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Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education

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Abstract

In this article, we present a study on teachers’ perceptions about Artificial Intelligence (AI) as a tool to support teaching in Estonian K-12 education. Estonia is promoting technological innovation in education. According to the Index of Readiness for Digital Lifelong Learning (IRDLL), Estonia was ranked first among 27 European countries. In this context, our goal was to explore teachers’ perceptions about cutting-edge technologies (in this case, AI) and to contextualize our results in the scope of Fairness, Accountability, Transparency and Ethics (FATE). We carried out a survey with 140 Estonian K-12 teachers and we asked them about their understanding and concerns regarding the use of AI in education and the challenges they face. The analysis of the survey responses suggests that teachers have limited knowledge about AI and how it could support them in practice. Nonetheless, they perceive it as an opportunity for education. The results indicate that teachers need support in order to be efficient and effective in their work practice; we envision that AI can be used to provide this support. Furthermore, we identified challenges that relate to the socio-cultural context of the study: for example, teachers perceived AI as a tool to support them in accessing, adapting and using multilingual content. To conclude, we discuss the findings of this work in relation to ethical AI, and elaborate on the implications and future aspects of this work in the context of FATE and participatory design of learning environments.

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Notes

  1. https://e-estonia.com/estonias-next-scholastic-leap-eesti-2-0/

  2. https://www.ceps.eu/ceps-publications/index-of-readiness-for-digital-lifelong-learning/

  3. https://kompass.hitsa.ee/tehisintellekt

  4. https://scratch.mit.edu/

  5. http://www.knewton.com

  6. https://www.ibm.com/blogs/watson/2018/06/using-ai-to-close-learning-gap/

  7. https://www.edsurge.com/news/2017-03-16-what-does-it-mean-to-prepare-students-for-a-future-with-artificial-intelligence

  8. http://simon.buckinghamshum.net/2020/07/should-predictive-models-of-student-outcome-be-colour-blind/

  9. https://www.hitsa.ee/en

  10. https://kompass.hitsa.ee/tehisintellekt

References

  • Aiken, R.M., & Epstein, R.G. (2000). Ethical guidelines for ai in education: starting a conversation. International Journal of Artificial Intelligence in Education, 11, 163–176.

    Google Scholar 

  • Albacete, P., Jordan, P., Katz, S., Chounta, I.A., & McLaren, B.M. (2019). The impact of student model updates on contingent scaffolding in a natural-language tutoring system. In International conference on artificial intelligence in education (pp. 37–47): Springer.

  • Bridle, J. (2018). New dark age: technology and the end of the future. Verso Books.

  • Bulger, M. (2016). Personalized learning: the conversations we’re not having. Data and Society, 22(1).

  • Chalvatza, F., Karkalas, S., & Mavrikis, M. (2019). Communicating learning analytics: stakeholder participation and early stage requirement analysis. In CSEDU 2019-proceedings of the 11th international conference on computer supported education, (Vol. 2 pp. 339–346): SCITEPRESS–Science and Technology Publications.

  • Chi, M.T., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152.

    Article  Google Scholar 

  • Chounta, I.A. (2019). A review of the state-of-art of the use of machine-learning and artificial intelligence by educational portals and oer repositories.

  • Chounta, I.A., Albacete, P., Jordan, P., Katz, S., & McLaren, B.M. (2017). The grey area: a computational approach to model the zone of proximal development. In European conference on technology enhanced learning (pp. 3–16): Springer.

  • Chounta, I.A., & Avouris, N. (2016). Towards the real-time evaluation of collaborative activities: integration of an automatic rater of collaboration quality in the classroom from the teacher’s perspective. Education and Information Technologies, 21(4), 815–835.

    Article  Google Scholar 

  • Cohen, L., Manion, L., & Morrison, K. (2013). Research methods in education. Routledge.

  • Corbett, A.T., Koedinger, K.R., & Anderson, J.R. (1997). Intelligent tutoring systems. In Handbook of human-computer interaction (pp. 849–874): Elsevier.

  • Council, N.R., & et al. (2000). How people learn: brain, mind, experience, and school: expanded edition. National Academies Press.

  • Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Vuorikari, R. (2016). Research evidence on the use of learning analytics: implications for education policy.

  • Freeman, A., Becker, S.A., & Cummins, M. (2017). Nmc/cosn horizon report: 2017 k. Tech. rep., The New Media Consortium.

  • Gardner, J., Brooks, C., & Baker, R. (2019). Evaluating the fairness of predictive student models through slicing analysis. In Proceedings of the 9th international conference on learning analytics & knowledge (pp. 225–234).

  • Hawkins, J., Sheingold, K., Gearhart, M., & Berger, C. (1982). Microcomputers in schools: impact on the social life of elementary classrooms. Journal of Applied Developmental Psychology, 3(4), 361–373.

    Article  Google Scholar 

  • Heffernan, N.T., & Heffernan, C.L. (2014). The assistments ecosystem: building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497.

    Article  MathSciNet  Google Scholar 

  • Hennessy, S., Wishart, J., Whitelock, D., Deaney, R., Brawn, R., La Velle, L., McFarlane, A., Ruthven, K., & Winterbottom, M. (2007). Pedagogical approaches for technology-integrated science teaching. Computers & Education, 48(1), 137–152.

    Article  Google Scholar 

  • Henriksen, D., Mishra, P., & Fisser, P. (2016). Infusing creativity and technology in 21st century education: a systemic view for change. Educational Technology & Society, 19(3), 27–37.

    Google Scholar 

  • Holder, C., Khurana, V., & Mark, W. (2018). Artificial intelligence: public perception attitude and trust.

  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: promises and implications for teaching and learning. Boston: Center for Curriculum Redesign.

    Google Scholar 

  • Holmes, W., Iniesto, F., Sharples, M., & Scanlon, E. (2019). Ethics in aied: who cares? An ec-tel workshop.

  • Holstein, K., Hong, G., Tegene, M., McLaren, B.M., & Aleven, V. (2018). The classroom as a dashboard: co-designing wearable cognitive augmentation for k-12 teachers. In Proceedings of the 8th international conference on learning Analytics and knowledge (pp. 79–88).

  • Holstein, K., McLaren, B.M., & Aleven, V. (2017). Intelligent tutors as teachers’ aides: exploring teacher needs for real-time analytics in blended classrooms. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 257–266).

  • Holstein, K., Wortman Vaughan, J., Daumé, H. III, Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: what do industry practitioners need?. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1–16).

  • Linzer, D.A., Lewis, J.B., & et al. (2011). polca: an r package for polytomous variable latent class analysis. Journal of Statistical Software, 42(10), 1–29.

    Article  Google Scholar 

  • Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1(3), 1–3.

    Article  Google Scholar 

  • Luckin, R. (2018). Machine learning and human intelligence: the future of education for the 21st century ERIC.

  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L.B. (2016). Intelligence unleashed: an argument for ai in education.

  • McCarthy, J. (1998). What is artificial intelligence?

  • Nwana, H.S. (1990). Intelligent tutoring systems: an overview. Artificial Intelligence Review, 4(4), 251–277.

    Article  Google Scholar 

  • Nye, B.D. (2015). Intelligent tutoring systems by and for the developing world: a review of trends and approaches for educational technology in a global context. International Journal of Artificial Intelligence in Education, 25(2), 177–203.

    Article  Google Scholar 

  • OECD. (2019). TALIS 2018 results (volume I): teachers and school leaders as lifelong learners. OECD Publishing.

  • OECD. (2020). TALIS 2018 Results (Volume II): Teachers and School Leaders as Valued Professionals. Paris: TALIS, OECD Publishing. https://doi.org/10.1787/19cf08dfen.

    Book  Google Scholar 

  • Ogbonnaya-Ogburu, I.F., Smith, A.D., To, A., & Toyama, K. (2020). Critical race theory for hci. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–16).

  • Papert, S. (1980). Mindstonns Vol. 607. New York: Basic Rooks.

    Google Scholar 

  • Pardo, A., Bartimote, K., Shum, S.B., Dawson, S., Gao, J., Gašević, D., Leichtweis, S., Liu, D., Martí, nez-Maldonado R., Mirriahi, N., & et al. (2018). Ontask: delivering data-informed, personalized learning support actions. Journal of Learning Analytics, 5(3), 235–249.

    Article  Google Scholar 

  • Pedaste, M., Leijen, Ä., Poom-Valickis, K., & Eisenschmidt, E. (2019). Teacher professional standards to support teacher quality and learning in estonia. European Journal of Education, 54(3), 389–399.

    Google Scholar 

  • Pedaste, M., Must, O., Leijen, Ä., Mäeots, M., Siiman, L., Kori, K., & Adov, L. (2017). Profiles of students who use mobile devices for the purposes of learning science and mathematics. Estonian Journal of Education, 5 (1), 99.

    Google Scholar 

  • Porayska-Pomsta, K., & Rajendran, G. (2019). Accountability in human and artificial intelligence decision-making as the basis for diversity and educational inclusion. In Artificial intelligence and inclusive education (pp. 39–59): Springer.

  • Prank, R. (1991). Using computerised exercises on mathematical logic. In Informatik und schule 1991 (pp. 34–38): Springer.

  • Prank, R., & Õunapuu, T. (1997). Intelligent tutoring system for proof building exercises. New Media and Telematic Technologies for Education in Eastern European Countries, pp. 371–376.

  • Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599.

    Article  Google Scholar 

  • Romano, S., Vinh, N.X., Bailey, J., & Verspoor, K. (2016). Adjusting for chance clustering comparison measures. The Journal of Machine Learning Research, 17(1), 4635–4666.

    MathSciNet  MATH  Google Scholar 

  • Rosé, C.P., McLaughlin, E.A., Liu, R., & Koedinger, K.R. (2019). Explanatory learner models: why machine learning (alone) is not the answer. British Journal of Educational Technology, 50(6), 2943–2958.

    Article  Google Scholar 

  • Runnel, P., Pruulmann-Vengerfeldt, P., & Reinsalu, K. (2009). The estonian tiger leap from post-communism to the information society: from policy to practice. Journal of Baltic Studies, 40(1), 29–51.

