning due to several factors, i.e., ease of accessing these tools, high quality of generated content from these tools, and students’ lack of confidence (Sharma and Yadav, 2022). Students’ over-reliance on these tools might cause academic problems such as hindering skills development, reducing creativity, and decreasing critical thinking (Yuan et al., 2024). Students’ reliance on GenAI tools may compromise academic integrity by enabling plagiarism, cheating, and data fabrication in research, writing, exams, and group work (Mahmud, 2024). Furthermore, using GenAI tools in educational settings might cause over-dependence issues among educators too (Holmes, 2023). In addition, faculty members’ reliance on GenAI tools might undermine traditional teaching practices and the roles of educators. In addition, the widespread use of different forms of electronic learning driven by GenAI in educational institutions might raise concerns about job security for educators (Meniado, 2023).

Building on the preceding discussion of educators’ awareness and perceptions of GenAI tools, this section illustrates how that awareness is translated into practical applications within higher education. It presents specific examples of how faculty members utilize GenAI technologies to enhance teaching, students’ learning, and research activities. Faculty members can use GenAI tools to create various types of educational content (Lo, 2023). Furthermore, GenAI tools can help educators develop curriculum materials (Zhang and Tur, 2024).

GenAI tools can facilitate discussions among students and between students and their instructors (Lin et al., 2024). Furthermore, GenAI tools can be incorporated into lectures to help answer students’ questions and provide additional explanations on complex topics (Labadze et al., 2023). In the context of teaching, faculty members can use GenAI tools to facilitate academic joint projects with other colleagues (Al-Zahrani, 2023).

Beyond in-class support, GenAI tools can also aid student learning outside the classroom. For instance, instructors can design assignments that encourage students to use GenAI tools to get through these assignments (McGuire et al., 2024). In addition, students can use GenAI tools to support their understanding by clarifying concepts (Karthikeyan, 2023). Students can use GenAI tools to practice problems with instant feedback and to support their writing (Karthikeyan, 2023). GenAI tools play an increasing role in evaluating the process of students’ learning. For instance, educators can employ GenAI tools to provide feedback on students’ work (Lo, 2023).

Educators can also use GenAI writing tools in their research activities. GenAI writing tools can for educators in their research activities (Alshami et al., 2023; Aithal and Aithal, 2023). GenAI writing tools can help educators draft professional emails and generate administrative documents (Michel-Villarreal et al., 2023; Chan and Colloton, 2024).

The current research has the following research questions that guide the investigation of Kuwaiti university faculty members’ perspective on GenAI tools in terms of their awareness, perceived benefits, perceived associated concerns, and use of these tools in higher education:

The participants in the current study were a group of faculty members from two universities in Kuwait. Based on their gender, the participants in the current study demonstrate a balanced distribution. The representative sample of males and females helps examine gender-related differences in faculty members’ perspectives on GenAI tools in higher education.

The participants’ ages varied, with most between 40 and 57 and fewer in younger or older age groups. Like the broad age range, participants varied in their years of experience in higher education, including both early-career and late-career faculty. Participants varied in academic rank, with representation across lecturers, assistants, associates, and full professors. This distribution reflects a balanced mix of seniority, aligning with participants’ age and experience levels.

Finally, regarding the faculty of the participants, about half (49%; n = 50) were from the College of Education, while the rest came from other faculties. Such distribution allows us to focus on educational fields while still maintaining some cross-disciplinary perspectives. The large number of participants from the College of Education might be because many of them know the researchers, which motivates them to complete the questionnaire. Table 1 shows a descriptive summary of participants’ demographic data.

