ChatGPT (Generative Pre-trained Transformer) is a conversational artificial intelligence interface application chatbot. It created a storm of reactions, both positive and negative. There are many positive responses to its use in various fields, including education. Some are cautious about its use in education as they fear possible abuses and concerns. There are concerns about privacy, personality, manipulation, and ethics (McCallum, 2023; Eke, 2023; Asad et al., 2024). ChatGPT can enhance traditional teaching methods through student engagement and machine-aided intelligent learning. ChatGPT provides an enhanced learning platform that could improve the learning experience and assist students in academic performance. It can empower instructors with interactive individualized attention (Chiu, 2024; García-Peñalvo, 2023).

With adequate appropriate input, ChatGPT is an interactive intelligent chatbot that could answer questions with correct responses (Clarizia et al., 2018). The size and accuracy of the database decide the reliability of responses (Aleedy et al., 2022). ChatGPT was tested in medical, law, and business school examinations and found to be impressive (Terwiesch, 2023; Choi et al., 2023). Since the 1980s, AI has become a significant research area (Williamson et al., 2020). ChatGPT has a profound effect on education (Zhou et al., 2023). However, the technology alone cannot revolutionize the education sector; there is a need to change the learning philosophy to achieve the desired benefit of the technology (Castañeda and Selwyn, 2018).

When learning is planned with technology, learning can be supported by the technology. The learners are recipients of technology services, or they collaborate with technology, or technology empowers learners to take control of their learning and collaborate with other stakeholders (Ouyang and Jiao, 2021; Xia et al., 2022). In whichever aspect the technology is used, the learners, the context of use, assigned tasks, pedagogical methods, level of integration, and application of technology need to be considered (Zheng et al., 2021). This could enhance learning, motivate the learners, and improve academic performance. ChatGPT can assist disabled students. Studies showed that AI-based teaching improved comprehension among dyslexia students (Llorente et al., 2021). It was also shown to enhance learning among autistic students (Zhang et al., 2022). The technology will be of great help in controlling students’ anxiety, inculcating a positive attitude toward learning, and improving skills (Jaiswal and Arun, 2021; Alotaibi et al., 2025). It could build teaching competence through inspiration and self-reflection (Aldeman et al., 2021). The instructors will be able to monitor the students’ performance and take diagnostic steps to control the students’ dropout rate. A rich database could be built to predict students’ performance, behaviors, and related information.

However, caution should be exercised as ChatGPT could diminish critical thinking and innovation capacities. It could affect educational quality, as the students may outsource their work to ChatGPT to facilitate cheating and plagiarism. ChatGPT learns from prior interaction; if there is no prior interaction on the subject matter, it could provide inaccurate information. It is used for answering questions by generating ideas, content, story, solving problems, and creating music. ChatGPT can create social interaction as it is designed to be interactive. ChatGPT will not make classical skills obsolete; paradoxically, it will improve skills related to critical thinking, problem-solving, and critical analysis.

Considering the positive and negative sentiments in the literature, studying the intention to use ChatGPT by law educators will be beneficial. The objective of this research was to collect data on the intention to use ChatGPT among law educators. Research in this area is new, as no study has been undertaken in this aspect among law educators in Saudi Arabia. This research could contribute to the existing knowledge. The research employed the Unified Theory of Acceptance and Use of Technology (UTAUT) model to investigate the intention to use and actual use of ChatGPT among law educators. The UTAUT model has been used since 2003, and since then, over 800 research papers have employed this model in various technology adoption studies (Morsi, 2024). The UTAUT model is considered complete since it covers all the variables of technology adoption (Venkatesh et al., 2011). The UTAUT model is the most acceptable in technology adoption (Rodrigues et al., 2016; Raffaghelli et al., 2022; Rodrigues et al., 2024). Therefore, the research employed the UTAUT model to achieve the research objective. The model can investigate technology adoption among users with dissimilar personal characteristics, such as age of user and IT skills.

