In today’s digital era, artificial intelligence (AI) driven robotic teaching assistants are gaining increasing attention in the field of education. These systems not only support personalized learning but also ease administrative workloads, thereby improving overall efficiency [1-2]. Their use offers a range of advantages, including enhanced productivity, improved communication and greater teaching effectiveness. Robots can deliver customized lessons, provide instant feedback and create an engaging, low-stress learning environment for students [3-4]. Despite these benefits, it is vital to examine the enablers and barriers that influence their adoption within educational institutions.   The past decade has witnessed a remarkable technological revolution that has significantly reshaped teaching and learning processes in educational settings [5-7]. Educators and stakeholders must be prepared to embrace these changes and adopt innovative tools to foster collaborative, interactive and meaningful learning experiences. Consequently, teaching methodologies must continuously evolve and adapt to the rapid digital transformations occurring globally [8-9].   As AI continues to advance, robotic teaching is emerging as an indispensable component of modern education, supporting and enhancing both teaching and learning practices. Several countries have already introduced or are actively experimenting with the use of robots at primary and secondary school levels [10-13]. This has prompted researchers to carefully analyse the potential benefits and challenges of robotic teaching, as concerns about its future role in education remains [14-15]. Colleges and universities must also prepare for the integration of robotic teaching systems, which requires a deep understanding of both the promoting factors and the barriers to implementation [16-18].   Research in this area is crucial for guiding education planners in designing effective policies and strategies to ensure the meaningful integration of robotic teaching into higher education. Such efforts can raise awareness about the best practices for its use and maximize its potential benefits for students and teachers alike. However, to date, limited evidence is available from the Kingdom of Saudi Arabia regarding the role of robotic teaching in colleges and universities [19-20]. To address this gap, the present study aims to explore university students’ perceptions of robotic teaching and evaluate its potential incorporation into higher education systems.

Study Setting and Design This study adopted a cross-sectional descriptive survey design and was conducted in Saudi Arabia between January and September 2024.   Sample Size and Sampling Method A convenience sampling technique was used. The minimum required sample size was determined to be 386, calculated using the following formula:     Where:   Z1-α/2 : The standard normal variate at 5% type 1 error (p<0.05); it is 1.96 P : The expected proportion based on previous studies D : The absolute error (0.05)   Research Tool Data were collected using a structured, pre-designed questionnaire that assessed participants’ perceptions of robotic teaching and their prior educational exposure. Awareness levels were categorized as high if more than 50% of the perception-related questions were answered correctly.   Inclusion and Exclusion Criteria The study included both male and female students aged 18 years and above. Individuals not enrolled in higher education, as well as those residing outside the study area, were excluded.   Statistical Analysis Data analysis was performed using STATA/SE version 11.2 (StataCorp, College Station, Texas, USA) and Microsoft Excel. Results were presented as frequencies and percentages. Comparative analyses were conducted using the Chi-square test (χ²) and Fisher’s Exact Test (FEET). A p-value of less than 0.05 was considered statistically significant.

Out of the total 423 participants, the majority (72.1%) were in the 18-22 age range, with 79.67% being female. Detailed demographic information is presented in Table 1.   