The data collection tool was a questionnaire. The questionnaire consisted of five sections. The first section consisted of questions regarding participants’ gender, age, academic rank, faculty, and experience in academic work. The second section consists of items that examine faculty members’ awareness of GenAI tools and their applications in education. The number of items in this section is 8. These items were adopted from examined literature (Foster, 2019; Roumeliotis and Tselikas, 2023; Gupta et al., 2024; Michel-Villarreal et al., 2023; Chan and Colloton, 2024; Aithal and Aithal, 2023; Alshami et al., 2023; Eckert, 2024; Gupta et al., 2023; Al-Mughairi and Bhaskar, 2024; Wang et al., 2021). The third section consists of items that examine faculty members’ perceptions of the benefits of applying GenAI tools in education. The number of items in this section is 8. These items were adopted from examined literature (Meli et al., 2024; Salinas-Navarro et al., 2024; Borah et al., 2024; Klein and Kovacs, 2024; Bai et al., 2024; Bdoor and Habes, 2024; Graefen and Fazal, 2024; Leelavathi and Surendhranatha, 2024; Chiu, 2023; Michel-Villarreal et al., 2023; Aithal and Aithal, 2023; Adeshola and Adepoju, 2023; Lo, 2023; Nikolopoulou, 2024). The fourth section consists of items that examine faculty members’ concerns regarding GenAI tools and their applications in education. The number of items in this section is 8. These items were adopted from examined literature (Pathak, 2023; Sharma and Yadav, 2022; Yuan et al., 2024; Holmes, 2023; Meniado, 2023). The fifth section consists of items that examine faculty members’ level of utilizing GenAI tools in education. The number of items in this section is 9. These items were adopted from examined literature (Lo, 2023; Zhang and Tur, 2024; Lin et al., 2024; Labadze et al., 2023; Al-Zahrani, 2023; McGuire et al., 2024; Karthikeyan, 2023; Alshami et al., 2023; Aithal and Aithal, 2023; Michel-Villarreal et al., 2023; Chan and Colloton, 2024).

In the second, third, fourth, and fifth sections, the participants were asked to select one response option from a five-point Likert scale: “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.” Each level of agreement corresponded to a specific numerical value ranging from 1 to 5, respectively.

The face validity of the questionnaire was verified by presenting its first draft to a panel of experts with various expertise in the field of education. Cronbach’s Alpha was used to indicate the reliability of each scale in the questionnaire instrument. The Cronbach’s Alpha values ranged from 0.88 to 0.92, and the values of Cronbach’s Alpha are greater than 0.8, indicating “good” internal consistency of the items in the scales (Aron et al., 2005). This suggests that the items within each scale were highly correlated and likely measured the same construct. A summary of the reliability analysis is provided in Table 2.

The study was conducted in the summer semester of the 2024/2025 academic year. The participants were invited to participate in the current study using online invitation letters and consent forms sent to faculty members via electronic groups. Participation in the study was entirely voluntary. To protect the confidentiality and anonymity of participants, they were informed that their responses would be anonymized, with no identifying information connected to their data. The questionnaire was designed to avoid collecting any information that could reveal participants’ identities. Before participating, respondents were presented with an informed consent form outlining the study’s purpose, how the data would be used, and assurances of confidentiality. The data was securely stored on password-protected servers, accessible only to the researcher. Clear instructions were provided to participants on completing the questionnaire to minimize any potential misunderstandings, and all questions were made mandatory to ensure complete responses.

Descriptive analyses were used to answer the first four research questions regarding faculty members’ awareness, perceived benefits, perceived concerns, and use of GenAI tools in education. The descriptive analysis involves determining the mean, standard deviation, and level for each question in the questionnaire and the mean, standard deviation, and level for each dimension.

To qualitatively describe faculty members’ responses to the questionnaire’s items, the current research employs the following criteria to categorize responses on a five-point Likert scale: a mean of 3.67 or higher is classified as “High,” a mean between 2.34 and 3.66 is considered “Moderate,” and a mean below 2.34 is designated as “Low.” This classification method has been adopted from prior studies.

Different forms of statistical tests were employed to answer the fifth question, including t-tests and analysis of variance (ANOVA) tests. Independent sample t-tests were conducted to examine the differences in faculty members’ awareness, perceived benefits, perceived concerns, and utilization of GenAI tools in education based on their gender. Analysis of variance (ANOVA) was conducted to examine variations in faculty members’ awareness, perceived benefits, perceived concerns, and utilization of GenAI tools in education based on their academic rank.

