Open Access Peer-reviewed Research Article

Exploring teachers' artificial intelligence awareness

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Submitted   Apr 9, 2024
Published   Jul 8, 2024
Issue    Vol 4 No 2 (2024)
DOI   10.25082/AMLER.2024.02.004

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Derya Uygun corresponding author
National Ministry of Education, Eskisehir, Turkey
Işıl Aktaş
National Ministry of Education, Eskisehir, Turkey
İsmail Duygulu
National Ministry of Education, Eskisehir, Turkey
Numan Köseer
National Ministry of Education, Eskisehir, Turkey

Abstract

The impact of artificial intelligence (AI) technological advancements is reshaping various aspects of our daily lives, including education. Integrating AI in education offers advantages such as personalized learning and operational efficiency. However, educators need to be aware of AI's implications in education. Teachers must enhance their awareness and knowledge levels to adapt to the educational environment where AI technologies are becoming increasingly prevalent. Therefore, this research aims to assess teachers' AI awareness levels and investigate whether AI awareness varies based on age, graduation status, and years of experience. This study used data collected from 147 educators using the Teachers' Artificial Intelligence Awareness Scale. The results indicated that teachers' AI awareness was at a moderate level. Additionally, the study examined teachers' AI awareness across different variables. Independent sample t-tests and one-way ANOVA analyses determined teachers' AI awareness variation based on age. The research findings suggest that younger educators and those with higher academic qualifications have more excellent practical knowledge of AI. The study's limitations included a relatively small sample size and the assumption of accurate participant responses. Despite these limitations, understanding teachers' AI awareness levels is a foundation for developing educational programs related to AI. By understanding teachers' perceptions and knowledge of AI, tailored interventions and training initiatives can enhance educators' proficiency in effectively utilizing AI technologies within educational settings.

Keywords
artificial intelligence awareness, quantitative analysis, educational technology

