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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Braga, C.P., et al.: Services Trade for Sustainable, Balanced, and Inclusive Growth (2019). https://t20japan.org/policy-brief-services-trade-sustainable-inclusive-growth/
Brynjolfsson, E., McAfee, A.: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. Norton, New York (2014)
Makridakis, S.: The forthcoming Artificial Intelligence (AI) revolution: its impact on society and firms. Futures 90, 46–60 (2017). https://doi.org/10.1016/j.futures.2017.03.006
Wahlster, W.: Künstliche Intelligenz als Treiber der zweiten Digitalisierungswelle. IM+io Das Magazin für Innovation Organisation und Management 2, 10–13 (2017)
Baldwin, R., Forslid, R.: Globotics and development: when manufacturing is jobless and services are tradable (Working paper 26731). National Bureau of Economic Research (2020). https://doi.org/10.3386/w26731
Jarrahi, M.H.: Artificial intelligence and the future of work: human-AI symbiosis in organizational decision making. Bus. Horiz. 61(4), 577–586 (2018). https://doi.org/10.1016/j.bushor.2018.03.007
McKenney, S., Visscher, A.J.: Technology for teacher learning and performance. Technol. Pedagogy Educ. 28(2), 129–132 (2019). https://doi.org/10.1080/1475939X.2019.1600859
Oeste, S., Lehmann, K., Janson, A., Söllner, M., Leimeister, J.M.: Redesigning university large scale lectures: how to activate the learner. In: Academy of Management Proceedings, vol. 2015, no. 1, p. 14650. Academy of Management, Briarcliff Manor, NY 10510 (2015). https://doi.org/10.5465/ambpp.2015.14650abstract
Pereira, A.: What are smart machines? Career in STEM (2019). https://careerinstem.com/what-are-smart-machines/
Strobl, C., et al.: Digital support for academic writing: a review of technologies and pedagogies. Comput. Educ. 131, 33–48 (2019). https://doi.org/10.1016/j.compedu.2018.12.005
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F.: Social robots for education: a review. Sci. Robot. 3(21), 1–9 (2018). https://doi.org/10.1126/scirobotics.aat5954
Reich-Stiebert, N., Eyssel, F., Hohnemann, C.: Exploring university students’ preferences for educational robot design by means of a user-centered design approach. Int. J. Soc. Robot. 12, 1–11 (2019). https://doi.org/10.1007/s12369-019-00554-7
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). https://doi.org/10.1186/s41239-019-0171-0
Abildgaard, J.R., Scharfe, H.: A geminoid as lecturer. In: Ge, S.S., Khatib, O., Cabibihan, J.-J., Simmons, R., Williams, M.-A. (eds.) ICSR 2012. LNCS (LNAI), vol. 7621, pp. 408–417. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34103-8_41
Cooney, M., Leister, W.: Using the engagement profile to design an engaging robotic teaching assistant for students. Robotics 8(1), 21–47 (2019). https://doi.org/10.3390/robotics8010021
Guggemos, J., Seufert, S., Sonderegger, S.: Humanoid robots in higher education: evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT. Br. J. Educ. Technol. 51(5), 1864–1883 (2020). https://doi.org/10.1111/bjet.13006
Masuta, H., et al.: Presentation robot system with interaction for class. In: 2018 Symposium Series on Computational Intelligence (SSCI), pp. 1801–1806. IEEE (2018). https://doi.org/10.1109/SSCI.2018.8628804
Bolea Monte, Y., Grau Saldes, A., Sanfeliu Cortés, A.: From research to teaching: integrating social robotics in engineering degrees. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(6), 1020–1023 (2016). https://doi.org/10.5281/zenodo.1124667
Gao, Y., Barendregt, W., Obaid, M., Castellano, G.: When robot personalization does not help: insights from a robot-supported learning study. In: 2018 27th International Symposium on Robot and Human Interactive Communication, pp. 705–712. IEEE (2018). https://doi.org/10.1109/ROMAN.2018.8525832
Luan, H., et al.: Challenges and future directions of big data and artificial intelligence in education. Front. Psychol. 11, 1–11 (2020). https://doi.org/10.3389/fpsyg.2020.580820
Macfadyen, L.P.: Overcoming barriers to educational analytics: how systems thinking and pragmatism can help. Educ. Technol. 57, 31–39 (2017). https://www.jstor.org/stable/44430538
Burkhard, M., Seufert, S., Guggemos, J.: Relative strengths of teachers and smart machines: towards an augmented task sharing. In: Proceedings of the 13th International Conference on Computer Supported Education (CSEDU), vol. 1, pp. 73–83 (2021). https://doi.org/10.5220/0010370300730083
Floridi, L.: Hyperhistory, the emergence of the MASs, and the design of infraethics. In: Information, Freedom and Property: The Philosophy of Law Meets the Philosophy of Technology, vol. 153 (2016)
Floridi, L.: The Ethics of Information. Oxford University Press, Oxford (2013). https://doi.org/10.1093/acprof:oso/9780199641321.001.0001