Table 1: Sociodemographic characteristics of the study participants Variable No. % Age (year) 18-22 305 72.10 23-27 71 16.78 ≥28 47 11.11 Gender Male 86 20.33 Female 337 79.67 College Faculty of Medicine 122 28.84 College of Business Administration 57 13.48 Faculty of Home Economics (Girls) 7 1.65 Faculty of Education and Arts 34 8.04 College of Nursing 47 11.11 College of Science 25 5.91 Faculty of Applied Medical Sciences 11 2.60 Arar Community College 16 3.78 College of Engineering 11 2.60 College of Sciences and Arts (Rafha) 5 1.18 College of Pharmacy (Rafha) 39 9.22 College of Computing and Information Technology (Rafha) 13 3.07 College of Science and Arts in (Turaif) 36 8.51 Academic Year Preparatory 52 12.29 First Year 35 8.27 Second Year 44 10.40 Third Year 67 15.84 Fourth Year 127 30.02 Fifth Year 41 9.69 Sixth Year 17 4.02 Internship 40 9.46 While most participants (69.27%) supported the inclusion of robots in teaching, their opinions were divided on whether robots could replace human instructors. Participants demonstrated a strong awareness of the advantages and disadvantages of robotic teaching, with over 65% providing correct responses for both categories. Nevertheless, 70.21% expressed a desire for additional training sessions. Figure 1 provides responses to all perception questions. However, as shown in Table 2, there were no significant differences in perceptions across age groups.     Figure 1: Students’ perception of robotic teaching   Table 2: Variations in students’ perception about robotic teaching by age (n.=423) Variable Age (year) Yes No I do not know χ2 p No. % No. % No. % Can robots be used for teaching in colleges and universities? 18-22 210 71.67 53 74.65 42 71.19 0.30 0.99 23-27 50 17.06 11 15.49 10 16.95 ≥28 33 11.26 7 9.86 7 11.86 Can robots replace human teachers? 18-22 119 68.79 159 75.71 27 67.50 FET 0.20 23-27 37 21.39 27 12.86 7 17.50 ≥28 17 9.83 24 11.43 6 15.00 Robots are attractive because they are available anytime, anywhere. 18-22 238 71.04 42 73.68 25 80.65 FET 0.67 23-27 58 17.31 8 14.04 5 16.13 ≥28 39 11.64 7 12.28 1 3.23 Robots are attractive because they allow repeated teaching sessions without getting tired. 18-22 256 71.11 29 74.36 20 83.33 FET 0.75 23-27 63 17.50 5 12.82 3 12.50 ≥28 41 11.39 5 12.82 1 4.17 Robots are attractive because they offer to conduct multiple tasks at the same time. 18-22 259 71.55 21 75.00 25 75.76 FET 0.73 23-27 64 17.68 4 14.29 3 9.09 ≥28 39 10.77 3 10.71 5 15.15 Robots are attractive because they offer to get real-time unbiased feedback without any fear. 18-22 241 72.81 24 70.59 40 68.97 FET 0.42 23-27 58 17.52 4 11.76 9 15.52 ≥28 32 9.67 6 17.65 9 15.52 Robots are attractive because they offer a non-stressful/ friendly learning environment. 18-22 200 72.46 51 68.92 54 73.97 2.76 0.60 23-27 49 17.75 11 14.86 11 15.07 ≥28 27 9.78 12 16.22 8 10.96 Robots are attractive because they offer personalized / customized teaching adaptable to the changing needs of the user. 18-22 211 70.57 52 80.00 42 71.19 4.02 0.40 23-27 54 18.06 9 13.85 8 13.56 ≥28 34 11.37 4 6.15 9 15.25 Robots are attractive because they can address to unlimited audience at the same time. 18-22 232 72.73 45 69.23 28 71.79 FET 0.95 23-27 53 16.61 12 18.46 6 15.38 ≥28 34 10.66 8 12.31 5 12.82 Do you think Robots will be more helpful in improving the education and research in near future? 18-22 203 71.48 58 70.73 44 77.19 3.16 0.53 23-27 45 15.85 17 20.73 9 15.79 ≥28 36 12.68 7 8.54 4 7.02 Robots are attractive because there are the least chances of infection. 18-22 231 70.64 41 80.39 33 73.33 3.83 0.43 23-27 55 16.82 8 15.69 8 17.78 ≥28 41 12.54 2 3.92 4 8.89 Robots are attractive because they can store huge amounts of updated information. 18-22 255 70.83 23 76.67 27 81.82 FET 0.46 23-27 62 17.22 6 20.00 3 9.09 ≥28 43 11.94 1 3.33 3 9.09 Robotic teaching is not attractive as it is dependent on Internet connections. 18-22 198 69.47 71 79.78 36 73.47 8.67 0.07 23-27 56 19.65 6 6.74 9 18.37 ≥28 31 10.88 12 13.48 4 8.16 Robotic teaching is not attractive as it depends on software and its updates. 18-22 202 69.90 66 80.49 37 71.15 6.63 0.16 23-27 55 19.03 6 7.32 10 19.23 ≥28 32 11.07 10 12.20 5 9.62 Robotic teaching is not attractive as the related technology may be difficult to use for some users. 18-22 205 71.43 57 75.00 43 71.67 5.40 0.25 23-27 52 18.12 7 9.21 12 20.00 ≥28 30 10.45 12 15.79 5 8.33 Robotic teaching is not attractive as the related technology may be expensive. 18-22 215 72.39 45 71.43 45 71.43 4.78 0.31 23-27 51 17.17 7 11.11 13 20.63 ≥28 31 10.44 11 17.46 5 7.94 Robots are not attractive as there is a lack of social and emotional touch as with the human teacher. 18-22 225 70.53 39 75.00 41 78.85 6.81 0.15 23-27 59 18.50 4 7.69 8 15.38 ≥28 35 10.97 9 17.31 3 5.77 Robots are not attractive for the poor audio verbatim (disrupted voice quality) from the robots. 18-22 210 72.16 55 74.32 40 68.97 5.91 0.21 23-27 51 17.53 7 9.46 13 22.41 ≥28 30 10.31 12 16.22 5 8.62 Do you think there should be some workshops/ seminars/webinars on robotic teaching? 18-22 213 71.72 46 70.77 46 75.41 3.92 0.42 23-27 46 15.49 14 21.54 11 18.03 ≥28 38 12.79 5 7.69 4 6.56 However, significant gender differences were observed in opinions regarding the inclusion of robots in educational settings (p = 0.04) and whether robots could replace human teachers (p = 0.04) (Table 3).   