Urban–Rural differences in teachers’ acceptance of artificial intelligence for teaching and learning: Evidence from indonesia using the technology acceptance model
DOI:
https://doi.org/10.58524/jasme.v6i2.1280Keywords:
Artificial Intelligence, Educational Technology, Teacher Acceptance, Technology Acceptance Model, Urban–Rural SchoolsAbstract
Background: Artificial Intelligence (AI) offers significant opportunities to enhance teaching and learning through instructional support, automated assessment, and learning analytics. However, teachers’ acceptance of AI may vary according to perceived usefulness, ease of use, and differences in technological access across geographical settings.
Aim: This study aimed to compare teachers’ acceptance of AI for teaching and learning between urban and rural schools in Melawi Regency, Indonesia.
Method: A quantitative comparative design was employed using the Technology Acceptance Model (TAM), encompassing Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), and Behavioral Intention (BI). Data were collected from 100 teachers through an online Likert-scale questionnaire. Content validity was established through expert review, and the data were analyzed using descriptive statistics and an independent samples t-test.
Results: The findings indicated that teachers in both settings demonstrated high levels of AI acceptance. However, urban teachers reported higher acceptance (M = 4.22) than rural teachers (M = 4.05). The difference was statistically significant (t = 7.35, p < 0.001) with a very large effect size (Cohen’s d = 2.32), suggesting a substantial influence of geographical context on AI acceptance.
Conclusion: Urban teachers exhibit significantly greater acceptance of AI than rural teachers. Infrastructure availability, digital literacy, and institutional support appear to be key factors influencing this disparity. Strengthening digital capacity and improving technological infrastructure are essential to promote equitable AI integration in education.
References
Abrar, M., Aboraya, W., Khaliq, R., Subramanian, K., Al Husaini, Y., & Abdulla, M. (2025). AI-powered learning pathways: Personalized learning and dynamic assessments. International Journal of Advanced Computer Science and Applications, 16. https://doi.org/10.14569/IJACSA.2025.0160145
Ahiaku, P. K. A., Uleanya, C., & Muyambi, G. C. (2025). Rural schools and tech use for sustainability: The challenge of disconnection. Education and Information Technologies, 30(9), 12557–12571. https://doi.org/10.1007/s10639-024-13311-9
Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, Article 100132. https://doi.org/10.1016/j.caeai.2023.100132
Aldraiweesh, A. A., & Alturki, U. (2025). The influence of social support theory on AI acceptance: Examining educational support and perceived usefulness using SEM analysis. IEEE Access, 13, 18366–18385. https://doi.org/10.1109/ACCESS.2025.3534099
Aleksieva, L., Racheva, V., & Peytcheva-Forsyth, R. (2025). Talking tech, teaching with tech: How primary teachers implement digital technologies in practice. Informatics, 12(3). https://doi.org/10.3390/informatics12030099
Ali, I., Warraich, N. F., & Butt, K. (2025). Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction. Information Development, 41(3), 859–874. https://doi.org/10.1177/02666669241257206
Al-kfairy, M. (2024). Factors impacting the adoption and acceptance of ChatGPT in educational settings: A narrative review of empirical studies. Applied System Innovation, 7(6). https://doi.org/10.3390/asi7060110
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Al-Otaibi, S., Sawafi, O. S. A., Al-Maroof, R. S., Lutfi, A., Alrawad, M., Mulhem, A. A., & Awad, A. B. (2022). Determinants influencing the continuous intention to use digital technologies in higher education. Electronics, 11(18). https://doi.org/10.3390/electronics11182827
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11). https://doi.org/10.1016/j.heliyon.2024.e31887
Al-Mughairi, H., & Bhaskar, P. (2024). Exploring the factors affecting the adoption AI techniques in higher education: Insights from teachers’ perspectives on ChatGPT. Journal of Research in Innovative Teaching & Learning, 18(2), 232–247. https://doi.org/10.1108/JRIT-09-2023-0129
