Skip to main content

Educ-AI-ted – Investigating Educators’ Perspectives Concerning the Use of AI in University Teaching and Learning

  • Conference paper
  • First Online:
Learning Technology for Education Challenges (LTEC 2023)

Abstract

Artificial Intelligence (AI) is currently been embedded into various tools and devices supporting many of our daily activities and routines. Thence, it is not surprising that AI-driven applications are increasingly also found in the education sector. Such an integration, which is often referred to as AIEd, not only offers great new opportunities for learners, but may also trigger significant challenges for education providers. The work discussed in this paper aims to bring some light to this problem space by offering teachers’ perspectives on the topic. We report on a Delphi study with \(n=17\) university teachers, focusing on their experiences, their doubts and their future wishes concerning the use of AI in teaching and learning settings. Results from three rounds of questioning indicate that educators are generally open to the idea of integrating AI components into their pedagogical concepts, even if in specific application scenarios, such as student assessment, opposing perspectives exist. Results furthermore show that corresponding tool training and better (technical) support is required in order successfully manage this significant change our education landscape is currently undergoing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Online: https://www.apple.com/de/siri/. [accessed: January 23th 2023].

  2. 2.

    Online: https://openai.com/blog/chatgpt/. [accessed: January 23th 2023].

  3. 3.

    Online: https://moodle.org/. [accessed: January 30th 2023].

  4. 4.

    Online: https://www.sakailms.org/. [accessed: January 30th 2023].

  5. 5.

    Online: https://tcexam.org/. [accessed: January 30th 2023].

  6. 6.

    Online: https://www.mentimeter.com/. [accessed: January 30th 2023].

  7. 7.

    Online: https://www.mural.co/. [accessed: January 30th 2023].

  8. 8.

    Online: https://miro.com/. [accessed: January 30th 2023].

  9. 9.

    Online: https://www.deepl.com/translator. [accessed: January 30th 2023].

  10. 10.

    Online: https://translate.google.com/. [accessed: January 30th 2023].

  11. 11.

    Online: https://www.grammarly.com/. [accessed: January 30th 2023].

  12. 12.

    Online: https://www.researchrabbit.ai/. [accessed: January 30th 2023].

  13. 13.

    Online: https://www.turnitin.com/. [accessed: January 30th 2023].

  14. 14.

    Online: https://www.plagscan.com/en/. [accessed: January 30th 2023].

  15. 15.

    Online: https://github.com/features/copilot. [accessed: January 30th 2023].

References

  1. Adamopoulou, E., Moussiades, L.: An overview of chatbot technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2020. IAICT, vol. 584, pp. 373–383. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49186-4_31

    Chapter  Google Scholar 

  2. Anderson, J.R., Boyle, C.F., Reiser, B.J.: Intelligent tutoring systems. Science 228(4698), 456–462 (1985)

    Article  Google Scholar 

  3. Azad, S., Chen, B., Fowler, M., West, M., Zilles, C.: Strategies for deploying unreliable AI graders in high-transparency high-stakes exams. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 16–28. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_2

    Chapter  Google Scholar 

  4. Baker, T., Smith, L., Anissa, N.: Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges (2019). Accessed 12 May 2020

    Google Scholar 

  5. Banerjee, A., Lamrani, I., Hossain, S., Paudyal, P., Gupta, S.K.S.: AI enabled tutor for accessible training. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 29–42. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_3

    Chapter  Google Scholar 

  6. Barrett, D., Heale, R.: What are Delphi studies? Evid. Based Nurs. 23(3), 68–69 (2020)

    Article  Google Scholar 

  7. Bii, P., Too, J., Mukwa, C.: Teacher attitude towards use of chatbots in routine teaching. Univ. J. Educ. Res. 6(7), 1586–1597 (2018)

    Article  Google Scholar 

  8. Cader, A.: The potential for the use of deep neural networks in e-Learning student evaluation with new data augmentation method. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 37–42. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_7

    Chapter  Google Scholar 

  9. Casamayor, A., Amandi, A., Campo, M.: Intelligent assistance for teachers in collaborative e-Learning environments. Comput. Educ. 53(4), 1147–1154 (2009)

    Article  Google Scholar 

  10. Chaudhry, M.A., Kazim, E.: Artificial intelligence in education (AIEd): a high-level academic and industry note 2021. AI Ethics 2(1), 157–165 (2022)

    Article  Google Scholar 

  11. Chen, L., Chen, P., Lin, Z.: Artificial intelligence in education: a review. IEEE Access 8, 75264–75278 (2020)

    Article  Google Scholar 

  12. Cukurova, M., Kent, C., Luckin, R.: Artificial intelligence and multimodal data in the service of human decision-making: a case study in debate tutoring. Br. J. Educ. Technol. 50(6), 3032–3046 (2019)

    Article  Google Scholar 

  13. Dalkey, N., Helmer, O.: An experimental application of the Delphi method to the use of experts. Manage. Sci. 9(3), 458–467 (1963)

    Article  Google Scholar 

  14. Del Bonifro, F., Gabbrielli, M., Lisanti, G., Zingaro, S.P.: Student dropout prediction. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 129–140. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_11

    Chapter  Google Scholar 

  15. Delgado, H.O.K., de Azevedo Fay, A., Sebastiany, M.J., Silva, A.D.C.: Artificial intelligence adaptive learning tools. BELT-Br. Engl. Lang. Teach. J. 11(2), e38749–e38749 (2020)

