Exploring Factors Influencing Basic Education Teachers’ Decisions to Use Generative Artificial Intelligence for Instructional Design in Nablus Directorate

Introduction

Generative AI tools (Gen AI) have offered recently new choices for instructional designers and educators all over the world in several domains. Nevertheless, teachers vary in their use of these tools. This creates an inquiry: Why do some teachers use these tools without hesitation, while others do not? Artificial intelligence is creating a massive change in the educational system (Garzon, Patino & Marulanda, 2025). Ertel (2025) defined AI as the capability of machines to build knowledge and make decisions through algorithmic processes that mimic the functioning of human brain. The educational process goes through continuous development and adaptation to technological breakthroughs, with generative artificial intelligence (GenAI) representing the latest trend reshaping teaching methodologies, instructional design, and the whole educational process (Gasevic & Sadiq, 2023; Yan et al., 2024). Salhab & Aboushi (2025) suggest that the integration of GenAI may enhance learning processes among students, allow for various assessment procedures, and facilitate the administrative responsibilities, potentially contributing to better teaching and learning experiences.

Teachers’ decisions to use GenAI are not motivated by the technology’s functions alone, but by a combination of personal beliefs and the school environment. This joint relationship is explained by the Technological Acceptance Model and the Unified Theory of Acceptance and Use of Technology (Davis, 1989; Venkatesh et al., 2003). The Technological Acceptance Model (TAM) suggests that the factors influence someone’s intention to use technology are mainly affected by perceived usefulness and perceived ease of use (Davis, 1989). In the context of Gen AI in education, Shata & Hartley (2025) confirmed that perceived usefulness had a great effect on perceptions and intentions to use Gen AI. Similarly, Al-Abdullatif (2024) highlighted that teachers are more likely to use technologies that improve educational goals and enhance supporting materials.

The Unified Theory of Acceptance and Use of Technology (UTAUT) expand on TAM by adding additional factors such as expected outcomes, ease of use, social influence, and organizational support to explain the actual use of these technologies (Venkatesh et al., 2003). Recent studies applying UTAUT to educational contexts revealed that both performance expectancy, social influence and facilitating conditions were important predictors of teachers’ readiness to use these tools in practice (ALshuridah & Al-Johani, 2025; Watted & watad, 2025).

In addition to TAM and UTAUT factors, teachers’ adoption of GenAI is also influenced by ethical considerations. Concerns about students’ privacy, the reliability of AI-generated content, and excessive dependance on AI tools can affect their willingness to use these tools (Holmes et al., 2019).

Instructional design (ID) is defined as the process teachers use to structure the lessons, set the learning goals, design activities and select appropriate resources; in other words, ID is the road map for the educational system (Wu et al., 2025). GenAI has been proposed to enhance this process by helping teachers to set clear learning goals, designing interactive activities for different learning styles, and help selecting various educational resources in a more effective and faster way (Krushinskaia et al., 2024; Kumar et al., 2024; Wang et al., 2024).

Despite the growing global interest in applying GenAI in education, regional studies highlight several challenges regarding using it, such as the infrastructural limitations, the language issues, the lack of AI-focused training programs, and AI illiteracy among teachers (Al-Abdullatif, 2024; Al-Amri & Al-Abdullatif, 2024). According to the Palestinian context, multiple recent studies point out to an increase interest in applying GenAI in education, with some limitations such as the lack of sufficient technical experience among teachers and the traditional educational system with older technologies (Omar, Shaqour & Khlaif, 2024). Moreover, in the Palestinian basic education system, teachers’ integration of digital tools is hindered by limited infrastructure, inconsistent access, and insufficient professional development, resulting in varying levels of technology readiness across schools (Bsharat, 2025; Hashaykeh, 2025).