Almuhanna, M. A. (2025). Teachers’ perspectives of integrating AI-powered technologies in K–12 education for creating customized learning materials and resources. Education and Information Technologies, 30(8), 10343–10371. https://doi.org/10.1007/s10639-024-13257-y
Alwaqdani, M. (2025). Investigating teachers’ perceptions of artificial intelligence tools in education: Potential and difficulties. Education and Information Technologies, 30(3), 2737–2755. https://doi.org/10.1007/s10639-024-12903-9
An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187–5208. https://doi.org/10.1007/s10639-022-11286-z
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, Article 100099. https://doi.org/10.1016/j.caeai.2022.100099
Babu J, M., Virgin B, A., Rs Edwin, M., Priya P, G., & Ravichandran, K. (2024). Identifying learning difficulties at an early stage in education with the help of artificial intelligence models and predictive analytics. International Research Journal of Multidisciplinary Scope, 5(4), 1455–1461. https://doi.org/10.47857/irjms.2024.v05i04.01821
Caffaro, F., Micheletti Cremasco, M., Roccato, M., & Cavallo, E. (2020). Drivers of farmers’ intention to adopt technological innovations in Italy: The role of information sources, perceived usefulness, and perceived ease of use. Journal of Rural Studies, 76, 264–271. https://doi.org/10.1016/j.jrurstud.2020.04.028
Castro, A., Díaz, B., Aguilera, C., Prat, M., & Chávez-Herting, D. (2025). Identifying rural elementary teachers’ perception challenges and opportunities in integrating artificial intelligence in teaching practices. Sustainability, 17(6). https://doi.org/10.3390/su17062748
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Cheung, S. K. S., Kwok, L. F., Phusavat, K., & Yang, H. H. (2021). Shaping the future learning environments with smart elements: Challenges and opportunities. International Journal of Educational Technology in Higher Education, 18(1), Article 16. https://doi.org/10.1186/s41239-021-00254-1
Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human-Computer Interaction, 39(4), 910–922. https://doi.org/10.1080/10447318.2022.2049145
Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring teachers’ perceptions of artificial intelligence as a tool to support their practice in Estonian K–12 education. International Journal of Artificial Intelligence in Education, 32(3), 725–755. https://doi.org/10.1007/s40593-021-00243-5
Christopoulos, A., & Sprangers, P. (2021). Integration of educational technology during the Covid-19 pandemic: An analysis of teacher and student receptions. Cogent Education, 8(1), Article 1964690. https://doi.org/10.1080/2331186X.2021.1964690
Cioc, M. M., Popa, Ș. C., Olariu, A. A., Popa, C. F., & Nica, C.-B. (2023). Behavioral intentions to use energy efficiency smart solutions under the impact of social influence: An extended TAM approach. Applied Sciences, 13(18). https://doi.org/10.3390/app131810241
Dubey, P., & Sahu, K. K. (2021). Investigating various factors that affect students’ adoption intention to technology-enhanced learning. Journal of Research in Innovative Teaching & Learning, 15(1), 110–131. https://doi.org/10.1108/JRIT-07-2021-0049
Feng, J., Yu, B., Tan, W. H., Dai, Z., & Li, Z. (2025). Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digital Health, 4(4), Article e0000764. https://doi.org/10.1371/journal.pdig.0000764
Feurich, M., Kourilova, J., Pelucha, M., & Kasabov, E. (2024). Bridging the urban-rural digital divide: Taxonomy of the best practice and critical reflection of the EU countries’ approach. European Planning Studies, 32(3), 483–505. https://doi.org/10.1080/09654313.2023.2186167
Galindo-Domínguez, H., Delgado, N., Losada, D., & Etxabe, J.-M. (2024). An analysis of the use of artificial intelligence in education in Spain: The in-service teacher’s perspective. Journal of Digital Learning in Teacher Education, 40(1), 41–56. https://doi.org/10.1080/21532974.2023.2284726
Granić, A. (2022). Educational technology adoption: A systematic review. Education and Information Technologies, 27(7), 9725–9744. https://doi.org/10.1007/s10639-022-10951-7