The findings regarding faculty members’ awareness of GenAI tools and their applications in education suggest that they had a moderate level of awareness of these applications. Table 3 shows the means, standard deviations, and levels of faculty members’ responses regarding their awareness of GenAI tools.

The faculty members’ overall awareness of GenAI tools and their applications in education was moderate (M = 3.39, SD = 0.90). There is a strong agreement among faculty members about the current and future impact of GenAI tools on education, as reflected in their positive response to the first statement “I believe GenAI has the potential to impact education significantly” (M = 4.17, SD = 1.03). In addition, faculty members had a high level of awareness of ethical implications and challenges associated with the use of GenAI tools in education reflected in the high mean score of their response to item 2, “I am aware of the ethical implications and challenges associated with GenAI such as bias, academic integrity, privacy and data security, and content quality.” (M = 3.69, SD = 1.30). Similarly, the participants had positive feelings regarding their ability to learn and use GenAI tools, reflected in the moderate mean score of their response to item 3, “I feel confident in my ability to learn about and use GenAI technologies” (M = 3.62, SD = 1.23).

Faculty members disagreed that they know how GenAI models like ChatGPT operate; they responded least positively to item 8, “I understand how GenAI models like ChatGPT operate, starting from the training phase to the generation phase.” (M = 2.97, SD = 1.19). Similarly, faculty members disagreed that they have the knowledge about the technical concepts associated with GenAI tools, e.g., deep learning, neural networks, and machine learning, reflected in their close to neutral response to item 6, “I am familiar with basic concepts of GenAI, such as deep learning, neural networks, and machine learning.” (M = 3.08, SD = 1.12). Another item with a neutral score was item 7, “I have used GenAI tools, such as ChatGPT, in my teaching or research.” (M = 3.00, SD = 1.40).

Faculty members’ responses to the awareness scale showed that they were in the process of getting familiar with and building knowledge about the GenAI tools and their applications in education by looking for more information about GenAI, where the participants agreed to some extent on item 4, “I actively seek out new information and resources to stay updated on advancements in GenAI.” (M = 3.49, SD = 1.23). However, they did not similarly agree that they engage in discussion with their colleagues to know more about GenAI tools and their applications in education, where the participants’ responses to item 5 were close to neutral, “I engage in discussions with colleagues about the implications and applications of GenAI.” (M = 3.12, SD = 1.20).

Faculty members’ moderate awareness of GenAI tools and their applications in education might be attributed to several reasons, including a lack of formal training about them and their applications in higher education and limited actual experiences using these tools. In addition, the participants appear to be influenced by mass media propaganda concerning the ethical implications of AI.

Faculty members’ responses to the first item in the awareness scale suggest that the effect of GenAI tools on education is inevitable. The overall moderate score suggests the faculty members were in a transitional phase and gradually increased their familiarity with these tools. The findings suggest the participants were optimistic about GenAI tools’ transformative potential in education. In addition, they were optimistic about the future impact of GenAI tools on higher education. However, the participants’ knowledge of the technical aspects of GenAI is still limited. Even though the participants reported high self-efficacy when learning about the GenAI tools.

The study’s results concerning the moderate level of awareness of GenAI tools and their applications in education indicate the need to improve faculty members’ awareness and their application in higher education. For instance, faculty members’ high ethical awareness of these tools should be reinforced through discussions on issues related to GenAI such as bias, privacy, and integrity, both at the institutional and national levels. The results showed a critical gap between faculty members’ recognition of GenAI’s transformative potential and their understanding of its operational foundations. This gap suggests that without deep understanding, faculty members might underutilize these tools or misguide students in their responsible use. Furthermore, at the institutional level, the findings suggest university administrators should provide faculty members with structured awareness programs about focus on technical fluency. These structured awareness programs can be in the form of workshops, sandbox environments, and guided experimentation.