In this context, the researchers notice that most of the local studies focus on higher education, leaving a gap in understanding how basic education teachers make decisions regarding using GenAI in instructional design. Obstacles such as lack of AI knowledge among teachers, insufficient training programs, poor infrastructure, and the incompatibility of traditional teaching systems with AI technologies lead to a problem with applying GenAI in our schools (Omar, Shaqour & Khlaif, 2024; Qahman et al., 2023). Therefore, it is essential to explore the factors- whether personal, institutional, or contextual- influencing basic education teachers’ decisions to integrate GenAI into instructional design, particularly in the Nablus directorate, where such studies are scarce.

Based on that, the current study hopes to explore factors influencing basic education teachers’ decisions to use Generative Artificial Intelligence for instructional design in Nablus Directorate, and tries to answer the following questions:

1. What are the factors influencing basic education teachers’ decisions to use generative artificial intelligence for instructional design in Nablus Directorate?

2. Is there any significant difference between basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to (gender, academic qualification, field of specialization, years of teaching experience)?

Hypotheses of the study

1. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to gender (male/ female).

2. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to academic qualification (B.A./ M.A./ Ph.D.).

3. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to the field of specialization (Natural sciences/ Humanities).

4. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to years of teaching experience (0-3 years/ 4-7 years/ more than 8 years).

Literature review

Artificial intelligence is a technological system that mimics the way human brain works with information; how individuals think, understand, learn and respond. Chat GPT has been released in 2022 and created a revolution in various domains, especially in the educational system with its ability to produce huge amounts of content on a variety of topics (Al Darayseh & Mersin, 2025; Grassini, 2023).

Instructional design and the educational system

Today, the educational process goes through rapid and continuous change, which necessitates the use of effective instructional design strategies and models that provide a framework to guide all members of the educational system to enhance both learning and teaching processes (Merrill, 2002; Smith & Regan, 1999). Abuhassna, Adnan & Awae (2024) carried out a systematic review using PRISMA framework to analyze 25 selected documents from 1102 papers regarding the results achieved from the synergy between instructional design models (IDM) and learning theories (behaviorism, constructivism, and cognitivism). The researchers found that the integration of IDM and learning theories leads to an increase in learners’ motivation, the development of creative teaching strategies, and the creation of diverse learning milieus.

Guided by the idea of integrating ID, AI, and CT (Computational Thinking) to improve learning outcomes, Weng et al. (2024) analyzed 18 empirical studies that explored the effect of integrating AI and CT on students’ development at the secondary education level. This systematic review discovered that instruments, projects, and activities related to the involving AI and CT improved the applying of student-centered instructional design, leading to better learning outcomes. The researchers recommended that teachers undergo professional development to possess the competency of using both AI and CT techniques in an appropriate way.

Abu Farhah and Abu Farhah (2024) examined the state of digital transformation and the use of AI in education within Jenin Governorate, focusing on teachers’ readiness, perceptions, and challenges in integrating AI tools into their instructional practice. Using a combination of teacher survey and classroom observations, the study found that although some educators recognized the potential benefits of AI-related technologies, many struggled due to limited access to devices, insufficient training, and varied levels of digital competency.

Instructional design and GenAI

Several studies assured that GenAI appears as a helpful tool in the field of instructional design, particularly with lesson planning and choosing various learning methodologies and materials (Kumar et al., 2024). Luo et al. (2024) employed a questionnaire to survey the perceptions of 70 instructional designers regarding the use of Gen AI in their teaching and interviewed 13 of them using an open-ended interview. The findings revealed that instructional designers are familiar with the use of Gen AI tools, while the interview clarified that Gen AI tools are used by instructional designers to create practice tasks, brainstorm ideas, and enhance cooperation.

Krushinskaia et al. (2024) employed an experimental research design to find out the effect of AI-assisted design platforms on 23 pre-service teachers’ instructional planning skills. The participants were divided into two groups, experimental and control. The experimental group used Gen AI in their lesson planning, while the control group did not. The findings revealed that the use of Gen AI increased creativity, confidence, and effectiveness in developing lesson plans. The researchers recommended using Gen AI for future teachers with appropriate guidance and training in their use.