    Google Scholar 

  16. Filighera, A., Steuer, T., Rensing, C.: Fooling automatic short answer grading systems. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 177–190. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_15

    Chapter  Google Scholar 

  17. Hayashi, Y., Nomura, T., Hirashima, T.: Prediction of group learning results from an aggregation of individual understanding with kit-build concept map. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 109–113. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_20

    Chapter  Google Scholar 

  18. Heiko, A.: Consensus measurement in Delphi studies: review and implications for future quality assurance. Technol. Forecast. Soc. Change 79(8), 1525–1536 (2012)

    Article  Google Scholar 

  19. Helfferich, C.: Die qualität qualitativer daten: Manual für die durchführung qualitativer interviews, third., überarbeitete auflage (2009)

    Google Scholar 

  20. Hsu, C.C., Sandford, B.A.: The Delphi technique: making sense of consensus. Pract. Assess. Res. Eval. 12(1), 10 (2007)

    Google Scholar 

  21. Huang, G.Y., et al.: Neural multi-task learning for teacher question detection in online classrooms. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 269–281. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_22

    Chapter  Google Scholar 

  22. Huang, Y., Aleven, V., McLaughlin, E., Koedinger, K.: A general multi-method approach to design-loop adaptivity in intelligent tutoring systems. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 124–129. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_23

    Chapter  Google Scholar 

  23. Hwang, G.J., Xie, H., Wah, B.W., Gašević, D.: Vision, challenges, roles and research issues of artificial intelligence in education (2020)

    Google Scholar 

  24. Keeney, S., Hasson, F., McKenna, H.P.: A critical review of the Delphi technique as a research methodology for nursing. Int. J. Nurs. Stud. 38(2), 195–200 (2001)

    Article  Google Scholar 

  25. Li, H., Wang, Z., Tang, J., Ding, W., Liu, Z.: Siamese neural networks for class activity detection. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 162–167. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_30

    Chapter  Google Scholar 

  26. Luckin, R., Holmes, W., Griffiths, M., Forcier, L.B.: Intelligence Unleashed: An Argument for AI in Education. Pearson Education, London (2016)

    Google Scholar 

  27. Mousavinasab, E., Zarifsanaiey, N., Kalhori, S.N.R., Rakhshan, M., Keikha, L., Saeedi, M.G.: Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interact. Learn. Environ. 29(1), 142–163 (2021)

    Google Scholar 

  28. Mouta, A., Sánchez, E.T., Llorente, A.M.P.: Sense of agency in times of automation: a teachers’ professional development proposal on the ethical challenges of AI applied to education. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 405–408. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_73

    Chapter  Google Scholar 

  29. Ndukwe, I.G., Amadi, C.E., Nkomo, L.M., Daniel, B.K.: Automatic grading system using sentence-BERT network. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 224–227. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_41

    Chapter  Google Scholar 

  30. Okonkwo, C.W., Ade-Ibijola, A.: Chatbots applications in education: a systematic review. Comput. Educ.: Artif. Intell. 2, 100033 (2021)

    Google Scholar 

  31. Ouyang, F., Jiao, P.: Artificial intelligence in education: the three paradigms. Comput. Educ.: Artif. Intell. 2, 100020 (2021)

    Google Scholar 

  32. Paliwoda, S.J.: Predicting the future using Delphi. Manage. Decis. 21, 31–38 (1983)

    Article  Google Scholar 

  33. Popenici, S.A.D., Kerr, S.: Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanced Learn. 12(1), 1–13 (2017). https://doi.org/10.1186/s41039-017-0062-8

    Article  Google Scholar 

  34. Skulmoski, G.J., Hartman, F.T., Krahn, J.: The Delphi method for graduate research. J. Inf. Technol. Educ.: Res. 6(1), 1–21 (2007)

    Google Scholar 

  35. Uto, M., Okano, M.: Robust neural automated essay scoring using item response theory. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 549–561. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_44

    Chapter  Google Scholar 

  36. Uto, M., Uchida, Y.: Automated short-answer grading using deep neural networks and item response theory. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12164, pp. 334–339. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52240-7_61

    Chapter  Google Scholar 

  37. Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., Drachsler, H.: Are we there yet?-A systematic literature review on chatbots in education. Front. Artif. Intell. 4, 654924 (2021)

    Article  Google Scholar 

  38. Zawacki-Richter, O., Marín, V.I., Bond, M., Gouverneur, F.: Systematic review of research on artificial intelligence applications in higher education-where are the educators? Int. J. Educ. Technol. High. Educ. 16(1), 1–27 (2019)

    Article  Google Scholar 

  39. Zhang, C., Lu, Y.: Study on artificial intelligence: the state of the art and future prospects. J. Ind. Inf. Integr. 23, 100224 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephan Schlögl .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tritscher, R., Röck, J., Schlögl, S. (2023). Educ-AI-ted – Investigating Educators’ Perspectives Concerning the Use of AI in University Teaching and Learning. In: Uden, L., Liberona, D. (eds) Learning Technology for Education Challenges. LTEC 2023. Communications in Computer and Information Science, vol 1830. Springer, Cham. https://doi.org/10.1007/978-3-031-34754-2_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34754-2_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34753-5

  • Online ISBN: 978-3-031-34754-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics