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

Year 2022, Volume 9, Issue 2, 297 - 312, 01.03.2022
https://doi.org/10.17275/per.22.41.9.2

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.

References

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  • Bösch, P. M., Becker, F., Becker, H. & Axhausen, K. W. (2018). Cost-based analysis of autonomous mobility services. Transport Policy, 64, 76-91.
  • 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
  • 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
  • Chassignol, M., Khoroshavin, A., Klimova, A. & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24.
  • 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 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
  • Dubhashi, D. & Lappin, S. (2017). AI dangers: Imagined and real. Communications of the ACM, 60(2), 43-45.
  • 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.
  • Gelmereanu, C., Morar, L. & Bogdan, S. (2014). Productivity and cycle time prediction using artificial neural network. Procedia Economics and Finance, 15, 1563-1569.
  • 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, San Francisco, California, USA, pp 2125–2126. https:// doi.org/10.1145/2939672.2945386.
  • Köse, U. (2018), Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety. Broad Research in Artificial Intelligence and Neuroscience, 9 (2), 184-197.
  • Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S. & Huber, P. (2016, October). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE.
  • Kuper, A., Lingard, L., & Levinson, W. (2008). Critically appraising qualitative research. BMJ, 337,a1035. https://doi.org/10.1136/bmj.a1035
  • Lee, E., Lee, Y., Kye, B. & Ko, B. (2008, June). Elementary and middle school teachers’, students’ and parents’ perception of robot-aided education in Korea. In EdMedia+ Innovate Learning (pp. 175-183). Association for the Advancement of Computing in Education (AACE).
  • Lincoln, Y.S., & Guba, E.G. (1985) Naturalistic Inquiry. Newbury Park, CA: Sage Publications.
  • Matua, G. A., & Van Der Wal, D. M. (2015). Differentiating between descriptive and interpretive phenomenological research approaches. Nurse researcher, 22(6).
  • Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C. C. (2020). Challenges and future directions of Big Data and Artificial Intelligence in education. Frontiers in Psychology, 11.
  • McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
  • Miles, M. B. & Huberman, A. M. (1994). Qualitative data analysis (Thousand Oaks, CA: Sage).
  • Nowak, A., Lukowicz, P. and Horodecki, P. (2018). Assessing Artificial Intelligence for Humanity: Will AI be the Our Biggest Ever Advance ? or the Biggest Threat [Opinion], in IEEE Technology and Society Magazine, vol. 37, no. 4, pp. 26-34, Dec. 2018, doi: 10.1109/MTS.2018.2876105.
  • O'Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine, 89(9), 1245e1251. https://doi.org/10.1097/ACM.0000000000000388.
  • Saban, A. (2008a). Primary School Teachers’ and Their Students’ Mental Images about the Concept of Knowledge. Elementary Education Online, 7(2), 421-455.
  • Saban, A. (2008b). Metaphors about School. Educational Administration – Theory and Practice, 14(3), 459-496.
  • Santiago-Delefosse, M., Gavin, A., Bruchez, C., Roux, P., & Stephen, S. L. (2016). Quality of qualitative research in the health sciences: Analysis of the common criteria present in 58 assessment guidelines by expert users. Social Science & Medicine, 148,142e151. https://doi.org/10.1016/j.socscimed.2015.11.007.
  • Singh, G., Mishra, A., Sagar, D. (2013). An Overview of Artificial Intelligence. SBIT Journal of Science and Technology. 2(1). 10.13140/RG.2.2.20660.19840.
  • Sinha, S. (2018, May 8) “China Publishes First AI Textbook To Educate High School Students” Analytics India. Retrieved 15.10.2020 from www.analyticsindiamag.com/china-publishes-first-ai-textbook-to-educate-high-school-students/
  • Sitterding, M. C., Raab, D. L., Saupe, J. L. & Israel, K. J. (2019). Using Artificial Intelligence and Gaming to Improve New Nurse Transition. Nurse Leader, 17(2), 125-130.
  • Skilton, M. & Hovsepian, F. (2017). The 4th Industrial Revolution: Responding to the Impact of Artificial Intelligence on Business. Springer.
  • Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473-486.
  • Tavana, M., Abtahi, A. R., Di Caprio, D. & Poortarigh, M. (2018). An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking. Neurocomputing, 275, 2525-2554.
  • Touger, G. E. (2018, August 3) “What’s the Difference between Artificial Learning (AI), Machine Learning, and Deep Learning?” Prowesscorp. Retrieved 15.10.2020 from www.prowesscorp.com/whats-the-difference-between-artificial-intelligence-ai-machine-learning-and-deep-learning
  • Twining, P., Heller, R. S., Nussbaum, M., & Tsai, C. C. (2017). Some guidance on conducting and reporting qualitative studies. Computers and Education, 106, A1-A9.
  • United Nations Children's Fund (UNICEF). (September, 2020). Policy guidance on AI for children Policy guidance on AI for children. Retrieved 15.10.2020 from https://www.unicef.org/globalinsight/media/1171/file/UNICEF-Global-Insight-policy-guidance-AI-children-draft-1.0-2020.pdf
  • World Bank. (2018). Better Data for Doing Good: Responsible Use of Big Data and Artificial Intelligence. In Information and Communications for Development. Information and Communications for Development 2018: Data-Driven Development (pp. 33–50). https://doi.org/doi:10.1596/978-1-4648-1325-2_ch3
  • Yıldırım, A., ve Şimşek, H. (2011). Nitel araştırma yöntemleri. (8. Baskı). Ankara: Seçkin Yayıncılık.
  • Yu, Y. & Chen, Y. (2018). Design and development of high school artificial intelligence textbook based on computational thinking. Open Access Library Journal, 5(9), 1-15.
  • Zinan, C. & Kai, G. (2018, November 22). “AI textbooks for Chinese kindergarteners released” China Daily. Retrieved 15.10.2020 from http://www.chinadaily.com.cn/a/201811/22/WS5bf66272a310eff30328a73a.html

Year 2022, Volume 9, Issue 2, 297 - 312, 01.03.2022
https://doi.org/10.17275/per.22.41.9.2

Abstract

References

  • 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.
  • Bostrom, N. (2003). Ethical issues in advanced artificial intelligence. Science Fiction and Philosophy: From Time Travel to Superintelligence, 277-284.
  • 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.
  • 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
  • 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
  • Chassignol, M., Khoroshavin, A., Klimova, A. & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24.
  • 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 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
  • Dubhashi, D. & Lappin, S. (2017). AI dangers: Imagined and real. Communications of the ACM, 60(2), 43-45.
  • 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.
  • Gelmereanu, C., Morar, L. & Bogdan, S. (2014). Productivity and cycle time prediction using artificial neural network. Procedia Economics and Finance, 15, 1563-1569.
  • 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, San Francisco, California, USA, pp 2125–2126. https:// doi.org/10.1145/2939672.2945386.
  • Köse, U. (2018), Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety. Broad Research in Artificial Intelligence and Neuroscience, 9 (2), 184-197.
  • Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S. & Huber, P. (2016, October). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE.
  • Kuper, A., Lingard, L., & Levinson, W. (2008). Critically appraising qualitative research. BMJ, 337,a1035. https://doi.org/10.1136/bmj.a1035
  • Lee, E., Lee, Y., Kye, B. & Ko, B. (2008, June). Elementary and middle school teachers’, students’ and parents’ perception of robot-aided education in Korea. In EdMedia+ Innovate Learning (pp. 175-183). Association for the Advancement of Computing in Education (AACE).
  • Lincoln, Y.S., & Guba, E.G. (1985) Naturalistic Inquiry. Newbury Park, CA: Sage Publications.
  • Matua, G. A., & Van Der Wal, D. M. (2015). Differentiating between descriptive and interpretive phenomenological research approaches. Nurse researcher, 22(6).
  • Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S. J., Ogata, H., Baltes, J., Guerra, R., Li, P., & Tsai, C. C. (2020). Challenges and future directions of Big Data and Artificial Intelligence in education. Frontiers in Psychology, 11.
  • McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
  • Miles, M. B. & Huberman, A. M. (1994). Qualitative data analysis (Thousand Oaks, CA: Sage).
  • Nowak, A., Lukowicz, P. and Horodecki, P. (2018). Assessing Artificial Intelligence for Humanity: Will AI be the Our Biggest Ever Advance ? or the Biggest Threat [Opinion], in IEEE Technology and Society Magazine, vol. 37, no. 4, pp. 26-34, Dec. 2018, doi: 10.1109/MTS.2018.2876105.
  • O'Brien, B. C., Harris, I. B., Beckman, T. J., Reed, D. A., & Cook, D. A. (2014). Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine, 89(9), 1245e1251. https://doi.org/10.1097/ACM.0000000000000388.
  • Saban, A. (2008a). Primary School Teachers’ and Their Students’ Mental Images about the Concept of Knowledge. Elementary Education Online, 7(2), 421-455.
  • Saban, A. (2008b). Metaphors about School. Educational Administration – Theory and Practice, 14(3), 459-496.