Al-Abdullatif’s (2024) quantitative study employed structural equation modeling (SEM) to examine the effect of AI literacy in teachers’ acceptance of using Gen AI. The sample of the study consisted of 237 university instructors. The findings indicated that AI literacy among the participants predicted their willingness to integrate Gen AI in their teaching practices. Moreover, the researchers assured that the familiarity of AI tools served as both a mediating and moderating factor, influencing teachers’ acceptance and readiness to use Gen AI tools, accompanied with the availability of institution support and ease of use.

Kozan et al. (2025) studied how instructional designers employed Gen AI tools into their instructional design practice. Through multiple-case qualitative design, with semi-structured interviews, the researchers investigated six instructional designers working in higher education. The results revealed that Gen AI is used during the design and development stages, especially for content building, brainstorming, and creating instructional materials. However, the participants showed some concerns about the accuracy, ethical issues, and institutional readiness. As a result, the researchers stressed the idea of humanizing education, suggesting that Gen AI should work as a collaborative partner not a replacement for human efforts in instructional design.

According to the Palestinian context, Ayyoub et al.’s (2025) study emphasized that AI literacy has become a crucial competency among academic figures. Their study sought to pinpoint the factors that shape university instructors’ literacy in artificial intelligence. By employing a sequential mixed- methods design, the researchers gathered qualitative data from 35 Palestinian faculty members through focus groups and semi-structured interviews and then surveyed 538 instructors’ perceptions through a refined questionnaire to gather the quantitative data. The researchers analyzed the responses using SmartPLS, and the results emphasized several influential elements related to instructors’ AI literacy, such as ease of use, access to professional development, prior experience with digital technologies, and the presence of supportive communities.

Challenges and implications

Many obstacles and limitations appeared when using Gen AI, especially in the educational system. Several researchers shed light on those shortcomings; Al Darayseh & Mersin (2025) highlighted the struggle teachers face with internet access in their institutions, outdated devices, lack of training, and insufficient support from their institutions. The review of Holmes et al. (2019) underlined the concerns related to transparency, privacy, and data protection, and underscored the need for clear ethical framework when using Gen AI tools.

Nabi et al. (2025) underscored the limitation of lacking sufficient training and preparation for teachers to integrate Gen AI meaningfully into instructional planning. The researchers concluded that without enough training, teachers could not understand the strengths or the concerns of using Gen AI in their teaching and consequently could misuse it or apply it in ways misaligned with the educational objectives.

Comments on previous studies

In reviewing the previous literature, some studies applied a descriptive approach to explore the role of artificial intelligence and Generative AI in instructional design, including the works of (Kumar et al., 2024; Luo et al., 2024; Kozan et al., 2025; Al-Abdullatif, 2024). Other studies, however, used a quasi-experimental approach, such as (Krushinskaia et al., 2024; Sabbah, Mahamid & Mousa, 2025). Regarding data collection, Kumar et al. (2024), Luo et al. (2024), Kozan et al. (2025), and Al-Abdullatif (2024) combined questionnaires and interviews, whereas Krushinskaia et al. (2024) and Sabbah, Mahamid & Mousa (2025) relied on pre- and post-tests, surveys, and interviews.

The review indicates that AI and GenAI have been widely studied in relation to instructional design, particularly for tasks such as lesson planning, content creation, and promoting learner engagement (Kumar et al., 2024; Luo et al., 2024; Krushinskaia et al., 2024; Kozan et al., 2025). Despite this, only a few studies have focused on teachers’ perceptions and readiness to use these technologies, as seen in (Al-Abdullatif, 2024; Kozan et al., 2025). Additionally, according to the Palestinian context, although the study of Ayyoub et al. (2025) investigated the perceptions of faculty members regarding their AI literacy, no previous research has examined primary school teachers’ perceptions of GenAI in instructional design, particularly in the Palestinian context.

