Skip to main content
Log in

Kompetenzfelder künftiger Beschäftigter im Bereich Künstlicher Intelligenz

Competencies for Future Employees in the Field of Artificial Intelligence

  • Schwerpunkt
  • Published:
HMD Praxis der Wirtschaftsinformatik Aims and scope Submit manuscript

Zusammenfassung

In dem Beitrag wird basierend auf einer Literaturrecherche herausgearbeitet, welche Kompetenzfelder zu adressieren sind, um Mitarbeitende von Unternehmen zu befähigen, Systeme aus dem Bereich der Künstlichen Intelligenz (KI) einsetzen und/oder mit ihnen umgehen zu können. Im Fokus stehen dabei nicht nur die Kompetenzanforderungen an KI-Expert*innen, sondern an alle Mitarbeitenden, die aktuell oder zukünftig mit KI-Systemen interagieren. Die Untersuchung basiert auf einem Kompetenzschema, das bereits zur Einordnung der Kompetenzen im Bereich Business Analytics verwendet wurde. Als Ergebnis wird die Unterscheidung von fünf Kompetenzfeldern und drei Kompetenzstufen vorgeschlagen, auf deren Grundlage Unternehmen und deren aktuelle und potenzielle Beschäftigte gezielt künftig benötigte KI-Kompetenzen erwerben bzw. entwickeln können.

Abstract

Based on a literature review, this article identifies the competencies that need to be addressed to enable company employees to use and/or deal with artificial intelligence (AI) systems. The focus is not only on the competence requirements for AI experts, but also for all employees who interact with AI systems now or in the future. The study is based on a competency scheme that has already been used to classify competencies in the field of business analytics. As a result, a distinction between five competence fields and three competence levels is proposed, based on which companies and their current and potential employees can acquire or develop the AI skills they will need in the future.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Abb. 1
Abb. 2
Abb. 3
Abb. 4
Abb. 5
Abb. 6

Literatur

  • André E, Bauer W et al (2021) Competence development for AI—Changes, needs and options for action. White paper from Plattform Lernende Systeme. München https://doi.org/10.48669/pls_2021-2

    Book  Google Scholar 

  • Bär J, Badura D, Bockshecker A et al (2019) Die Mitarbeiter von morgen: Ergebnisse eines Workshops zu den Kompetenzen künftiger Mitarbeiter im Bereich Business Analytics. Nordblick 8:34–49

    Google Scholar 

  • Below F, Neuhaus U, Schulz M (2023) What do you call your analytical endeavours? An analysis of term usage in German job openings from 2017 to 2022. Data Anal: 21–24

  • Carolus A, Koch M, Straka S et al (2023) MAILS—Meta AI literacy scale: development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and Meta-competencies https://doi.org/10.48550/arXiv.2302.09319

    Book  Google Scholar 

  • Cetindamar D, Kitto K, Wu M et al (2022) Explicating AI literacy of employees at digital workplaces. In: IEEE transactions on engineering management https://doi.org/10.1109/TEM.2021.3138503

    Chapter  Google Scholar 

  • Chai CS, Wang X, Xu C (2020) An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics 8(11):2089. https://doi.org/10.3390/math8112089

    Article  Google Scholar 

  • Desai A (2023) Exploring business schools’ role in artificial intelligence education, technology & innovation. J Natl Acad Invent 23:1–13

    Google Scholar 

  • Deuze M, Beckett C (2022) Imagination, algorithms and news: developing AI literacy for journalism. Digit Journalism 10(10):1913–1918. https://doi.org/10.1080/21670811.2022.2119152

    Article  Google Scholar 

  • Druga S, Yip J, Preston M et al (2023) The 4 as: ask, adapt, author, analyze: aI literacy framework for families. In: Ito M, Cross R, Dinakar K et al (Hrsg) Algorithmic rights and Protections for children. MIT Press, https://doi.org/10.7551/mitpress/13654.003.0014

    Chapter  Google Scholar 

  • Eguchi A, Okada H, Muto Y (2021) Contextualizing AI education for K‑12 students to enhance their learning of AI literacy through culturally responsive approaches. Künstl Intell 35:153–161. https://doi.org/10.1007/s13218-021-00737-3

    Article  Google Scholar 

  • Kandlhofer M, Steinbauer-Wagner G, Hirschmugl-Gaisch S et al (2016) Artificial intelligence and computer science in education: From kindergarten to university. 2016 IEEE Frontiers in Education Conference (FIE), Erie, S 1–9 https://doi.org/10.1109/FIE.2016.7757570

    Book  Google Scholar 

  • Karaca O, Çalışkan SA, Demir K (2021) Medical artificial intelligence readiness scale for medical students (MAIRS-MS)—development, validity and reliability study. BMC Med Educ 21:112. https://doi.org/10.1186/s12909-021-02546-6

    Article  Google Scholar 

  • Kim K, Kwon K (2023) Exploring the AI competencies of elementary school teachers in South Korea, computers and education. Artif Intell 4:100137. https://doi.org/10.1016/j.caeai.2023.100137

    Article  Google Scholar 

  • Kim S, Jang Y, Kim W et al (2021) Why and what to teach: aI curriculum for elementary school. Proc AAAI Conf Artif Intell 35(17):15569–15576. https://doi.org/10.1609/aaai.v35i17.17833

