This research explores the use of Generative Artificial Intelligence (GenAI) tools among higher education students in Saudi Arabia, aiming to understand their current perceptions of these technologies. This study utilizes the Technology Acceptance Model (TAM) and the theory of Task-Technology Fit (TTF) to examine students’ utilization, perceived benefits, and challenges associated with these tools. A cross-sectional survey was conducted, yielding 859 responses. The findings indicate that 78.7% of students frequently use GenAI tools, while 21.3% do not, often due to a lack of knowledge or interest. ChatGPT emerged as the most widely used GenAI tool, utilized by 86.2% of respondents, followed by other tools like Gemini, Socratic, and CoPilot. Students primarily use these tools for defining or clarifying concepts, translation, generating ideas in writing, and summarizing academic literature. They cite benefits such as ease of access, time-saving, and instant feedback. However, they express concerns about the challenges, including subscription fees, unreliable information, plagiarism, reduced human-to-human interaction, and impacts on learning autonomy. This study underscores the need for increased awareness, ethical guidelines, and robust academic integrity measures to ensure the responsible use of GenAI tools in educational settings. These findings highlight the need for a balanced utilization of GenAI tools in higher education that maximizes benefits while addressing potential challenges and guides the development of policies, curricula, and support systems.

This small-scale qualitative study aimed to explore learning programming through online experiences among middle school students in a school for girls in Saudi Arabia. The low uptake of computing by girls has been a persistent problem in schools and beyond. In Saudi Arabia, there are similar issues in encouraging learners and girls in particular to be interested in computer coding. To explore how to engage learners in coding, an informal online course on programming for learners (age 12) was designed using a community-of-inquiry approach and a gamification process enabled through the use of Thunkable™ and TalentLMS™ to engage learners. An inductive qualitative research approach was used to explore influencing factors for engaging learners in programming. The data comprised three individual interviews, one focus group, a teacher’s diary, and a content analysis of the activities recorded in the TalentLMS™ system’s student progress reports. Findings highlighted the need to consider digital learning agency in the online learning environment and that learning programming online was best facilitated through student collaboration using live tools with teacher support to develop the online community. Further, findings revealed the rationale for the girls enrolling in the online programming course, which included improving their online learning skills, planning future careers, and developing résumés. These findings may contribute to offering some insight into pedagogy that can encourage greater interest in computer programming in schools.

The aim of research is to reveal the effect of a training program based on the TAWOCK model for teaching computational thinking skills on teaching self-efficacy among computer teachers. It used the quasi-experimental approach, with a pre-test and post-test design with a control group. An electronic training program based on the TAWOCK model was designed, and prepared (teaching self-efficacy scale). The research sample included (42) female teachers in the experimental group, and (39) female teachers in the control group. The research reached: There was a statistically significant difference at the level (α ≤ 0.05) between the mean scores of computer teachers in the experimental and control groups in the post application; In favor of the experimental group, the value of the ETA square for the total scale of teaching self-efficacy was (0.613); Which indicates the size of significant impact on the self-teaching effectiveness of female computer teachers. There was also a statistically significant difference at the level (α ≤ 0.05) between the mean scores of computer teachers in the experimental group in the pre and post applications; In favor of the post application, the value of Cohen’s coefficient for the total scale of teaching self-efficacy was (1.26).