In Palestine, embedding digital technologies into teaching and learning faces specific structural and contextual barriers that distinguishes it from other educational contexts. Limitations such as limited technological infrastructure, poor internet access, and insufficient professional development opportunities for teachers, hinder the effective use of digital tools in classrooms. These difficulties are amplified by socio-economic inequalities and uneven institutional support across different regions, resulting in varying levels of technology readiness and necessitating targeted strategies to assist teachers (Shehadah & Burhanuddin, 2025).

This gap highlights the need for the present study, which aims to explore basic education teachers’ perceptions of GenAI through a structured questionnaire. By doing so, the study seeks to provide a clearer understanding of teachers’ familiarity with, attitudes toward, and willingness to integrate GenAI into their teaching practices.

Methodology

Study design

This study aims to explore the factors influencing basic education teachers’ decision to use Gen AI in instructional design, and to fulfill this aim, a descriptive research design was employed, using a 16-item questionnaire. The reason for adopting a questionnaire as the instrument of the study is that the quantitative approaches provide objective measures and solutions (Geremew, Huang & Hung, 2024).

Population and sample of the study

The population of the study consisted of Palestinian teachers working in public schools under the Ministry of Education (MOE) in the Nablus Directorate. They teach basic education levels, which include grades 1-9, typically serving students aged approximately 6 to 15 years. The total population comprised around 1829 teachers, including 778 males and 1051 females. On the other hand, the sample of the study consisted of 328 teachers who were chosen randomly. Table 1 below shows the distribution of the sample according to the independent variables of the study.

Table 1

The distribution of the sample according to study independent variables

Instrument of the study

To achieve the objectives of the study, the researchers adopted a validated 13-item questionnaire from Marengo et al. (2025). In addition, more three items were added to align with the current study variables and improve their comprehensiveness and contextual fit. Therefore, the final version of the questionnaire consisted of 16 items with two main sections along with an introductory paragraph. The first section gathered demographic information, mainly: gender, academic qualification, field of specialization, and years of teaching experience. Whereas the second section consisted of 16 items structured on a 5-point Likert scale and presented to the sample in two languages, Arabic and English.

Validity and reliability

The questionnaire items adopted from Marengo et al., (2025) were originally reviewed by experts in educational evaluation and instructional technology to ensure clarity, content relevance, and linguistic accuracy, with results indicating strong content validity, with Content Validity Index (CVI) values of 0.78 and high clarity ratings averaging at 4.5 out of 5 (Marengo et al., 2025). In addition, the items were reviewed to ensure their theoretical relevance to the use of Gen AI in education. Moreover, 4 Palestinian experts were asked to validate the new version of the adopted questionnaire for achieving the objectives of the study.

The Cronbach Alpha formula was used to assess the reliability of the questionnaire. The internal consistency scales for the Cronbach Alpha were obtained from Tavakol & Dennick (2011), as illustrated below.

The total reliability coefficient degree was (0.783) which is considered good and suitable for the study purpose and indicated a strong internal consistency among the items. This assures that the instrument of the current study is reliable and can be trusted to accurately measure teachers’ perceptions regarding the factors influencing their decisions to use Gen AI in their instructional design.

Data analysis

The Statistical Package for Social Sciences (SPSS) was employed to analyze the responses of the teachers to the questionnaire items and address the questions of the study. Descriptive statistical methods, including means, frequencies, standard deviations, and LSD test for distance comparison were used to determine the relative distribution of the items. Additionally, the independent sample t-test was used to examine the role of gender, while the One-way Analysis of Variance (ANOVA) was applied to analyze the role of the other variables (academic qualification, field of specialization, and years of teaching experience). To analyze the findings, the researchers used the following scale to represent the estimation level of teachers’ responses.

