Journals / Participatory Educational Research / 2022 / Cilt: 9 - Sayı: 2

Determining middle school students' perceptions of the concept of artificial intelligence: A metaphor analysis

Pages
297–312
Publication Date
2022-03-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Teachers can consider students' perceptions of AI by using AI-assisted teaching and designing content to enhance students' learning skills.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Apart from the fact that human-like robots are still one of the most interesting topics in science fiction, artificial intelligence (AI) continues to develop rapidly as a popular phenomenon for all sectors.
📊
Key Findings & Data:The sample consisted of 339 seventh and eighth grade students of four secondary schools in the central districts of Afyonkarahisar and Izmir in the 2019-2020 academic year.
💡
Conclusion & Impact:Teachers can consider students' perceptions of AI by using AI-assisted teaching and designing content to enhance students' learning skills.
🔬Design:Research Article
📍Scope:2019-2020 Verileri
⚙️Variables:artificial intelligence · students · secondary education

Abstract

Apart from the fact that human-like robots are still one of the most interesting topics in science fiction, artificial intelligence (AI) continues to develop rapidly as a popular phenomenon for all sectors. Although the idea that this rapid rise of AI means the rise of humanity has been voiced by many, the point of how AI will affect humanity continues to raise doubts in certain parts of the society. In this study, it is aimed to determine the perceptions of middle school students, which are a part of the future of humanity, towards the concept of AI, on which many discussions have been made, through metaphors. The sample consisted of 339 seventh and eighth grade students of four secondary schools in the central districts of Afyonkarahisar and Izmir in the 2019-2020 academic year. This study used a qualitative approach utilizing metaphor analysis as a research tool to investigate phenomena. Participants were asked the complete the sentence “Artificial intelligence is like.................., because ..................” Data were analyzed using content analysis. Participants’ metaphors for AI were grouped under 11 categories: smart, brain, nature, security, humanistic, the dilemma of good and evil, service, object, technology, life, and time. The data obtained showed that the participants generally used positive metaphors to describe AI, that is, they had positive perceptions about AI. However, in this study, which focused on the AI perceptions of middle school students, all of the metaphors collected under 11 conceptual categories containing positive and negative perceptions were examined and interpreted separately. It is thought that educational computer systems can be designed to shape students' perceptions of AI. Teachers can consider students' perceptions of AI by using AI-assisted teaching and designing content to enhance students' learning skills.

🔖

Cite This Article

DEMİR, K., GÜRAKSIN, G. E. (2022). Determining middle school students' perceptions of the concept of artificial intelligence: A metaphor analysis. Participatory Educational Research, Cilt: 9 - Sayı: 2, 297–312. https://ajindex.com/articles/720608

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Arauja, T., Helberger, N., Kruikemeier, S. & Vreese, C.H. (2020). In AI we trust? Perceptions about automated decision‐making by artificial intelligence. AI & Society, 35: 611-623.
  2. Bostrom, N. (2003). Ethical issues in advanced artificial intelligence. Science Fiction and Philosophy: From Time Travel to Superintelligence, 277-284.
  3. Bostrom, N. & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish & W. Ramsey (Eds.), The Cambridge Handbook of Artificial Intelligence (pp. 316-334). Cambridge: Cambridge University Press. doi:10.1017/CBO9781139046855.020. Bösch, P. M., Becker, F., Becker, H. & Axhausen, K. W. (2018). Cost-based analysis of autonomous mobility services. Transport Policy, 64, 76-91.
  4. Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the ‘good society’: The US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505–528
  5. Cellan-Jones, R. (2014, December 2). Stephen Hawking warns artificial intelligence could end mankind, BBC. Retrieved 15.10.2020 from https://www.bbc.com/news/technology-30290540
  6. Chassignol, M., Khoroshavin, A., Klimova, A. & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24.
  7. Clarke, R. (2019). Why the world wants controls over Artificial Intelligence. Computer Law & Security Review. Cross, A. & Lucas, E. (2019). Getting into Medical School 2020
  8. Entry. Trotman Education. Dickson, B. (2017, May 12). What is Narrow, General and Super Artificial Intelligence. TechTalks. Retrieved 15.10.2020 from https://bdtechtalks.com/2017/05/12/what-is-narrow-general-and-super-artificial-intelligence
  9. Dubhashi, D. & Lappin, S. (2017). AI dangers: Imagined and real. Communications of the ACM, 60(2), 43-45.
  10. Elahi, E., Weijun, C., Zhang, H. & Nazeer, M. (2019). Agricultural intensification and damages to human health in relation to agrochemicals: Application of artificial intelligence. Land Use Policy, 83, 461-474.
  11. Gelmereanu, C., Morar, L. & Bogdan, S. (2014). Productivity and cycle time prediction using artificial neural network. Procedia Economics and Finance, 15, 1563-1569.
  12. Hajian, S., Bonchi, F., & Castillo, C. (2016). Algorithmic bias: from discrimination discovery to fairness-aware data mining. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD’16. ACM Press,
  13. San Francisco, California, USA, pp 2125–2126. https:// doi.org/10.1145/2