    Article  Google Scholar 

  • Laupichler MC, Aster A, Raupach T (2023) Delphi study for the development and preliminary validation of an item set for the assessment of non-experts’ AI literacy, Computers and Education. Artif Intell 4:100126. https://doi.org/10.1016/j.caeai.2023.100126

    Article  Google Scholar 

  • Long D, Magerko B (2020) What is AI Literacy? Competencies and Design Considerations. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20). Association for Computing Machinery, New York, S 1–16 https://doi.org/10.1145/3313831.3376727

    Chapter  Google Scholar 

  • Neuhaus U, Schröder H, Schulz M (2019) Die Mitarbeiter von morgen – Kompetenzen künftiger Mitarbeiter im Bereich Business Analytics. In: Ludwig T, Pipek V (Hrsg) Human Practice. Digital Ecologies Our Future. 14. Internationale Tagung Wirtschaftsinformatik (WI 2019, S 2020–2031

    Google Scholar 

  • Ng DTK, Leung JKL, Chu SKW et al (2021a) Conceptualizing AI literacy: An exploratory review. Comput Educ Artif Intell 2:100041. https://doi.org/10.1016/j.caeai.2021.100041

    Article  Google Scholar 

  • Ng DTK, Leung JKL, Chu KWS et al (2021b) AI literacy: definition, teaching, evaluation and ethical issues. Proc Assoc Inf Sci Technol 58(1):504–509

    Article  Google Scholar 

  • Ng DTK, Luo W, Chan HMY et al (2022) Using digital story writing as a pedagogy to develop AI literacy among primary students. Comput Educ Artif Intell 3(2666-920X):100054. https://doi.org/10.1016/j.caeai.2022.100054

    Article  Google Scholar 

  • Olari V, Romeike R (2021) Addressing AI and Data Literacy in Teacher Education: A Review of Existing Educational Frameworks. In: The 16th Workshop in Primary and Secondary Computing Education (WiPSCE ’21. Article 17. Association for Computing Machinery, New York, S 1–2 https://doi.org/10.1145/3481312.3481351

    Chapter  Google Scholar 

  • Park J (2023) A case study on enhancing the expertise of artificial intelligence education for pre-service teachers https://doi.org/10.20944/preprints202305.2006.v1 (Preprints 2023)

    Book  Google Scholar 

  • Payne BH (2019) An ethics of artificial intelligence curriculum for middle school students. MIT Media Lab

    Google Scholar 

  • Pinski M, Benlian A (2023) AI literacy—towards measuring human competency in artificial intelligence. In: Proceedings of the 56th hawaii international conference on system sciences 2023 3.–6. Januar 2023

    Google Scholar 

  • Schüller K (2021) Towards a framework for data and AI literacy. In: Proceedings 63rd ISI world statistics congress 11.–16. Juli 2021

    Google Scholar 

  • Schüller K (2022) Data and AI literacy for everyone. SJI 38(2):477–490

    Article  Google Scholar 

  • Southworth J, Migliaccio K, Glover J et al (2023) Developing a model for AI across the curriculum: transforming the higher education landscape via innovation in AI literacy. Comput Educ Artif Intell 4:100127. https://doi.org/10.1016/j.caeai.2023.100127

    Article  Google Scholar 

  • Urbach N, Ahlemann F (2016) IT-Management im Zeitalter der Digitalisierung. Auf dem Weg zur IT-Organisation der Zukunft. Springer Gabler, Berlin Heidelberg

    Google Scholar 

  • Wang B, Rau PP, Yuan T (2022) Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behav Inf Technol 42:1–14. https://doi.org/10.1080/0144929X.2022.2072768

    Article  Google Scholar 

  • Yau KW, Chai CS, Chiu TKF et al (2022) Developing an AI literacy test for junior secondary students: the first stage. 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), S 59–64 https://doi.org/10.1109/TALE54877.2022.00018

    Book  Google Scholar 

  • Zhao L, Wu X, Luo H (2022) Developing AI literacy for primary and middle school teachers in China: based on a structural equation modeling analysis. Sustainability 14(21):14549. https://doi.org/10.3390/su142114549

    Article  Google Scholar 

  • Zschech P, Fleißner V, Baumgärtel N et al (2018) Data Science Skills and Enabling Enterprise Systems – Eine Erhebung von Kompetenzanforderungen und Weiterbildungsangeboten. HMD 55:163–181

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Franziska Herrmann.

Additional information

Hinweis des Verlags

Der Verlag bleibt in Hinblick auf geografische Zuordnungen und Gebietsbezeichnungen in veröffentlichten Karten und Institutsadressen neutral.

Rights and permissions

Springer Nature oder sein Lizenzgeber (z.B. eine Gesellschaft oder ein*e andere*r Vertragspartner*in) hält die ausschließlichen Nutzungsrechte an diesem Artikel kraft eines Verlagsvertrags mit dem/den Autor*in(nen) oder anderen Rechteinhaber*in(nen); die Selbstarchivierung der akzeptierten Manuskriptversion dieses Artikels durch Autor*in(nen) unterliegt ausschließlich den Bedingungen dieses Verlagsvertrags und dem geltenden Recht.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Neuhaus, U., Schulz, M., Schröder, H. et al. Kompetenzfelder künftiger Beschäftigter im Bereich Künstlicher Intelligenz. HMD 61, 471–484 (2024). https://doi.org/10.1365/s40702-024-01046-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1365/s40702-024-01046-7

Schlüsselwörter

Keywords

Navigation