Procedures

The flow of the study that aimed to investigate the factors influencing basic education teachers’ decisions to use Gen AI in instructional design went under the following procedures:

1. A thorough review of relevant studies was carried out to create a stable foundation for the current study.

2. Findings from the review led to the adoption of a highly validated questionnaire and helped in selecting the participants in the study.

3. Four Palestinian experts validated the questionnaire items to ensure credibility.

4. The finalized version of the questionnaire was created using Google forms in two languages, and distributed among personal e-mails, Messenger, and WhatsApp applications.

5. Collecting the data lasted about four weeks, resulting in responses from 328 teachers.

6. The responses were analyzed using SPSS software.

7. The results were discussed, and some recommendations were suggested.

Results and discussion

This study aimed to examine the key factors that influence basic education teachers’ decisions to use generative artificial intelligence in instructional design within the Nablus Directorate. It also explored whether gender, academic qualification, field of specialization, and years of teaching experience influence these factors. To address the research questions and test the related hypotheses, the data were analyzed systematically, and the results revealed the following:

Results related to the Main Question: What are the factors influencing basic education teachers’ decisions to use generative artificial intelligence for instructional design in Nablus Directorate?

To answer this question, the researchers used means, standard deviations, and estimation level as shown in Table 2 below.

Table 2

Means, Standard Deviations and estimation level of the factors influencing basic education teachers’ decisions to use generative artificial intelligence for instructional design in Nablus Directorate in descending order

Table 2 shows that the total mean score of the factors influencing basic education teachers’ decisions to use generative artificial intelligence for instructional design in Nablus directorate is (3.69) which suggests a high level of estimation and indicates generally positive perceptions among the study sample. Among the highest-rated items were the seventh item (I am impressed by the features of generative AI applications for teaching) with a mean score of (4.01), and the second item (I use generative AI applications to develop creative ideas for lesson activities) with a mean score of (4.0). In line with TAM, these results highlight the importance of perceived usefulness and ease of use, indicating that teachers adopt tools that clearly enhance teaching effectiveness and simplify lesson planning (Davis, 1983).

This result is also aligned with Marengo et al. (2025), who found that students generally hold positive attitudes toward Gen AI, especially when they perceive it as useful and easy to use. In the same way, teachers in Nablus were enthusiastic about AI tools that support creativity, lesson preparation, and overall teaching efficiency. However, teachers also raised concerns about accuracy, data privacy, and possible negative effects on students’ thinking skills. This indicates that, while both students and teachers recognize the advantages of Gen AI, teachers’ professional responsibilities make them more cautious, highlighting the importance of proper training, institutional support, and careful implementation.

Moreover, these results reflect teachers’ appreciation for the potentials AI tools may offer to reinforce their teaching practices, support creativity, and develop new ideas for their lessons. This goes in harmony with Hazzan-Bishara, Kol & Levy’s (2025) results, who found that among the most influential factors affecting teachers’ decisions to use AI tools in their teaching are perceived usefulness, workload reduction, and streamlined lesson preparation. Teachers may be motivated to integrate Gen AI tools into their teaching practices once they see the benefits in improving their instructional practices.

At the same time, items with high mean scores such as “I believe that overusing generative AI tools could hinder my students’ thinking skill development” (M=3.89), “I have concerns about the accuracy of the responses provided by generative AI tools” (M=3.73), “I understand that using generative AI applications may risk the protection of my personal data” (M=3.67), and “I am concerned about the future implications of using generative AI tools in education” (M=3.68) reflects cautions and concerns among teachers regarding the use of Gen AI tools in instructional design. This finding mirrors those of Holmes et al. (2019), who underlined concerns related to transparency, privacy, and data protection, and underscored the need for clear ethical framework when using Gen AI tools.