These findings aligned with previous studies that showed that some faculty members were familiar with the use of GenAI tools reflected in their experience with these tools. For instance, (Malik’s 92,024) study found that half of the participants were active users of GenAI tools like ChatGPT, and Amani et al.’s (2023) study showed that 64% of faculty and staff had an account and had used GenAI tools like ChatGPT. However, there is a lack of similar studies that directly examined faculty members’ awareness of GenAI tools and the various dimensions of awareness of such tools.

The findings regarding faculty members’ perceptions of the benefits of applying GenAI tools in education suggest that they had highly positive perceptions of these benefits. Table 4 shows the means, standard deviations, and levels of faculty members’ responses regarding their perceptions of the benefits of applying GenAI tools.

The faculty members’ overall perceptions of the benefits of applying GenAI tools in educational practices were at a high level (M = 3.77, SD = 0.90), with most of the benefits rated at a high level. Faculty members believed that one of the main benefits of GenAI is to help in administrative tasks; they were found to have responded most positively to item 1, “GenAI can help reduce the time spent on administrative tasks, allowing more focus on teaching and research” (M = 3.95, SD = 1.10). Another closely rated benefit was the one related to the roles of GenAI tools in curriculum development reflected in the high mean score of their response to item 2, “The integration of GenAI fosters innovation in curriculum design and delivery.” (M = 3.94, SD = 1.02). The benefits of GenAI tools in facilitating research and online learning were rated equally high by the faculty members, reflected in the mean score of their response to item 3, “GenAI provides valuable support for faculty research activities, such as data analysis and literature reviews.” (M = 3.92, SD = 1.05) and item 4, “GenAI tools are particularly beneficial for enhancing the quality and delivery of online learning.” (M = 3.88, SD = 1.09).

The faculty members responded less enthusiastically regarding the benefits of GenAI tools in education when it comes to the use of these tools with students. The benefits of the use of GenAI tools in classroom and to enhance students’ learning were rated almost equally by the faculty members, reflected in the high-level mean score of their response to item 5, “The use of GenAI in the classroom increases student engagement.” (M = 3.67, SD = 1.06) and the moderate level mean score of their response to item 6 “Using GenAI in higher education better prepares students for the demands of the future workforce.” (M = 3.66, SD = 1.06).

Faculty members’ perceptions of the benefit related to the role of GenAI tools’ potential to tailor educational experiences to each student’s needs did not match the other level benefits; they responded least positively to item 8, “GenAI enables more personalized learning experiences for students” (M = 3.52, SD = 1.10). Similarly, faculty members moderate level of perception of the benefit related to the role of GenAI tools to prepare students for the demands of the future workforce reflected in their response to item 7, “Using GenAI in higher education better prepares students for the demands of the future workforce.” (M = 3.64, SD = 1.20).

Faculty members’ high overall perception of the benefits of GenAI tools might be attributed to several reasons that include saving their time and efforts, particularly in reducing administrative tasks and paperwork that might distract them from their core academic responsibilities, i.e., teaching and research. In addition, conducting research requires a range of tasks that vary in intellectual demand; therefore, GenAI tools can help in accomplishing some low-order research tasks, e.g., conducting literature reviews, allowing faculty members to focus on higher-order research tasks. Given the increasing demand for online education worldwide, particularly in the last few years with the appearance of the COVID-19 pandemic (Ashour, 2024), faculty members believe GenAI tools can help facilitate high-quality online education. In addition, faculty members usually seek to enhance student engagement (Barkley and Major, 2020), and they believe that GenAI tools can assist in creating more dynamic learning materials. However, the faculty was more cautious about GenAI tools’ direct benefits for students. A possible explanation might be their belief that while they can responsibly use these tools, students might misuse them. This could lead to over-reliance on GenAI tools, affecting academic integrity and hindering students’ learning.

The high overall score suggests that faculty members are confident in GenAI tools’ benefits for accomplishing administrative tasks, developing innovation in curriculum design, facilitating research, and improving online learning.