Saini, M.K., Goel, N.: How smart are smart classrooms? A review of smart classroom technologies. ACM Comput. Surv. (CSUR) 52(6), 1–28 (2019). https://doi.org/10.1145/3365757
Heaven, W.D.: OpenAI’s new language generator GPT-3 is shockingly good—And completely mindless. MIT Technology Review (2020). https://www.technologyreview.com/2020/07/20/1005454/openai-machine-learning-language-generator-gpt-3-nlp/
Brown, T.B., et al.: Language models are few-shot learners. arXiv preprint (2020). https://arxiv.org/abs/2005.14165
Acemoglu, D., Restrepo, P.: The wrong kind of AI? Artificial intelligence and the future of labour demand. Camb. J. Reg. Econ. Soc. 13(1), 25–35 (2020). https://doi.org/10.1093/cjres/rsz022
Aoun, J.E.: Robot-Proof: Higher Education in the Age of Artificial Intelligence. MIT Press, Cambridge (2017)
Davenport, T.H., Kirby, J.: Only Humans Need Apply: Winners and Losers in the Age of Smart Machines. Harper Business, New York (2016)
Dellermann, D., Ebel, P., Söllner, M., Leimeister, J.M.: Hybrid intelligence. Bus. Inf. Syst. Eng. 61(5), 637–643 (2019). https://doi.org/10.1007/s12599-019-00595-2
Wilson, H.J., Daugherty, P.R.: Collaborative intelligence: humans and AI are joining forces. Harvard Bus. Rev. 96(4), 114–123 (2018)
Bradshaw, J.M., Feltovich, P.J., Johnson, M.: Human–agent interaction. In: The Handbook of Human-Machine Interaction, pp. 283–300. CRC Press (2011). https://www.taylorfrancis.com/chapters/edit/10.1201/9781315557380-14/human%E2%80%93agent-interaction-jeffrey-bradshaw-paul-feltovich-matthew-johnson
Ranz, F., Hummel, V., Sihn, W.: Capability-based task allocation in human-robot collaboration. Proc. Manuf. 9, 182–189 (2017). https://doi.org/10.1016/j.promfg.2017.04.011
Musto, M.: Revisiting Marx’s concept of alienation. Socialism Democracy 24(3), 79–101 (2010). https://doi.org/10.1080/08854300.2010.544075
Wogu, I.A.P., et al.: Artificial intelligence, alienation and ontological problems of other minds: a critical investigation into the future of man and machines. In: 2017 International Conference on Computing Networking and Informatics (ICCNI), pp. 1–10 (2017). https://doi.org/10.1109/ICCNI.2017.8123792
GETChina Insights. Schools using facial recognition system sparks privacy concerns in China. Medium (2019). https://edtechchina.medium.com/schools-using-facial-recognition-system-sparks-privacy-concerns-in-china-d4f706e5cfd0
Sharkey, A.J.C.: Should we welcome robot teachers? Ethics Inf. Technol. 18(4), 283–297 (2016). https://doi.org/10.1007/s10676-016-9387-z
Pardo, A., Siemens, G.: Ethical and privacy principles for learning analytics. Br. J. Educ. Technol. 45(3), 438–450 (2014). https://doi.org/10.1111/bjet.12152
Sohn, K., Kwon, O.: Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics Inform. 47, 101324 (2020). https://doi.org/10.1016/j.tele.2019.101324
Frey, C.B., Osborne, M.A.: The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114, 254–280 (2017). https://doi.org/10.1016/j.techfore.2016.08.019
Mubin, O., Stevens, C.J., Shahid, S., Mahmud, A.A., Dong, J.-J.: A review of the applicability of robots in education. Technol. Educ. Learn. 1(1), 1–7 (2013). https://doi.org/10.2316/Journal.209.2013.1.209-0015
Beumelburg, K.: Fähigkeitsorientierte Montageablaufplanung in der direkten Mensch-Roboter-Kooperation. Doctoral dissertation. Institut für Industrielle Fertigung und Fabrikbetrieb (IFF), University of Stuttgart (2005)
Ruffin, R.: David Ricardo’s discovery of comparative advantage. Hist. Polit. Econ. 34(4), 727–748 (2002)
Landsburg, L.F.: Comparative Advantage. The Library of Economics and Liberty (n.d.). https://www.econlib.org/library/Topics/Details/comparativeadvantage.html
Ricardo, D.: Principles of Political Economy and Taxation. G. Bell and Sons, London (1891)
Latham, S., Humberd, B.: Four ways jobs will respond to automation. MIT Sloan Manag. Rev. 60(1), 11–14 (2018)
Harteis, C., Billett, S.: Intuitive expertise: theories and empirical evidence. Educ. Res. Rev. 9, 145–157 (2013). https://doi.org/10.1016/j.edurev.2013.02.001
Süzen, N., Gorban, A.N., Levesley, J., Mirkes, E.M.: Automatic short answer grading and feedback using text mining methods. Proc. Comput. Sci. 169, 726–743 (2020). https://doi.org/10.1016/j.procs.2020.02.171
Mumtaz, S.: Factors affecting teachers’ use of information and communications technology: a review of the literature. J. Inf. Technol. Teach. Educ. 9(3), 319–342 (2000). https://doi.org/10.1080/14759390000200096
Dillenbourg, P.: The evolution of research on digital education. Int. J. Artif. Intell. Educ. 26(2), 544–560 (2016). https://doi.org/10.1007/s40593-016-0106-z
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-14756-2_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-14755-5
Online ISBN: 978-3-031-14756-2
eBook Packages: Computer ScienceComputer Science (R0)