On the other hand, the items (I know where to find reliable AI tools for instructional design) and (I can rely on my school’s infrastructure (devices, internet) to support my use of generative AI tools) received the lowest mean scores. This result highlights concerns among teachers regarding their ability to find adequate AI resources, and poor schools’ infrastructure, which in turn may hinder the meaningful use of Gen AI in their instructional design. This goes in harmony with Al Darayseh & Mersin’s (2025) findings which highlighted the struggle teachers face with internet access in their institutions, outdated devices, lack of training, and insufficient support from their institutions.

While Gen AI offers potential benefits for lesson planning and instructional support, previous studies highlighted several concerns, including data privacy risks, overreliance on automated systems, and questions about reliability of AI-generated content (Kasneci et al., 2023; Tlili et al., 2023). The argument that can be drawn here is that although teachers may be eager to use Gen AI tools in their teaching practices and explore their potential positive effects, they also need practical training, institutional and technical support and guidance to use these tools effectively and safely.

Results related to the Second Question: Is there any significant difference between basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to (gender, academic qualification, field of specialization, years of teaching experience)?

To answer this question, the researchers analyzed the study hypotheses as shown below.

Results Related to the Study Hypotheses

1. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to gender (male/ female).

To analyze the first hypothesis, T- Test for Independent Samples was used and Table 3 shows the results.

Table 3

T-test for Independent Samples for the factors influencing teachers’ decisions to use generative artificial intelligence for instructional design due to gender (male/ female).

*The mean difference is significant at the 0.05 level.

Table 3 illustrates that there is no statistically significant difference at (α = 0.05) between the mean scores of male basic education teachers’ perceptions (M = 3.74, SD = 0.41) and female basic education teachers’ perceptions (M = 3.67, SD = 0.39) regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to gender with t (326) = 1.390, P = 0.165, which is more than (0.05). This result clarifies that the sample of the study, whether males or females, shares similar perceptions regarding the factors influencing their use of Gen AI. In other words, the decision to whether to use Gen AI for instructional design or not, does not appear to depend on gender as a determinant factor. This result supports Viberg et al.’s (2025) findings, which found that gender, age, and academic level did not significantly affect teachers’ willingness to integrate AI-based educational tools. Such a result may be attributed to the fact that factors, other than gender, may motivate teachers to use Gen AI for their instructional design, such as ease of use, access to professional development, prior experience with digital technologies, and the presence of supportive communities (Ayyoub et al., 2025).

2. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to academic qualification (B.A./ M.A./ Ph.D.).

To analyze the second hypothesis, One Way ANOVA test was used, and tables (5&6) show the results.

Table 4

Frequencies, Means and Standards Deviations of the factors influencing their decisions to use generative artificial intelligence for instructional design due to academic qualification (B.A./ M.A./ Ph.D.)

Table 4 illustrates that there are differences in the mean scores of participants’ perceptions due to their academic qualifications with a total mean of (3.69). To calculate the differences, a one-way ANOVA test was used with the results presented in the following table.

Table 5

Results of One Way ANOVA for the factors influencing their decisions to use generative artificial intelligence for instructional design due to academic qualification (B.A./ M.A./ Ph.D.)

* The mean difference is significant at the 0.05 level.

Table 5 demonstrates the result of the one-way ANOVA test which indicates that there is statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to academic qualification with F (2, 325) = 4.165, P = 0.016, which is less than (0.05). To explore the differences between the means of academic qualification, LSD test for post hoc comparisons was used and the results are shown in table 6.

Table 6

Results of LSD Test for Post Hoc Comparisons for basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to academic qualification (B.A./ M.A./ Ph.D.)

* The mean difference is significant at the 0.05 level.