Table 3: Variations in students’ perception of robotic teaching by gender Variable Gender Yes No I do not know χ2 p No. % No. % No. % Can robots be used for teaching in colleges and universities? Male 69 23.55 11 15.49 6 10.17 6.66 0.04 Female 224 76.45 60 84.51 53 89.83 Can a robot replace a human teacher? Male 54 31.21 28 13.33 4 10.00 21.63 <0.001 Female 119 68.79 182 86.67 36 90.00 Robots are attractive because they are available anytime, anywhere Male 67 20.00 12 21.05 7 22.58 0.14 0.93 Female 268 80.00 45 78.95 24 77.42 Robots are attractive because they allow repeated teaching sessions without getting tired Male 72 20.00 10 25.64 4 16.67 FET 0.64 Female 288 80.00 29 74.36 20 83.33 Robots are attractive because they offer to conduct multiple tasks at the same time Male 67 18.51 9 32.14 10 30.30 5.18 0.07 Female 295 81.49 19 67.86 23 69.70 Robots are attractive because they offer to get real-time unbiased feedback without any fear Male 62 18.73 10 29.41 14 24.14 2.77 0.25 Female 269 81.27 24 70.59 44 75.86 Robots are attractive because they offer a non-stressful/ friendly learning environment Male 62 22.46 14 18.92 10 13.70 2.85 0.24 Female 214 77.54 60 81.08 63 86.30 Robots are attractive because they offer personalized / customized teaching adaptable to the changing needs of the user Male 59 19.73 13 20.00 14 23.73 0.49 0.78 Female 240 80.27 52 80.00 45 76.27 Robots are attractive because they can address to unlimited audience at the same time Male 66 20.69 12 18.46 8 20.51 0.17 0.92 Female 253 79.31 53 81.54 31 79.49 Do you think Robots will be more helpful in improving the education and research in near future? Male 59 20.77 17 20.73 10 17.54 0.32 0.85 Female 225 79.23 65 79.27 47 82.46 Robots are attractive because there are the least chances of infection Male 64 19.57 12 23.53 10 22.22 0.54 0.76 Female 263 80.43 39 76.47 35 77.78 Robots are attractive because they can store huge amounts of updated information Male 70 19.44 9 30.00 7 21.21 1.92 0.38 Female 290 80.56 21 70.00 26 78.79 Robotic teaching is not attractive as it is dependent on Internet connections Male 64 22.46 13 14.61 9 18.37 2.71 0.26 Female 221 77.54 76 85.39 40 81.63 Robotic teaching is not attractive as it depends on software and its updates Male 63 21.80 12 14.63 11 21.15 2.05 0.36 Female 226 78.20 70 85.37 41 78.85 Robotic teaching is not attractive as the related technology may be difficult to use for some users Male 63 21.95 12 15.79 11 18.33 1.58 0.45 Female 224 78.05 64 84.21 49 81.67 Robotic teaching is not attractive as the related technology may be expensive Male 61 20.54 14 22.22 11 17.46 0.47 0.79 Female 236 79.46 49 77.78 52 82.54 Robots are not attractive as there is a lack of social and emotional touch as with the human teacher Male 66 20.69 9 17.31 11 21.15 0.34 0.84 Female 253 79.31 43 82.69 41 78.85 Robots are not attractive for the poor audio verbatim (disrupted voice quality) from the robots Male 55 18.90 19 25.68 12 20.69 1.68 0.43 Female 236 81.10 55 74.32 46 79.31 Do you think there should be some workshops/seminars/webinars on robotic teaching? Male 65 21.89 9 13.85 12 19.67 2.15 0.34 Female 232 78.11 56 86.15 49 80.33 Regarding prior exposure to robotic teaching, 70.2% of participants had already attended one or two educational sessions. The full data on these sessions is illustrated in Figure 2.     Figure 2: Robotic teaching education sessions attended by studied students

The demographic analysis of this study revealed that the majority of participants (72.1%) were between 18-22 years of age, with females representing a significant proportion (79.67%). This suggests a strong engagement with emerging technologies, including robotic teaching, particularly among young women. Similar global trends have been reported; for instance, research from the United States shows that young girls increasingly take part in robotics programming competitions, highlighting their growing confidence and interest in technological innovation [21]. Such enthusiasm is likely driven by broader access to STEM education and targeted initiatives that encourage female participation in technology-oriented fields.   