Faculty members’ cautiousness about the direct benefits for students suggests that they still hesitate to fully integrate these tools into students’ educational activities. These results indicate the need to improve GenAI tools’ applications in higher education by offering training for faculty members to improve administrative efficiency and curriculum design. In addition, training should address the role of GenAI tools in supporting research and enhancing online learning. Furthermore, students should be offered literacy courses about the proper uses of GenAI tools. Institutions should set GenAI use guidelines. Therefore, higher education must also address the perceived boundary between GenAI as a professional tool and a student learning aid by framing GenAI as a replacement and a scaffold for student-centered learning. In addition, policies should clarify the responsible use of GenAI.

These findings regarding the highly perceived benefits of GenAI tools in accomplishing administrative tasks in traditional and online education and helping with research are aligned with Malik’s (2024) study.

The findings regarding faculty members’ perceptions of the concerns associated with applying GenAI tools in education suggest they had a moderate level of concerns about applying these tools. Table 5 shows the means, standard deviations, and levels of faculty members’ responses regarding their concerns regarding the applications of GenAI tools in education.

The faculty members’ overall concerns regarding the applications of GenAI tools in education were moderate (M = 3.549, SD = 0.84), with four of the concerns rated high. Faculty members believed that one of the main concerns of GenAI is related to the effect of GenAI tools on academic integrity and the potential for plagiarism, they were found to have responded most positively to item 1 “The use of GenAI raises concerns about academic integrity and the potential for plagiarism.” (M = 3.99, SD = 1.16). Another closely rated concern was the one related to students’ overly reliant on GenAI tools and overdependence on technology reflected in the high mean score of their response to item 2 “I am concerned that students may become overly reliant on GenAI tools for their assignments and learning.” (M = 3.85, SD = 1.22). Furthermore, item 3, “The increasing use of GenAI in education could lead to an over-dependence on technology.” (M = 3.81, SD = 1.14). Another concern rated at a high level was related to the ethical implications of GenAI in higher education, presented in item 4, “I am concerned about the ethical implications of using GenAI in higher education.” (M = 3.74, SD = 1.08).

Faculty members’ responses to the scale of the concerns showed that had a moderate level of concerns regarding the effect of GenAI tools on traditional teaching and the role of educators as well as the threat of GenAI tools on their jobs as educators, where the participants moderately agreed on item 5, “I worry that GenAI might undermine traditional teaching practices and the role of educators.” (M = 3.23, SD = 1.15) and item 6, “I worry that the widespread adoption of GenAI could threaten job security for faculty members.” (M = 3.17, SD = 1.20) Faculty members had close to neutral concerns regarding the quality and accuracy of content generated by AI tools; they responded least positively to item 8, “I am concerned about the quality and accuracy of content generated by AI tools like ChatGPT” (M = 3.07, SD = 1.12). Similarly, faculty members had close to neutral concerns about the cost of using GenAI in education, reflected in their response to item 7, “I am concerned about the financial costs associated with implementing and maintaining GenAI tools in higher education.” (M = 3.10, SD = 1.04).

It is easy to generate content using GenAI tools (Kalla et al., 2023), the ease of generating content using GenAI tools might be one of the reasons behind the faculty members’ concerns about the effect of GenAI tools on academic integrity and plagiarism as well as the ethical implications associated with the use of these tools. In addition, the ease of generating content by GenAI tools might contribute to faculty members’ concerns about students’ reliance on GenAI tools and technology overdependencies. However, faculty members’ concerns about the impact of generative AI on their roles as educators, job security, and the associated costs were less intense than their concerns about these tools’ effects on their students. Faculty members viewed GenAI tools as supplementary tools rather than a replacement for human educators.

The moderate overall score suggests that faculty members are transitioning to accepting GenAI tools and their application in higher education. The findings suggest that faculty members were more concerned about students’ use of GenAI tools, particularly in terms of academic integrity, plagiarism, and over-reliance on technology, rather than the personal impact of GenAI tools on their roles, job security, or traditional teaching practices.