The results of the post hoc LSD test show that the statistical difference in total degree was between (B.A.) and (Ph.D.) levels in favor of (Ph.D.), which indicates that teachers with higher academic qualification perceive the factors influencing their use of Gen AI more positively. This result contradicts with the results of Viberg et al. (2025), which confirmed no significant differences toward teachers’ decisions of integrating AI-based technologies based on demographic information such as gender and academic qualification, contributing the effect to teachers’ understanding and self-efficacy. This result indicates that it is not the academic degree itself that creates the difference, but rather maybe the associated knowledge, skills, usage, and confidence that is often built with higher qualifications. Teachers with high academic degrees may have had greater exposure to pedagogical research, a reason that may enhance their readiness and openness to adopt any new technologies.

However, this difference should be interpreted with caution. The number of Ph.D. holders on the study’s sample was very small (n =12) in comparison to the much larger number of B.A. participants (n =254), which may have created the mean difference. As illustrated in Tabachnick & Fidell (2019), extremely small group sizes can affect the reliability of the ANOVA test and decrease the generalizability of the group comparisons. Consequently, the presumed advantage among Ph.D. holders may be because of sampling inequality rather than the true effect of academic qualifications.

3. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to the field of specialization (Natural sciences/ Humanities).

To analyze the third hypothesis, T- Test for Independent Samples was used and Table 7 shows the results.

Table 7

T-test for Independent Samples for the factors influencing teachers’ decisions to use generative artificial intelligence for instructional design due to the field of specialization (Natural sciences/ Humanities).

*The mean difference is significant at the 0.05 level.

Table 7 illustrates that there is a statistically significant difference at (α = 0.05) between the mean scores of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to the field of specialization with t (326) = 4.076, P = 0.000, which is less than (0.05). The difference is in favor of Natural Science group (M = 3.81, SD = 0.36) compared to Humanities group (M = 3.62, SD = 0.40). This result suggests that teachers specializing in natural sciences view the factors influencing the decisions to use Gen AI for teaching more favorably than teachers in the humanities.

This difference can be interpreted as follows: Natural science subjects often involve frequent use of digital tools for simulations, modeling, and visualization, giving science teachers higher exposure to technology and possibly greater confidence in using those technologies, and consequently, this familiarity could make them more open to experience Gen AI tools for their instructional design. On the other hand, the humanities teachers may find fewer connections between Gen AI tools and their traditional instructional activities, or they may have some concerns regarding the use of these tools.

Consistent with Zawacki-Richter et al.’s (2019) study, which confirmed that because teachers in scientific fields are involved in analytical reasoning and algorithmic support, they were more linked to higher adaptation of AI and intelligent systems. In contrast, recent research found that teachers in humanities subjects were more cautious toward using AI for instructional tasks, especially for writing activities, because of worries that it may affect students’ critical thinking and engagement (Chen, Lee & Lee, 2025).

4. There is no statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to teaching experience (0-3 years/ 4-7 years/ more than 8 years).

To analyze the fourth hypothesis, One Way ANOVA test was used, and Tables 8 & 9 show the results.

Table 8

Frequencies, Means and Standards Deviations of the factors influencing teachers’ decisions to use generative artificial intelligence for instructional design due to teaching experience (0-3 years/ 4-7 years/ more than 8 years).

Table 8 illustrates that there are differences in the mean scores of participants’ perceptions due to their teaching experience with a total mean of (3.69). To calculate the differences, a one-way ANOVA test was used with the results presented in the following table.

Table 9

Results of One-Way ANOVA test for the factors influencing teachers’ decisions to use generative artificial intelligence for instructional design due to teaching experience

* The mean difference is significant at the 0.05 level.

Table 9 demonstrates the result of the one-way ANOVA test which indicates that there is statistically significant difference at (α = 0.05) between the means of basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to years of teaching experience with F (2, 325) = 4.279, P = 0.015, which is less than (0.05). To explore the differences between the means of teaching experience, LSD test for post hoc comparisons was used and the results are shown in table 10.

Table 10

Results of LSD Test for Post Hoc Comparisons for basic education teachers’ perceptions regarding the factors influencing their decisions to use generative artificial intelligence for instructional design due to teaching experience

* The mean difference is significant at the 0.05 level.

