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Who is Best Suited for the Job? Task Allocation Process Between Teachers and Smart Machines Based on Comparative Strengths

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Computer Supported Education (CSEDU 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1624))

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Abstract

Due to advances in machine learning (ML) and artificial intelligence (AI), computer systems are becoming increasingly intelligent and capable of taking on new tasks (e.g., automatic translation of texts). In education, such AI-powered smart machines (e.g., chatbots, social robots) have the potential to support teachers in the classroom in order to improve the quality of teaching. However, from a teacher’s point of view, it may be unclear which subtasks could be best outsourced to the smart machine.

Considering human augmentation, this paper presents a theoretical basis for the use of smart machines in education. It highlights the relative strengths of teachers and smart machines in the classroom and proposes a staged process for assigning classroom tasks. The derived task allocation process can be characterized by its three main steps of 1) break-down of task sequence and rethinking the existing task structure, 2) invariable task assignment (normative and technical considerations), and 3) variable task assignment (efficiency considerations). Based on the comparative strengths of both parties, the derived process ensures that subtasks are assigned as efficiently as possible (variable task assignment), while always granting priority to subtasks of normative importance (invariable task assignment). In this way, the derived task allocation process can serve as a guideline for the design and the implementation of smart machine projects in education.

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Notes

  1. 1.

    Note. Section 4 is an adapted version of “Sect. 3: Comparative Strengths of Teachers and Smart Machines” published in Burkhard et al. [22].

  2. 2.

    Note. Section 4 is an adapted version of “Sect. 3: Comparative Strengths of Teachers and Smart Machines” published in Burkhard et al. [22].

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Authors and Affiliations

  1. Institute for Educational Management and Technologies, University of St. Gallen, St. Jakob-Strasse 21, 9000, St. Gallen, Switzerland

    Michael Burkhard, Josef Guggemos & Sabine Seufert

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  1. Michael Burkhard
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  2. Josef Guggemos
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  3. Sabine Seufert
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Correspondence to Michael Burkhard .

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Editors and Affiliations

  1. Institute of Education, University of Szeged, Szeged, Hungary

    Beno Csapó

  2. School of Engineering, University of Ulster, Newtownabbey, UK

    James Uhomoibhi

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Burkhard, M., Guggemos, J., Seufert, S. (2022). Who is Best Suited for the Job? Task Allocation Process Between Teachers and Smart Machines Based on Comparative Strengths. In: Csapó, B., Uhomoibhi, J. (eds) Computer Supported Education. CSEDU 2021. Communications in Computer and Information Science, vol 1624. Springer, Cham. https://doi.org/10.1007/978-3-031-14756-2_1

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  • DOI: https://doi.org/10.1007/978-3-031-14756-2_1

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