Exploring Middle School Students’ Perceptions, Attitudes, and Opinions Toward Artificial Intelligence
DOI:
https://doi.org/10.46328/ijemst.5738Keywords:
Artificial intelligence (AI), student perceptions, attitudes toward technology, middle school education, mixed-methods researchAbstract
The main aim of this study is to examine the perceptions, attitudes, and opinions of seventh and eighth grade middle school students toward artificial intelligence (AI). A mixed-methods approach, combining both quantitative and qualitative data, was employed in the research. The study group consists of a total of 168 students enrolled in the seventh and eighth grades at a middle school located in the Bozüyük district of Bilecik province during the 2024–2025 academic year. Quantitative data were collected using Artificial Intelligence Perception and Attitude Scale. For the qualitative dimension, a semi-structured interview form was used to gain deeper insights into students' views. The analysis of the scale data revealed that students' perceptions and attitudes toward AI were at a moderate level, while their negative perceptions were found to be low. No statistically significant difference was observed based on gender; however, a significant difference favoring seventh-grade students was found when compared to eighth graders. Qualitative findings indicated that students generally held highly positive views toward artificial intelligence. Students emphasized that AI facilitates access to information, improves efficiency, saves time, and contributes to individuals’ design and creativity skills. It was also found that students used AI tools in educational contexts for purposes such as completing homework, conducting research, summarizing data, reviewing subjects, and solving problems. On the other hand, some concerns regarding artificial intelligence were also identified. These included the potential disappearance of certain professions, a decline in creativity, threats to data privacy, dependency on AI tools, and reduced opportunities for socialization.
References
Adıgüzel, O. C. (2019). Eğitim programlarının geliştirilmesinde ihtiyaç analizi el kitabı (2. baskı). Anı Yayıncılık.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Akbay, B., & Yıldırır, H. E. (2024). Ortaokul ve lise öğrencilerinin yapay zekâya yönelik metaforlarının karşılaştırmalı olarak incelenmesi. International Journal of Computers in Education, 7(2), 118–132.
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7
Aksakal, Ş., Emre, İ., & Özbek, M. (2024). Sınıf Öğretmenlerinin Yapay Zekaya İlişkin Tutumlarının Belirlenmesi. Eğitimde Yeni Yaklaşımlar Dergisi, 7(1), 1-13. https://izlik.org/JA37YU94PN
Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2016). Bilimsel araştırma yöntemleri (22. baskı). Pegem Akademi.
Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
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
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
Dinler, H. (2025). Development of the artificial intelligence perception and attitude scale (AIPAS): Validity and reliability study. Bartın University Journal of Faculty of Education, 14(4), 1283-1304. https://doi.org/10.14686/buefad.1602673
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Eker, C., & Halıcı Gürbüz, S. (2024). Matematik öğretmenlerinin matematik dersinde yapay zekâ kullanımına yönelik yeterlilik algıları. Sosyal, Beşeri ve İdari Bilimler Dergisi, 7(7), 513–528.
Elçiçek, M. (2024). Öğrencilerin yapay zeka okuryazarlığı üzerine bir inceleme [A study on students' artificial intelligence literacy]. Bilgi ve İletişim Teknolojileri Dergisi, 6(1), 24–35. https://doi.org/10.53694/bited.1460106
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Karasar, N. (2005). Bilimsel araştırma yöntemi (15. baskı). Nobel Yayın Dağıtım.
Kayış, A. (2014). Güvenirlik katsayısı olarak Cronbach alfa. İçinde Ş. Kalaycı (Ed.), SPSS uygulamalı çok değişkenli istatistik teknikleri. Asil Yayın Dağıtım.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
Merriam, S. B. (2015). Nitel araştırma: Desen ve uygulama için bir rehber (S. Turan, Çev. Ed.). Nobel Yayıncılık. (Özgün eser 2009'da yayımlanmıştır)
Oruç, T., Korkmaz, O., & Kurt, M. (2024). Primary school students' views on artificial intelligence. International Journal of Technology in Education and Science (IJTES), 8(4), 583–601. https://doi.org/10.46328/ijtes.577
Schunk, D. H. (2020). Learning theories: An educational perspective (8th ed.). Pearson.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Learning, Media and Technology, 44(2), 109–123. https://doi.org/10.1080/17439884.2019.1666281
Suh, W., & Ahn, S. (2022). Development and validation of a scale measuring student attitudes toward artificial intelligence. SAGE Open, 12(2), 1–12. https://doi.org/10.1177/21582440221100463
Teo, T. (2011). Technology acceptance research in education. In T. Teo (Ed.), Technology acceptance in education (pp. 1–16). SensePublishers. https://doi.org/10.1007/978-94-6091-487-4_1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Williamson, B. (2017). Big data in education: The digital future of learning, policy and practice. SAGE Publications.
Yıldırım, A., & Şimşek, H. (2021). Sosyal bilimlerde nitel araştırma yöntemleri (12. baskı). Seçkin Yayıncılık.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Education in Mathematics, Science and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Articles may be used for research, teaching, and private study purposes. Authors alone are responsible for the contents of their articles. The journal owns the copyright of the articles. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of the research material.
The author(s) of a manuscript agree that if the manuscript is accepted for publication in the journal, the published article will be copyrighted using a Creative Commons “Attribution 4.0 International” license. This license allows others to freely copy, distribute, and display the copyrighted work, and derivative works based upon it, under certain specified conditions.
Authors are responsible for obtaining written permission to include any images or artwork for which they do not hold copyright in their articles, or to adapt any such images or artwork for inclusion in their articles. The copyright holder must be made explicitly aware that the image(s) or artwork will be made freely available online as part of the article under a Creative Commons “Attribution 4.0 International” license.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