The remainder of the article is divided as follows. Section 2 describes the literature review and hypothesis development. Section 3 explains the method used. The result of the survey is explained in section 4. The policy, practical, and theoretical implications of the research findings are provided in Section 5. Conclusion, limitation, and future research direction are covered in section 6.

The survey method using items from the UTAUT model as developed by Venkatesh et al. (2003) was used in this research. Numerous studies. The same measurement items were adopted with some modification. During the research development process, some of these items were removed as it is not relevant to achieving the objective of the current study. The UTAUT model has five major variables with few items in each variable. There are four items in the performance expectancy variable. The usefulness of ChatGPT, information collection, overall productivity, and ability to increase information and efficiency. Another four items are included in effort expectancy. The Items are: clear and understandable interaction, easy to learn, easy to use, and easy to operate. The third variable has 4 items related to social influence, and the fourth variable of facilitating conditions has four variables too. The items are related to availability of resources, having necessary knowledge, technology compatibility, and support from others. The final variable of behavioral intention has 3 items; they are related to intention to frequently use ChatGPT in education in the future. The questionnaire is provided in Appendix A. As suggested by Churchill (1979), three steps were taken to validate the measurement items and the survey questionnaires. The validated survey questionnaire from Venkatesh et al. (2003) was used after assessing its suitability for the current research. A pilot test with seven participants who are experienced educators was conducted to make sure that the questions convey the meaning. The experts’ opinions helped to remove the ambiguity in questions. The target population was law educators who used or intended to use ChatGPT in education (Figure 1).

The participants were selected using a convenience sampling method as there is no official published registry of residents available (Shah Alam et al., 2024; Fatma and Khan, 2023). The participants were selected from those who teach law and have experience in using the internet and technology. Males and females from different nationalities, educational levels, and age groups were reached out to. The selected participant was reached online; they were informed about the purpose of the survey. The researcher obtained participants ‘consent. The responses were anonymous, and they were given the option to leave the survey at any time. The participants were given 3 months to complete the survey, and a reminder was sent after a month. After 3 months, the researcher received 70 responses, out of which 61 were complete. Nine incomplete surveys were removed.

The Questionnaire used was a closed questionnaire with a multiple-choice five-point Likert scale, where “1” was “strongly disagree,” and “5” was “strongly agree” as used by the original UTAUT model. The questionnaire had two parts. The first part included questions on demography, and the second part consisted of questions on ChatGPT adoption in law education. The second section was divided into five areas, comprising questions related to UTAUT components. They are performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. The researcher used Microsoft Excel for analyses as it is best suited to achieve the research objective. It applied descriptive analysis as it was enough to achieve the research objective and provide appropriate practical and policy suggestions to encourage adoption of ChatGPT by legal educators.

The survey was distributed to 100 respondents who teach various law subjects with varying backgrounds and work in Riyadh. The researcher received 70 responses, out of which 61 were reliable. The demographic data showed that 41% of the respondents were male and 59% of them were female. 87% of them had postgraduate studies, as many institutions in Saudi Arabia require a master’s degree and beyond to be appointed as educators in colleges and universities. A minimum of a bachelor’s degree is required to be appointed as a teacher. Many of those who participated in the survey were above 26 years old. This helped the researcher collect data from educated, knowledgeable, and active educators. Computer knowledge and use are important for the use of ChatGPT. The survey results showed that 92% of the respondents have used computers for more than 5 years. The results further revealed that the respondents used ChatGPT for various purposes: learning, preparation for classes, assignments, and research. The majority of them have used it for research. The descriptive analysis is presented in Table 1.

The results of the analyses show that H1, H2, H3, H4, and H5 are important, and all the constructs contributed to the possible adoption of ChatGPT in education. H1 (performance expectancy) comprises questions on usefulness, efficiency, and productivity. The H1 was proved to be significant. This is said to be proven to be an important factor in adopting technology among users of technology in developing and developed nations (Venkatesh et al., 2003; Carter and Belanger, 2004; Chen et al., 2023). The higher the score in this construct, the higher the chances of using ChatGPT. The result of the survey showed that in total, 42% of the respondents strongly agreed on all four variables within the construct. 32% of respondents agreed to all four variables within the construct. In total, 64% of respondents have confirmed that the performance expectancy is an important construct in ChatGPT use in legal education. The detailed responses are provided in Figure 2.