    Article  Google Scholar 

  • Schiff, D. (2020). Out of the laboratory and into the classroom: the future of artificial intelligence in education. AI & Society, pp 1–18.

  • Shum, S.J.B., & Luckin, R. (2019). Learning analytics and ai: politics, pedagogy and practices. British Journal of Educational Technology, 50 (6), 2785–2793.

    Article  Google Scholar 

  • Sinha, R., & Swearingen, K. (2002). The role of transparency in recommender systems. In CHI’02 extended abstracts on human factors in computing systems (pp. 830–831).

  • Tarus, J.K., Niu, Z., & Mustafa, G. (2018). Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artificial Intelligence Review, 50(1), 21–48.

    Article  Google Scholar 

  • van Leeuwen, A., & Rummel, N. (2020). Comparing teachers’ use of mirroring and advising dashboards. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 26–34).

  • van Leeuwen, A., Rummel, N., Holstein, K., McLaren, B.M., Aleven, V., Molenaar, I., Campen, C.K.V., Schwarz, B., Prusak, N., Swidan, O., & et al. (2018). Orchestration tools for teachers in the context of individual and collaborative learning: what information do teachers need and what do they do with it? International Society of the Learning Sciences, Inc.[ISLS].

  • VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.

    Article  Google Scholar 

  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J.L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509.

    Article  Google Scholar 

  • Verbert, K., Ochoa, X., De Croon, R., Dourado, R.A., & De Laet, T. (2020). Learning analytics dashboards: the past, the present and the future. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 35–40).

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Acknowledgements

We thank Marit Dremljuga-Telk, and Joshua Schiefelbein for their contribution in this work. This research was supported by the Estonian Research Council grant PSG286 and by the University of Tartu ASTRA Project PER ASPERA, financed by the European Regional Development Fund.

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Authors and Affiliations

  1. Institute of Education, University of Tartu, 1a Salme St., 50103, Tartu, Estonia

    Irene-Angelica Chounta, Emanuele Bardone, Aet Raudsep & Margus Pedaste

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  1. Irene-Angelica Chounta
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  2. Emanuele Bardone
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  3. Aet Raudsep
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  4. Margus Pedaste
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Correspondence to Irene-Angelica Chounta.

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Appendix: A

Appendix: A

A.1 Inter Coder Agreement Scores

Table 6 The results of the superpowers’ coding process where two raters coded whether a superpower was referenced in participants’ input
Full size table

A.2 Popular Learning Platforms Among Estonian Teachers

Table 7 A list of the learning platforms that the participants use on a regular basis for their work purposes along with a short description of each platform and the percentage of the participants who use it
Full size table

A.3 Survey of this Study

Survey “AI in Education: Perceptions and Perceived Challenges of Estonian K-12 Teachers”

  1. 1.

    What do you know about Artificial Intelligence? (choose one)

    O I have never heard of AI O Not sure what AI is O I have limited knowledge about AI O I know what AI is O I know a lot about AI O I am an expert in AI O Other (free text)

  2. 2.

    Mark the statements you think are true (multiple choice)

    O AI can perform tasks by replicating human intelligence O AI is a collection of connected entities/ machines O AI can modify itself O AI is able to learn from new information and can adapt to the environment around it O AI doesn’t necessarily have a physical form. It can be just software

  3. 3.

    Have you ever used an AI application? (choose one)

    O Never O Yes O I don’t know

  4. 4.

    Positive aspects of using AI in my job. Mark the statements that apply for you (multiple choice):

    O It could help me to save time when creating a time plan for my lesson O It could help me to save time when looking for materials/content for my lesson O It could help me to save time when reviewing homework O It could help me make less errors O Other (free text)

  5. 5.

    Negative aspects of using AI in my job. Mark the statements that apply for you (multiple choice):

    O It would require effort to learn how to use it O I’m scared it could take someone else’s job O I don’t trust it to carry out tasks without error O My work requires human involvement and i don’t think AI can do what is needed O Other (free text)

  6. 6.

    If you could have any superpowers you wanted to help you do your job, what would they be? (list up to three)

    O (free text)

  7. 7.

    What areas of your work could be supported by AI?

    O Administrative tasks O Grading students’ homework O Planning the lesson in terms of content O Planning the lesson in terms of time O Monitoring students in the Classroom O Other (free text)

  8. 8.

    What kind of learning applications do you use in your classroom?

    O eKool O Stuudium O e-Koolikott O Other (free text)

  9. 9.

    Do you want to know what kind of technology (for example AI or machine learning) your classroom tools use?

    O Yes O No O Maybe

  10. 10.

    Please indicate your professional experience (in years):

    O Less than five years O Between five and less than ten years O Between ten and less than twenty years O More than twenty years

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Chounta, IA., Bardone, E., Raudsep, A. et al. Exploring Teachers’ Perceptions of Artificial Intelligence as a Tool to Support their Practice in Estonian K-12 Education. Int J Artif Intell Educ 32, 725–755 (2022). https://doi.org/10.1007/s40593-021-00243-5

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