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What Students Should Learn and Teachers Must Know About Artificial Intelligence

  • Conference paper
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Intelligent Data Engineering and Automated Learning – IDEAL 2024 (IDEAL 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15347))

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Abstract

Systems based on artificial intelligence have become prominent in nearly all domains. However, knowledge of the inner workings of these intelligent systems is not as widespread, partly because the associated issues have been discussed only to a limited extent in computer science education. In order to gain an overview of AI in curricula and to see what competencies teachers need to teach this content, the AI-related content of the computer science curricula of the German federal states was analysed and compared with existing approaches. Proposals for further training courses are derived from this to enable teachers to teach AI competently.

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References

  1. Abedjan, Z., et al.: Empfehlungen für Masterstudiengänge “Data Science” –auf Basis eines Bachelors in (Wirtschafts-) Informatik oder Mathematik. Gesellschaft für Informatik e.V. (2021)

    Google Scholar 

  2. Anderson, L.W., Krathwohl, D.R., et al.: A taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Addison Wesley Longman, New York (2001)

    Google Scholar 

  3. Anderson, P., Bowring, J., McCauley, R., Pothering, G., Starr, C.: An undergraduate degree in data science: curriculum and a decade of implementation experience. In: Proceedings of the 45th ACM Technical Symposium on Computer Science Education, pp. 145–150 (2014)

    Google Scholar 

  4. Biggs, J.: Enhancing teaching through constructive alignment. High. Educ. 32(3), 347–364 (1996)

    Article  Google Scholar 

  5. Brinda, T., et al.: Dagstuhl-Erklärung: Bildung in der digitalen vernetzten Welt. Gesellschaft für Informatik e.V. (2016)

    Google Scholar 

  6. Clear, A., et al.: Computing curricula 2020. Technical report. ACM/IEEE, New York (2020). https://www.acm.org/education/curricula-recommendations

  7. Conti, M., Di Pietro, R., Mancini, L.V., Mei, A.: (old) distributed data source verification in wireless sensor networks. Inf. Fusion 10(4), 342–353 (2009). https://doi.org/10.1016/j.inffus.2009.01.002

    Article  Google Scholar 

  8. De Veaux, R.D., et al.: Curriculum guidelines for undergraduate programs in data science. Annu. Rev. Stat. Appl. 4(1), 15–30 (2017)

    Article  Google Scholar 

  9. EDISON Project: Edison: Building the data science profession (2023). https://edison-project.eu/

  10. GI, Gesellschaft für Informatik e.V.: Bildungsstandards informatik (2019). https://informatikstandards.de/standards/operatoren

  11. Heinemann, B., et al.: Drafting a data science curriculum for secondary schools. In: Proceedings of the 18th Koli Calling International Conference on Computing Education Research, Koli Calling 2018. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3279720.3279737

  12. Jetzinger, F., Baumer, S., Michaeli, T.: Artificial intelligence in compulsory k-12 computer science classrooms: a scalable professional development offer for computer science teachers. In: Proceedings of the 55th ACM Technical Symposium on Computer Science Education, SIGCSE 2024, vol. 1, pp. 590–596. Association for Computing Machinery, New York (2024). https://doi.org/10.1145/3626252.3630782

  13. Lorenz, U., Romeike, R.: What is AI-pack? - outline of AI competencies for teaching with DPACK. In: Pellet, J.P., Parriaux, G. (eds.) ISSEP 2023. LNCS, vol. 14296, pp. 13–25. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-44900-0_2

    Chapter  Google Scholar 

  14. Michaeli, T., Romeike, R., Seegerer, S.: What students can learn about artificial intelligence-recommendations for k-12 computing education. In: Keane, T., Lewin, C., Brinda, T., Bottino, R. (eds.) WCCE 2022. IFIP Advances in Information and Communication Technology, vol. 685, pp. 196–208. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-43393-1_19

    Chapter  Google Scholar 

  15. Olari, V., et al.: Introduction of artificial intelligence literacy and data literacy in computer science teacher education. In: Proceedings of the 23rd Koli Calling International Conference on Computing Education Research, Koli Calling 2023. Association for Computing Machinery, New York (2024). https://doi.org/10.1145/3631802.3631851

  16. Zhang, Y.: New advances in machine learning. BoD–Books on Demand (2010)

    Google Scholar 

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Authors and Affiliations

  1. FernUniversität in Hagen, 58097, Hagen, Germany

    Simone Opel, Andrea Linxen & Christian Beecks

Authors
  1. Simone Opel
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  2. Andrea Linxen
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  3. Christian Beecks
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Corresponding author

Correspondence to Simone Opel .

Editor information

Editors and Affiliations

  1. Technical University of Valencia, Valencia, Valencia, Spain

    Vicente Julian

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. The University of Manchester, Manchester, UK

    Hujun Yin

  4. Universitat Politècnica de València, Valencia, Valencia, Spain

    Juan M. Alberola

  5. University of Evora, Evora, Portugal

    Vitor Beires Nogueira

  6. Universidade do Minho, Braga, Portugal

    Paulo Novais

  7. University of Huelva, Huelva, Spain

    Antonio Tallón-Ballesteros

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Opel, S., Linxen, A., Beecks, C. (2025). What Students Should Learn and Teachers Must Know About Artificial Intelligence. In: Julian, V., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2024. IDEAL 2024. Lecture Notes in Computer Science, vol 15347. Springer, Cham. https://doi.org/10.1007/978-3-031-77738-7_40

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  • DOI: https://doi.org/10.1007/978-3-031-77738-7_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77737-0

  • Online ISBN: 978-3-031-77738-7

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