H2 (Effort expectancy) is another important construct in the technology adoption model. The effort expectancy was tested to understand the perception and ease of use of ChatGPT. This construct is used to understand the simplicity of the system that could facilitate use and adoption. The result showed that the familiarity of ChatGPT is high, as many of the respondents were computer savvy. The participant in the current research perceived that the use of ChatGPT is easy to learn and use. About 48% strongly agreed that ChatGPT is easy to learn, and 43% of them strongly agreed that it is easy to use. The majority of the respondents also agreed that ChatGPT is easy to become skilled in, and it is understandable. As research showed that the effort expectations are important to the adoption of new technology, it is expected that educators will find ways to use ChatGPT in education. The finding is similar to the findings of other researchers on technology adoption (Wu et al., 2023). Research suggested that those who found it easy to use will eventually adopt the technology (Venkatesh et al., 2003; Chen et al., 2023). Figure 3 provides detailed results on effort expectancy.

H3 (Social influence) construct includes social factors, norms, and image that tend to influence intention to use technology. The author tested this construct to see the influence of the behavioral intention to use ChatGPT in education. The finding revealed that 61% of the respondents agreed that others’ influence on their behavior is significant in using ChatGPT. 48% of them stated that the people important to them influence the use of ChatGPT, and 51% of them agreed/strongly agreed that their organization supports the use of ChatGPT in education. 57% of the respondents agreed that they have the support to use ChatGPT in education. The finding of social influence of technology adoption was similar to the finding of Yu (2012), who studied adoption of mobile banking among users, and Wu et al. (2023), who assessed the adoption of Medical self-service terminals among the elderly. However, some research in areas like e-government showed the insignificance of social influence in voluntary adoption of technology (Kim et al., 2015, Mansoori et al., 2018). The detailed results are presented in Figure 4.

H4 (Facilitating conditions) construct is also known as perceived behavior control or adoptability of innovation (Davis, 1989). This construct is considered important in adopting a technology. The Facilitating conditions were tested to assess the availability of resources, knowledge, support, and compatibility of the technology, as these factors seem to be significant in predicting behavioral intention to adopt technology. The results revealed that the respondents are positive about the availability of necessary resources. 66% of the respondents either strongly agreed or agreed that they have access to resources. The respondents who are knowledgeable in using ChatGPT effectively, as 87% of the participants have a postgraduate qualification, and they have many years of experience with the technology. These factors made it easy for them to adopt ChatGPT. 66% of them agreed that the ChatGPT technology is compatible with other technology, and as such, it will allow users to use the existing technology without the need to improve their technological assets. 52% also confirmed that they will be able to get help from others if they face difficulties. The finding is in par with the findings of Alsharif et al. (2013) and Frans and Pather (2022). The detailed result is presented in Figure 5.

Davis (1989) explains that behavioral intention is the individual readiness to perform a certain activity or behavior. When individuals have a stronger intention to act, the likelihood of performance is high (Ajzen, 1991). The positive intention to use ChatGPT will lead to actual use. The survey result showed that 75% of the respondents planned to use ChatGPT in the future, and 89% of them intended to use it frequently. The overall result revealed that the respondents intended to use ChatGPT in the future. The finding is similar to the other research findings on technology adoption. It was established that the stronger the intention to use a particular technology, the greater the actual use will be (Venkatesh et al., 2003; Frans and Pather, 2022). The result of this construct is presented in Figure 6.

The research has few policy, practical, and theoretical implications. The present study expanded the body of knowledge about ChatGPT adoption among law educators in Saudi Arabia. The technology is new. No research has investigated the possible factors that influence the adoption of ChatGPT among law educators. The research is the first of its kind in this context and will fill the gap in the literature in this area, as the search of the literature does not reveal any literature on ChatGPT adoption by law educators in Saudi Arabia. The research can assist in understanding factors that influence technology adoption among educators. Educational institutions or departments can use findings to understand the need for providing facilities to support the use of ChatGPT.