Graves, J. M., Abshire, D. A., Amiri, S., & Mackelprang, J. L. (2021). Disparities in technology and broadband internet access across rurality: Implications for health and education. Family & Community Health, 44(4), 257–265. https://doi.org/10.1097/FCH.0000000000000306
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100001. https://doi.org/10.1016/j.caeai.2020.100001
Inegbedion, H. E. (2021). Digital divide in the major regions of the world and the possibility of convergence. The Bottom Line, 34(1), 68–85. https://doi.org/10.1108/BL-09-2020-0064
Johler, M., Krumsvik, R. J., Bugge, H. E., & Helgevold, N. (2022). Teachers’ perceptions of their role and classroom management practices in a technology rich primary school classroom. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.841385
Karolčík, Š., & Marková, M. (2023). How teachers perceive innovations in education. Journal of Research in Innovative Teaching & Learning, 18(1), 39–55. https://doi.org/10.1108/JRIT-04-2023-0039
Khan, I., Ahmad, A. R., Jabeur, N., & Mahdi, M. N. (2021). An artificial intelligence approach to monitor student performance and devise preventive measures. Smart Learning Environments, 8(1), Article 17. https://doi.org/10.1186/s40561-021-00161-y
Kong, S. C., Yang, Y., & Hou, C. (2024). Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model. Computers and Education: Artificial Intelligence, 7, Article 100328. https://doi.org/10.1016/j.caeai.2024.100328
Kormos, E. (2022). Technology as a facilitator in the learning process in urban high-needs schools: Challenges and opportunities. Education and Urban Society, 54(2), 146–163. https://doi.org/10.1177/00131245211004555
Li, M. (2025). Exploring the digital divide in primary education: A comparative study of urban and rural mathematics teachers’ TPACK and attitudes towards technology integration in post-pandemic China. Education and Information Technologies, 30(2), 1913–1945. https://doi.org/10.1007/s10639-024-12890-x
Lin, C.-C., Huang, A. Y. Q., & Lu, O. H. T. (2023). Artificial intelligence in intelligent tutoring systems toward sustainable education: A systematic review. Smart Learning Environments, 10(1), Article 41. https://doi.org/10.1186/s40561-023-00260-y
Liu, N. (2025). Exploring the factors influencing the adoption of artificial intelligence technology by university teachers: The mediating role of confidence and AI readiness. BMC Psychology, 13(1), Article 311. https://doi.org/10.1186/s40359-025-02620-4
Lythreatis, S., Singh, S. K., & El-Kassar, A.-N. (2022). The digital divide: A review and future research agenda. Technological Forecasting and Social Change, 175, Article 121359. https://doi.org/10.1016/j.techfore.2021.121359
Ma, S., & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44(1), 94–111. https://doi.org/10.1080/02188791.2024.2305155
Mailizar, M., Almanthari, A., & Maulina, S. (2021). Examining teachers’ behavioral intention to use e-learning in teaching of mathematics: An extended TAM model. Contemporary Educational Technology, 13(2), Article ep298. https://doi.org/10.30935/cedtech/9709
Moreno-Guerrero, A.-J., Rodríguez-Jiménez, C., Gómez-García, G., & Navas-Parejo, M. R. (2020). Educational innovation in higher education: Use of role playing and educational video in future teachers’ training. Sustainability, 12(6). https://doi.org/10.3390/su12062558
Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
Olanrewaju, G. S., Adebayo, S. B., Omotosho, A. Y., & Olajide, C. F. (2021). Left behind? The effects of digital gaps on e-learning in rural secondary schools and remote communities across Nigeria during the COVID-19 pandemic. International Journal of Educational Research Open, 2, Article 100092. https://doi.org/10.1016/j.ijedro.2021.100092
Ouyang, Y., Wang, K., Zhang, T., Peng, L., Song, G., & Luo, J. (2020). The influence of sports participation on body image, self-efficacy, and self-esteem in college students. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.03039
Peng, R., Razak, R. A., & Halili, S. H. (2023). Factors influencing in-service teachers’ technology integration model: Innovative strategies for educational technology. PLOS ONE, 18(8), Article e0286112. https://doi.org/10.1371/journal.pone.0286112