The results of the post hoc LSD test show that the statistical difference in total degree was between (0-3 years) group and the other two groups (4-7 years and more than 8 years) in favor of (0-3 years), which indicates that novice teachers with less years of teaching experience perceive the factors influencing their use of Gen AI more positively. Novice teachers tend to be more open to utilizing modern technologies because they often are younger and belong to a generation that grew up with digital tools and received more systematic exposure to such technologies during their study. Therefore, those teachers may be more confident and open to trying new technologies like Gen AI with their teaching (Pischetola, 2022).

Moreover, current pre-service programs integrate digital pedagogy and technology-focused courses more, so recent graduates begin their academic career with stronger preparation in educational technology than those whose teacher education took place years earlier (Tzafilkou et al., 2023). These differences in preparation and experience help explain why less-experienced teachers show greater readiness to use AI-based tools (Bakhadirov, 2024).

Conclusion

The results of the current study revealed that basic education teachers generally had positive perspectives toward using Gen AI for instructional design, especially for appreciating its role in developing creative ideas for lesson activities, raising excitement, helping in preparing lessons and instructional materials, and raising teachers’ overall efficiency. However, despite this enthusiasm, teachers also were concerned about some negative effects Gen AI tools may have, such as hindering students’ skills, protecting personal data, and providing inaccurate responses. Moreover, the results clarified some differences in teachers’ perceptions due to their academic qualifications, years of teaching experience and their fields of specialization. Highly qualified teachers, Natural Science’s educators, and novice teachers reported more positive attitudes toward the use of Gen AI in teaching.

On the other hand, items related to the availability of sufficient training on the use of AI, the inadequate infrastructure of schools, and the limited access to reliable AI tools were among the lowest-rated items in the survey, suggesting that teachers’ positive attitudes toward the use of Gen AI alone is not enough for the meaningful use of these tools, it should be accompanied with sufficient and practical training, adequate and modern infrastructure (devices and Wi-fi networks), and institutional support.

Recommendations

Based on the previous results, the researchers suggested the following recommendations:

1. Enhancing teachers’ skills in using GenAI for instructional design through ongoing hands-on workshops that focus on creating lesson activities, planning effectively, and addressing ethical considerations. This responds to the low scores in training and resource awareness.

2. Establishing school policies and guidelines to ensure responsible and effective use of Gen AI, addressing concerns such as overreliance on AI, data privacy risks, and the accuracy of AI-generated materials.

3. Upgrading infrastructure and access to reliable AI tools, including improving internet connectivity and providing adequate devices, to facilitate meaningful integration of Gen AI in teaching.

4. Conducting longitudinal and mixed-methods research to monitor how teachers implement Gen AI over time and how their usage patterns develop.

5. Examining demographic factors such as academic qualification, years of teaching experience, and field of specialization to better tailor training and support initiatives.

6. Investigating students’ perspectives and outcomes when Gen AI is integrated into instruction, focusing on engagement, creativity, and critical thinking development.

Limitations

As any academic work, the current study carries some limitations that need to be considered. Since the study was conducted only with basic education teachers in the Nablus Directorate, the findings may not accurately reflect the experience of teachers of elementary or secondary levels, or teachers working in other areas. Specifically, the small number of Ph.D. participants (n = 12) compared to the bigger number of B.A. holders (n = 254) may have affected the differences in perspectives based on academic qualifications, which may decrease the reliability of this comparison. The study relies on a questionnaire as the only research tool to investigate the perceptions of the participants, which can be influenced by personal bias or differences on how participants interpret the items of the questionnaire. Additionally, the distribution of the questionnaire took a lot of time and faced a lot of challenges in reaching many participants. As for the participants, despite being randomly selected, it may not reflect the whole population of the study. Moreover, because the study explored teachers’ perceptions rather than their actual use of Gen AI in instructional design, the findings may not reflect how Gen AI is really employed or practiced.

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