Support for the inclusion of robotic teaching in higher education was expressed by 69.27% of respondents, reflecting students’ openness toward adopting innovative educational tools. This positive outlook can be attributed to an increased awareness of technology’s role in education, particularly in the post-pandemic era where digital platforms and hybrid models have become integral to learning. However, participants expressed divided opinions on whether robotic systems could entirely replace human instructors. These perspectives mirror findings in existing literature, which emphasize the efficiency, consistency and scalability of AI-driven teaching but simultaneously underscore the irreplaceable value of human educators in providing emotional intelligence, mentorship and adaptive guidance [22-24]. The observation that approximately 71% of students were interested in expanding their knowledge of robotic teaching indicates a genuine curiosity and readiness to embrace this evolving field. In Saudi Arabia, robotics has already been introduced at school levels, largely inspired by Vision 2030 initiatives that prioritize technology, innovation and youth empowerment. Although robotics is not yet a formal component of the national curriculum, initiatives and competitions are increasingly promoted to strengthen critical thinking, problem-solving and technological literacy among students [25-26]. This context may explain the strong interest observed among university students, who recognize the potential benefits of integrating robotic systems into higher education.   Additionally, the study found that 70.2% of respondents had attended one or two sessions on robotic teaching. Such exposure likely contributed to the relatively high awareness levels observed in this cohort. Early engagement with educational technologies helps demystify robotics, reduces apprehension and cultivates positive attitudes toward its use. Incorporating similar introductory experiences into higher education curricula could further enhance students’ preparedness and receptiveness to robotic teaching.   Overall, the findings suggest that students are highly receptive to robotic teaching, though cautious about the idea of fully replacing human educators. A hybrid model, where robotic systems complement rather than replace human instructors, may provide the most effective and balanced approach, combining the efficiency of AI with the mentorship, empathy and adaptability that only human teachers can offer.

This research provides important insights into university students’ perceptions of robotic teaching, highlighting both their awareness of its advantages and acknowledgment of its challenges. The findings underscore a strong interest in the subject, particularly among young female students and emphasize the need for further training opportunities in this emerging field.   Based on these results, higher education institutions are encouraged to design and implement structured training programs on robotic teaching to align with students’ interests and prepare them for future academic and professional demands. In parallel, policymakers should work toward developing frameworks that ensure the ethical, responsible and sustainable integration of robotics into education.   The study’s main limitations include the gender imbalance of participants and its single-institution scope. To strengthen the evidence base, future research should involve more balanced and diverse student populations across multiple universities, thereby enhancing generalizability and capturing a broader range of perspectives.   Strengths A key strength of this study lies in its focus on a highly relevant and timely topic, exploring the integration of robotic teaching in higher education. The research also benefits from a relatively large sample size, which enhances the robustness of its findings. Another strength is the inclusion of participants from different faculties and academic years, ensuring a more comprehensive and representative overview of student perspectives.   Limitations Despite its contributions, the study has some limitations. The reliance on self-reported data introduces the possibility of response bias, as participants’ answers may not always reflect their true knowledge or attitudes. Additionally, the notable gender imbalance within the sample restricts the generalizability of the results. Finally, as this was a single-institution study, the findings may not be fully applicable to other universities or regions with different contexts and educational systems.   Acknowledgement The authors gratefully acknowledge the Deanship of Scientific Research at Northern Border University, Arar, Saudi Arabia, for funding this work through project number: NBU-FFR-2024-1301-02.   Ethical Statement Ethical approval was obtained from the Local Committee of Bioethics at Northern Border University (Approval No: 112/23/H). Informed consent was secured from all participants, with assurances of confidentiality, anonymity and voluntary participation. Participants were informed of their right to withdraw at any stage and all data were handled in compliance with strict security and privacy protocols.