Findings stress the need for clear GenAI rules in student work. Monitoring is needed to ensure integrity and prevent GenAI-related plagiarism. In addition, concerns about students’ overreliance on GenAI tools, as well as the impact of GenAI on traditional teaching, highlight the need to design instruction that balance the use of technology, particularly GenAI, with the promotion of self-regulation and student agency. In addition, the finding suggests the need for training both students and faculty on how to use and critically assess the content of GenAI tools ethically. In addition, the findings necessitate the creation of AI governance frameworks that include clear policies on plagiarism, usage boundaries, and AI-assisted assessment.

The findings regarding faculty members’ concern about issues of academic integrity and plagiarism associated with the use of GenAI in education aligned with the findings of previous studies (Kiryakova and Angelova, 2023; Malik, 2024) which found that faculty members had worries about the unethical use of GenAI tools and possible plagiarism and academic dishonesty associated with the use of these tools. In addition, the findings regarding faculty members’ concern about over-reliance on technology aligned with the findings of a similar study (Al-Mughairi and Bhaskar, 2024). The findings of the current study are supported by Yusuf et al. (2024), who highlighted the significant role of both cultural and institutional contexts in shaping perceptions of GenAI in higher education. Their cross-cultural analysis demonstrated that participants from cultures with high uncertainty avoidance and strong long-term orientation were likelier to associate GenAI use with academic dishonesty and support stricter regulatory measures. Similarly, our study found that faculty members’ concerns—particularly those related to plagiarism, over-reliance, and ethical misuse—may be influenced not only by personal values but also by broader cultural expectations and institutional norms that emphasize academic integrity and traditional pedagogical practices. Furthermore, institutions that structured training may reinforce ambiguity and apprehension among faculty regarding GenAI tools. These findings collectively emphasize the need for culturally responsive and institutionally supported frameworks that provide clear policies, ethical guidelines, and targeted training to ensure the responsible integration of GenAI in educational settings.

Regarding the faculty members’ responses to the items indicating their level of utilization of GenAI tools, the results showed that their level of utilization of GenAI was lower than their level of awareness and perceived benefits of these tools. Overall, they demonstrated a moderate level of utilization (M = 2.86, SD = 1.00). Table 6 shows the means, standard deviations, and levels of faculty members’ responses regarding their level of utilization of GenAI tools.

The results indicated that participants’ level of agreement regarding the extent of generative AI usage, as presented in the questionnaire, ranged from “neutral” to “agree” across three items: for accomplishing research activities (M = 3.15, SD = 1.28), and developing and updating educational materials such as new curriculum materials (M = 3.11, SD = 1.26). However, for the remaining items, participants’ level of agreement ranged from “disagree” to “neutral.” These items were related to the use of GenAI tools providing additional support for students outside of class (M = 2.99, SD = 1.19), completing administrative tasks (M = 2.92, SD = 1.26), providing feedback and grading student work (M = 2.87, SD = 1.26), teaching activities (M = 2.62, SD = 1.20), designing assignments that involve the use of generative AI tools (M = 2.61, SD = 1.32), and facilitating in class discussion (M = 2.58, SD = 1.17).

A possible explanation of the indecision or moderate level of the utilization of GenAI tools is that the popularity of GenAI tools and their applications in education are still in their early stages, as ongoing debates persist regarding their formal use in education (Strzelecki, 2023). Based on the adoption models, adopting new technologies like GenAI tools can take time (Rogers, 1995) and go through multiple stages such as knowledge, persuasion, decision, implementation, and confirmation (Rogers, 1995). Furthermore, need institutional support and facilitating conditions (Venkatesh et al., 2016) as educators gradually build an understanding of these tools. In addition, the findings regarding the faculty members’ perceptions of the benefits and concerns of GenAI tools contribute to the explanation of the variation in the utilization level of GenAI tools for different purposes, where faculty members were concerned about students’ use of tools.

The findings suggest that faculty members’ use of GenAI tools in education is still in its early stages. Many faculty members can be described as in the “awareness-to-practice” gap, unsure of how to operationalize GenAI in their specific teaching contexts. Faculty members appear open to using GenAI for specific purposes, such as conducting research and developing educational materials, but remain cautious about its use in direct teaching and assessment. This caution likely stems from concerns about potential negative impacts on academic integrity and student over-reliance on GenAI tools.