Article Details

How to Cite
Uygun, D., Aktaş, I., Duygulu, İsmail, & Köseer, N. (2024). Exploring teachers’ artificial intelligence awareness. Advances in Mobile Learning Educational Research, 4(2), 1093-1104. https://doi.org/10.25082/AMLER.2024.02.004
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Adams, C., Pente, P., Lemermeyer, G., Turville, J., & Rockwell, G. (2022). Artificial Intelligence and Teachers’ New Ethical Obligations. The International Review of Information Ethics, 31(1). https://doi.org/10.29173/irie483
  2. AlKanaan, H. M. N. (2022). Awareness Regarding the Implication of Artificial Intelligence in Science Education among Pre-Service Science Teachers. International Journal of Instruction, 15(3), 895–912. https://doi.org/10.29333/iji.2022.15348a
  3. Athanassopoulos, S., Manoli, P., Gouvi, M., Lavidas, K., & Komis, V. (2023). The use of ChatGPT as a learning tool to improve foreign language writing in a multilingual and multicultural classroom. Advances in Mobile Learning Educational Research, 3(2), 818–824. https://doi.org/10.25082/amler.2023.02.009
  4. Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1). https://doi.org/10.1186/s40561-023-00269-3
  5. 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
  6. Chounta, I.-A., Bardone, E., Raudsep, A., & Pedaste, M. (2021). 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
  7. Du, H., Sun, Y., Jiang, H., Islam, A. Y. M. A., & Gu, X. (2024). Exploring the effects of AI literacy in teacher learning: an empirical study. Humanities and Social Sciences Communications, 11(1). https://doi.org/10.1057/s41599-024-03101-6
  8. Ferikoğlu, D., & Akgün, E. (2022). An Investigation of Teachers’ Artificial Intelligence Awareness: A Scale Development Study. Malaysian Online Journal of Educational Technology, 10(3), 215–231. https://doi.org/10.52380/mojet.2022.10.3.407
  9. Field, A. (2005). Discovering statistics using spss: And sex and drugs and rock `n' roll (2nd Edition). London: Sage Publications.
  10. Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez, J.-M. (2019). Artificial Intelligence in Higher Education: A Bibliometric Study on its Impact in the Scientific Literature. Education Sciences, 9(1), 51. https://doi.org/10.3390/educsci9010051
  11. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
  12. Huck, S. W. (2012). Reading statistics and research (6th ed.). Boston, MA. Pearson Education.
  13. İpek, Z. H., Gözüm, A. İ. C., Papadakis, S., & Kallogiannakis, M. (2023). Educational Applications of the ChatGPT AI System: A Systematic Review Research. Educational Process International Journal, 12(3), 26-55. https://doi.org/10.22521/edupij.2023.123.2
  14. Jarrell, M. G. (1994). A comparison of two procedures, the Mahalanobis distance, and the andrews-pregibon statistic, for identifying multivariate outliers. Research in the Schools, 1, 49-58.
  15. Karakose, T., Demirkol, M., Aslan, N., Köse, H., & Yirci, R. (2023). A Conversation with ChatGPT about the Impact of the COVID-19 Pandemic on Education: Comparative Review Based on Human–AI Collaboration. Educational Process International Journal, 12(3). https://doi.org/10.22521/edupij.2023.123.1
  16. Karakose, T., Polat, H., Yirci, R., Tülübaş, T., Papadakis, S., Ozdemir, T. Y., & Demirkol, M. (2023). Assessment of the Relationships between Prospective Mathematics Teachers’ Classroom Management Anxiety, Academic Self-Efficacy Beliefs, Academic Amotivation and Attitudes toward the Teaching Profession Using Structural Equation Modelling. Mathematics, 11(2), 449. https://doi.org/10.3390/math11020449
  17. Khabib, S. (2022). Introducing artificial intelligence (AI)-based digital writing assistants for teachers in writing scientific articles. Teaching English as a Foreign Language Journal, 1(2), 114–124. https://doi.org/10.12928/tefl.v1i2.249
  18. Mertler, C. A., & Vannatta, R. A. (2005). Advanced and multivariate statistical methods. California: Pyrczak.
  19. Ministry of National Education. (2021). Türkiye Milli Eğitim Bakanlığı İstatistikleri. https://www.meb.gov.tr
  20. Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
  21. Pallant, J. (2012). SPSS survival manual: A step-by-step guide to data analysis using SPSS (Version 12). Australia: National Library of Australia Cataloguing-in-Publication.
  22. Pörn, R., Braskén, M., Wingren, M., & Andersson, S. (2024). Attitudes towards and expectations on the role of artificial intelligence in the classroom among digitally skilled Finnish K-12 mathematics teachers. LUMAT: International Journal on Math, Science and Technology Education, 12(3). https://doi.org/10.31129/lumat.12.3.2102
  23. Rasmussen, J. L. (1988). Evaluating Outlier Identification Tests: Mahalanobis D Squared and Comrey Dk. Multivariate Behavioral Research, 23(2), 189–202. https://doi.org/10.1207/s15327906mbr2302_4
  24. Robinson, O. C. (2013). Sampling in Interview-Based Qualitative Research: A Theoretical and Practical Guide. Qualitative Research in Psychology, 11(1), 25–41. https://doi.org/10.1080/14780887.2013.801543
  25. Shara, A. M., Andriani, D., Ningsih, A. W., & Shinoda, K. (2020). Correlating Reading Literacy and Writing Literacy in Junior High School Pematangsiantar. Journal of English Education, 5(2), 72–85. https://doi.org/10.31327/jee.v5i2.1249
  26. Research on the Current Situation of Artificial Intelligence Literacy of Teacher Trainees and Strategies to Improve It. (2024). Advances in Educational Technology and Psychology, 8(1). https://doi.org/10.23977/aetp.2024.080116
  27. Simhadri, N., & Swamy, T. N. V. R. (2023). Awareness among teaching on AI and ML applications based on fuzzy in education sector at USA. Soft Computing. https://doi.org/10.1007/s00500-023-08329-z
  28. Song, J., Zhang, L., Yu, J., Peng, Y., Ma, A., & Lu, Y. (2022). Paving the Way for Novices: How to Teach AI for K-12 Education in China. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12852–12857. https://doi.org/10.1609/aaai.v36i11.21565
  29. Strzelecki, A., & ElArabawy, S. (2024). Investigation of the moderation effect of gender and study level on the acceptance and use of generative AI by higher education students: Comparative evidence from Poland and Egypt. British Journal of Educational Technology, 55(3), 1209–1230. Portico. https://doi.org/10.1111/bjet.13425
  30. Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (3rd ed.). New York Harper Collins.
  31. Trochim, W. M., & Donnelly, J. P. (2006). The research methods knowledge base (3rd ed.). Cincinnati, OH: Atomic Dog.
  32. Tülübaş, T., Karakose, T., & Papadakis, S. (2023). A Holistic Investigation of the Relationship between Digital Addiction and Academic Achievement among Students. European Journal of Investigation in Health, Psychology and Education, 13(10), 2006–2034. https://doi.org/10.3390/ejihpe13100143
  33. Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: a systematic review from 2011 to 2021. International Journal of STEM Education, 9(1). https://doi.org/10.1186/s40594-022-00377-5
  34. Yau, K. W., CHAI, C. S., Chiu, T. K. F., Meng, H., King, I., & Yam, Y. (2022). A phenomenographic approach on teacher conceptions of teaching Artificial Intelligence (AI) in K-12 schools. Education and Information Technologies, 28(1), 1041–1064. https://doi.org/10.1007/s10639-022-11161-x
  35. 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(1). https://doi.org/10.1186/s41239-019-0171-0
  36. Zhang, L. (2023). Optimal Processing of English Education Model Based on Artificial Intelligence Technology. Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022), 489–494. https://doi.org/10.2991/978-94-6463-058-9_80
  37. Zhao, L., Wu, X., & Luo, H. (2022). Developing AI Literacy for Primary and Middle School Teachers in China: Based on a Structural Equation Modeling Analysis. Sustainability, 14(21), 14549. https://doi.org/10.3390/su142114549
  38. Zormanová, L. (2024). The Attitudes of Czech Teachers Towards the Use of Artificial Intelligence in Schools. Horyzonty Wychowania, 23(65), 31–41. https://doi.org/10.35765/hw.2024.2365.05