Primartadi, A., Suyitno, S., Abdillah, F., Kamin, Y., Riyadi, S., & Nugroho, S. (2026). Student acceptance of AI-driven deep learning tools: The influence of institutional support and teacher competency in vocational schools. European Journal of Educational Research, 15(2), 481–495. https://doi.org/10.12973/eu-jer.15.2.481
Raihan, M. M. H., Subroto, S., Chowdhury, N., Koch, K., Ruttan, E., & Turin, T. C. (2024). Dimensions and barriers for digital (in)equity and digital divide: A systematic integrative review. Digital Transformation and Society, 4(2), 111–127. https://doi.org/10.1108/DTS-04-2024-0054
Rydzewski, P. (2025). Digital inequality and sustainable development. Problemy Ekorozwoju, 20(1), 96–108. https://doi.org/10.35784/preko.6691
Schroeder, T., Dodds, L., Georgiou, A., Gewald, H., & Siette, J. (2023). Older adults and new technology: Mapping review of the factors associated with older adults’ intention to adopt digital technologies. JMIR Aging, 6(1), Article e44564. https://doi.org/10.2196/44564
Stumbrienė, D., Jevsikova, T., & Kontvainė, V. (2024). Key factors influencing teachers’ motivation to transfer technology-enabled educational innovation. Education and Information Technologies, 29(2), 1697–1731. https://doi.org/10.1007/s10639-023-11891-6
Timotheou, S., Miliou, O., Dimitriadis, Y., Sobrino, S. V., Giannoutsou, N., Cachia, R., Monés, A. M., & Ioannou, A. (2023). Impacts of digital technologies on education and factors influencing schools’ digital capacity and transformation: A literature review. Education and Information Technologies, 28(6), 6695–6726. https://doi.org/10.1007/s10639-022-11431-8
Trivedi, S. K., Patra, P., Srivastava, P. R., Kumar, A., & Ye, F. (2024). Exploring factors affecting users’ behavioral intention to adopt digital technologies: The mediating effect of social influence. IEEE Transactions on Engineering Management, 71, 13814–13826. https://doi.org/10.1109/TEM.2022.3182361
Unal, E., & Uzun, A. M. (2021). Understanding university students’ behavioral intention to use Edmodo through the lens of an extended technology acceptance model. British Journal of Educational Technology, 52(2), 619–637. https://doi.org/10.1111/bjet.13046
Wang, J., Qin, Z., Zhang, Q., & Wang, J. (2025). Lower limb dynamic balance, strength, explosive power, agility, and injuries in volleyball players. Journal of Orthopaedic Surgery and Research, 20(1), Article 211. https://doi.org/10.1186/s13018-025-05566-w
Wang, K., van Hemmen, S. F., & Criado, J. R. (2022). The behavioural intention to use MOOCs by undergraduate students: Incorporating TAM with TPB. International Journal of Educational Management, 36(7), 1321–1342. https://doi.org/10.1108/IJEM-11-2021-0446
Wang, Y. (2025). Artificial intelligence in student management systems to enhance academic performance monitoring and intervention. Scientific Reports, 15(1), Article 35122. https://doi.org/10.1038/s41598-025-19159-4
Yang, K., Choi, J., & Chung, J. (2021). Extending the Technology Acceptance Model (TAM) to explore customer’s behavioral intention to use Self-Service Technologies (SSTs) in Chinese budget hotels. Global Business & Finance Review, 26(1), 79–94. https://doi.org/10.17549/gbfr.2021.26.1.79
Yao, N., & Wang, Q. (2024). Retracted: Factors influencing pre-service special education teachers’ intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness. Heliyon, 10(14). https://doi.org/10.1016/j.heliyon.2024.e34894
Zhang, C., Hu, M., Wu, W., Kamran, F., & Wang, X. (2025). Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis. Education and Information Technologies, 30(2), 2645–2672. https://doi.org/10.1007/s10639-024-12905-7
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), Article 49. https://doi.org/10.1186/s41239-023-00420-7
Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871–1885. https://doi.org/10.1111/jcal.12988
Zhao, D. (2025). The impact of AI-enhanced natural language processing tools on writing proficiency: An analysis of language precision, content summarization, and creative writing facilitation. Education and Information Technologies, 30(6), 8055–8086. https://doi.org/10.1007/s10639-024-13145-5
Zhao, J., Li, S., & Zhang, J. (2025). Understanding teachers’ adoption of AI technologies: An empirical study from Chinese middle schools. Systems, 13(4). https://doi.org/10.3390/systems13040302
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