These findings suggest that while there is some acceptance of GenAI tools in education, institutions should offer targeted support to help faculty members enhance their use of these tools, considering their perceived benefits and concerns. This support could include offering training, providing resources, and developing clear policies and guidelines to govern the effective use of generative AI tools by students and faculty members. In addition, higher education institutions can provide templates for classroom use and communities of practice that allow peer support and experimentation.

The findings regarding the level of utilization of GenAI tools aligned with some research studies that found that faculty members moderately use GenAI tools like ChatGPT to assist in research activities (Malik, 2024) to get through some streamlined repetitive tasks (Goli-Cruz, 2024), while they contradicted other research studies that found faculty members moderately use GenAI tools like ChatGPT teaching and supporting students’ tasks (Amani et al., 2023;)

The differences in participants’ perspectives on GenAI regarding the level of awareness, perceived benefits, perceived concerns, and utilization, and some of their demographic characteristics were examined through t-tests and analysis of variance (ANOVA) tests. Demographic characteristics included gender and academic rank.

Based on the findings of this study, several recommendations can be offered for policy and practice concerning the use of GenAI tools or similar technologies in the field of higher education. These recommendations relate directly to strategies that policymakers and practitioners can implement to ensure the successful use and integration of GenAI tools, and the faculty members and their students get the benefit of the use of these tools in the educational practice. The recommendations concerning the use and integration of GenAI tools in educational practice relate to four categories: awareness of faculty members of GenAI tools, their perceived benefits of GenAI tools, their perceived concerns of GenAI tools, and the actual use of GenAI tools in educational practice.

The effect of GenAI tools on education is inevitable, as is evident in faculty members’ responses to the first item in the awareness scale. Faculty members showed confidence to learn about GenAI tools and a willingness to know more about these tools. Therefore, there is a need to enhance their awareness in some areas about the GenAI tools and their application in educational practice, i.e., GenAI tools’ working mechanism, the possible uses of the tools in educational practice, and technical concepts associated with GenAI tools. In addition, there is a need to reinforce their awareness of some common issues regarding these tools, i.e., the effect of GenAI tools on educational practice and associated ethical issues related to GenAI tools. Therefore, policymakers and practitioners should use various ways to increase educators’ awareness about specific GenAI tools and their applications in educational practice. The literature showed some common ways to increase educators’ awareness of specific issues; these ways include training programs (Bayar, 2014), social media platforms (La Rocca and Boccia Artieri, 2022), and involving individuals in policy discussions. In addition, peer learning has proven to be effective in raising educators’ awareness (Topping et al., 2017), Therefore, policymakers and practitioners should facilitate discussion and exchange experiences among faculty members about GenAI tools and their applications in the educational practice of on the institutional, national, and international levels.

Regarding the faculty members’ perceptions of the benefits of GenAI tools, the higher education institutions can take advantage of their high agreement on the benefits of GenAI tools in accomplishing administrative tasks, facilitating research, developing innovation in curriculum design, and improving online learning. This suggests that institutions of higher education could integrate GenAI tools across departments to handle administrative tasks, e.g., grading, providing access to GenAI tools that facilitate research activities, providing training on using GenAI tools for curriculum innovation, and facilitating the delivery of online courses with the aid of GenAI tools. However, the lower perceived benefit of the GenAI tools in enhancing students’ education requires institutions of higher education to issue policies and provide training for faculty members on how to use GenAI tools to enhance students’ learning and to cater to diverse student needs.

Considering faculty members’ high-level concerns in some areas related to GenAI tools and their applications in education, this suggests that institutions of higher education should consider these concerns by establishing guidelines that set up the rules regarding students’ use of GenAI in their academic work, establishing monitoring systems to address the issue of the effect of GenAI tools on academic integrity and the potential plagiarism associated with the use of these tools; provide appropriate resources for faculty members that would detect to plagiarism associated with the use of these tools, provide training for faculty members on how to design assignments that ensure balance use of GenAI tools and encourage critical thinking skills. Given faculty members’ moderate level concerns in some areas related to GenAI tools and their applications in education, institutions of higher education should provide training on how to integrate GenAI tools to enhance traditional teaching methods, provide faculty members with free access to GenAI tools, and implement quality control processes related to generated content of GenAI tools.