The result provided insight into the factors that influence the possible adoption of ChatGPT by law educators. This will guide the policy makers in preparing and executing a technology adoption strategy that is academically sound and practically acceptable. The strategy should have a structured institutional policy on the use of technology and AI tools in learning and research. Training should be included as part of skill development within the strategy. The training should prioritize the optimum use of ChatGPT in law education. It should also enhance educators’ skills in asking the right questions or creating assessments like reflective writing and flipped learning (Rudolph et al., 2023). Dilekçi and Karatay (2023) stated that assessment of information, deriving ideas, referencing, presentation, and justifications of their work could be categorized as ChatGPT era skills, as they could augment learning capacity. Preparing educators is necessary to enhance those skills. According to Kung et al. (2023), 60% of the content sourced through ChatGPT is accurate, and it demands careful verification before using the inputs from ChatGPT. The accuracy of the answers that one can get from ChatGPT depends on the wording of the questions, which benefits from ChatGPT. Asking questions and critical thinking are necessary skills that should be included as part of learning objectives. The failure of these competencies could affect motivation to adopt the technology (Fryer et al., 2019). ChatGPT adoption will necessitate modifying the teaching philosophy and assessment methods, where oral argument, logical and critical thinking, group projects, and hands-on activities should be an integral part of the teaching assessment.

Organizational policy should facilitate the change of teaching philosophy and assessment methods. Use of ChatGPT can cause bias and violate intellectual property and privacy. As such, it is necessary to provide guidelines to control bias and other violations. Rahman and Watanobe (2023) suggested taking initiative to eliminate fake, baseless, and harmful content. As ChatGPT can generate text, educators should enhance their skills to check the accuracy of the information presented. They can include a viva of the assignment to ensure that the assignment is completed by them, and require a report or peer assessment. ChatGPT has many benefits for law educators, and they should be encouraged to adopt it in their teaching and research. The educational institution should fund research related to ChatGPT so that the benefits of ChatGPT could be explored and advances could be fully achieved (Sok and Heng, 2024). Finally, this study applied the theoretical literature relating to technology adoption models and the UTAUT model, with some variation, to suit the Saudi Arabian context regarding adoption of technology among law educators.

The study intended to identify factors influencing intention to use and actual use of ChatGPT by law educators. The research found that all the factors within UTAUT were highly significant and can positively influence behavioral intention and actual behavior. Since scores on behavioral intention to use ChatGPT are positive, the educators will likely use ChatGPT in the future. The findings are beneficial in providing insights to educational institutions. They can assess their policies to strengthen educators’ intention to use and actual use of ChatGPT in educational services. The up-to-date policies can cater to the changing dynamics of the education sector.

However, the current study has a few limitations. The research used a questionnaire-based survey to collect data from law educators, and Microsoft Excel was used to analyze the data. Further, the study was conducted among the law educators based in Riyadh, Saudi Arabia. Since all the cities in Saudi Arabia have similar cultures, the findings could be generalized to other cities in Saudi Arabia. However, it may not be generalized to countries with different cultures. Another limitation was that the questionnaire was distributed at a single point in time; as such, it would not have captured the change in reaction among the participants. It is also limited to the use of ChatGPT in education, and DeepSeek and other chatbots were not included in the research. The research has utilized descriptive analysis and not inferential tests.

Future studies may consider covering educators who teach other disciplines and could also include students. It could apply inferential tests to understand the linear relationship between the dependent variable and the independent variable, the differences between independent groups, and to compare independent variables. They may use qualitative methods to study a different group and use other technology adoption models as well. Since this study covered only the capital city due to time and logistic constraints, future studies could expand to cover all the provinces of Saudi Arabia and beyond. Future research could also study adoption of other chatbots and compare with the results at different points in time.