  • Santiago-Delefosse, M., Gavin, A., Bruchez, C., Roux, P., & Stephen, S. L. (2016). Quality of qualitative research in the health sciences: Analysis of the common criteria present in 58 assessment guidelines by expert users. Social Science & Medicine, 148,142e151. https://doi.org/10.1016/j.socscimed.2015.11.007.
  • Singh, G., Mishra, A., Sagar, D. (2013). An Overview of Artificial Intelligence. SBIT Journal of Science and Technology. 2(1). 10.13140/RG.2.2.20660.19840.
  • Sinha, S. (2018, May 8) “China Publishes First AI Textbook To Educate High School Students” Analytics India. Retrieved 15.10.2020 from www.analyticsindiamag.com/china-publishes-first-ai-textbook-to-educate-high-school-students/
  • Sitterding, M. C., Raab, D. L., Saupe, J. L. & Israel, K. J. (2019). Using Artificial Intelligence and Gaming to Improve New Nurse Transition. Nurse Leader, 17(2), 125-130.
  • Skilton, M. & Hovsepian, F. (2017). The 4th Industrial Revolution: Responding to the Impact of Artificial Intelligence on Business. Springer.
  • Song, P., & Wang, X. (2020). A bibliometric analysis of worldwide educational artificial intelligence research development in recent twenty years. Asia Pacific Education Review, 21(3), 473-486.
  • Tavana, M., Abtahi, A. R., Di Caprio, D. & Poortarigh, M. (2018). An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking. Neurocomputing, 275, 2525-2554.
  • Touger, G. E. (2018, August 3) “What’s the Difference between Artificial Learning (AI), Machine Learning, and Deep Learning?” Prowesscorp. Retrieved 15.10.2020 from www.prowesscorp.com/whats-the-difference-between-artificial-intelligence-ai-machine-learning-and-deep-learning
  • Twining, P., Heller, R. S., Nussbaum, M., & Tsai, C. C. (2017). Some guidance on conducting and reporting qualitative studies. Computers and Education, 106, A1-A9.
  • United Nations Children's Fund (UNICEF). (September, 2020). Policy guidance on AI for children Policy guidance on AI for children. Retrieved 15.10.2020 from https://www.unicef.org/globalinsight/media/1171/file/UNICEF-Global-Insight-policy-guidance-AI-children-draft-1.0-2020.pdf
  • World Bank. (2018). Better Data for Doing Good: Responsible Use of Big Data and Artificial Intelligence. In Information and Communications for Development. Information and Communications for Development 2018: Data-Driven Development (pp. 33–50). https://doi.org/doi:10.1596/978-1-4648-1325-2_ch3
  • Yıldırım, A., ve Şimşek, H. (2011). Nitel araştırma yöntemleri. (8. Baskı). Ankara: Seçkin Yayıncılık.
  • Yu, Y. & Chen, Y. (2018). Design and development of high school artificial intelligence textbook based on computational thinking. Open Access Library Journal, 5(9), 1-15.
  • Zinan, C. & Kai, G. (2018, November 22). “AI textbooks for Chinese kindergarteners released” China Daily. Retrieved 15.10.2020 from http://www.chinadaily.com.cn/a/201811/22/WS5bf66272a310eff30328a73a.html

Details

Primary Language English
Subjects Education and Educational Research
Journal Section Research Articles
Authors

Kadir DEMİR> (Primary Author)
Dokuz Eylül Üniversitesi
0000-0001-9568-9450
Türkiye


Gür Emre GÜRAKSIN>
AFYON KOCATEPE UNIVERSITY
0000-0002-1935-2781
Türkiye

Publication Date March 1, 2022
Published in Issue Year 2022, Volume 9, Issue 2

Cite

APA Demir, K. & Güraksın, G. E. (2022). Determining middle school students' perceptions of the concept of artificial intelligence: A metaphor analysis . Participatory Educational Research , 9 (2) , 297-312 . DOI: 10.17275/per.22.41.9.2