Given the moderate utilization of GenAI tools by faculty members, it suggests that while there is some acceptance of GenAI tools in education, institutions should offer targeted support to help faculty members enhance their actual use of these tools, considering both their perceived benefits and concerns. This support could take multiple forms, including providing digital platforms that faculty members can use to present best practices for using GenAI tools. In addition, training could be provided for faculty on how to use GenAI tools to design curriculum, conduct research, facilitate students’ assessment, accomplish administrative tasks, design assignments that require students to use GenAI tools ethically, and use GenAI to facilitate in-class discussion. Furthermore, institutions should provide access to advanced GenAI tools that can be used in research and provide formal guidelines and training for students on how to use GenAI tools responsibly. Finally, the significant variations between male and female participants on their awareness, perceived concern, and level of utilization of GenAI tools suggest that educational institutions should provide faculty members with tailored interventions based on gender.

The current study indicates the requirements for educational management in higher education institutions to facilitate the implementation of GenAI tools by establishing clear policies and investing in infrastructure to support the responsible use of GenAI in higher education. Institutions should monitor usage, encourage cross-departmental collaboration, and align GenAI integration with national digital strategies to enhance teaching, research, and administrative efficiency.

The previous recommendations were based on the findings specific to Kuwaiti faculty members, however, several recommended practices such as the ones that are related to enhancing awareness, offering training, addressing ethical concerns, and integrating GenAI into curriculum and administrative work were potentially generalizable to similar higher education contexts, particularly in the Arab countries or other countries that are looking to integrate digital technology in their higher education systems.

Future research studies might be conducted with different study settings, disciplines, research samples, sampling procedures, and data collection methods to gain a comprehensive and deep understanding of faculty members’ perspectives on GenAI tools and their applications in education. The sample size was 102 faculty members, with almost half of the participants from the College of Education. Future research studies should consider a larger sample of faculty members across a range of disciplines to improve the generalizability of the findings. Besides studies investigating faculty members’ perspectives on GenAI tools, there is a need to examine administrators’ perspectives on GenAI tools to provide a more comprehensive view of GenAI acceptance in higher education.

The current study provides several practice recommendations providing professional training programs to promote efficient and ethical use of GenAI tools in educational practice and developing guidelines, policies, and monitoring systems that govern the proper educational uses of GenAI tools in educational practice. Therefore, future studies should be conducted to design and develop these interventions. In addition to providing training to faculty members and developing guidelines of use, the current study recommended facilitating access and integration of GenAI tools for various purposes, e.g., accomplishing administrative tasks, facilitating research, developing innovation in curriculum design, and improving online learning. Therefore, future research should examine how best to integrate GenAI tools into these processes to maximize efficiency without compromising the quality of work.

The current study have some limitations. One of these limitations is related to the representativeness of the participants based on their major. The participants were faculty members from two universities who were invited to participate in the current study using online invitation letters and consent forms that were sent using electronic groups for faculty members. The two institutions were selected due to their accessibility, which facilitated participant recruitment, and data collection. Participation in the study was entirely voluntary. The great majority of participants were from the college of education. Possible explanation might relate to the topic of the research that capture the attention of faculty members from college of education more than other faculty members from other colleges since their majors related to the teaching methods and incorporating new technologies in the educational settings. In addition, faculty members from college of education might complete the questionnaire due to their proximity to the authors and that might cause possible self-selection bias. Such consistency in participants’ majors might limit the generalizability of the findings and limit the capability of examining the difference in the participants’ perceptions based on their major.

In addition to the limitations related to the representativeness of the sample, the exclusive quantitative approach of the current study represents a limitation where methodological triangulation would enrich the findings. Furthermore, the lack of items in the data collection tool that examine most used GenAI tools represent a limitation in the current study.