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Practice With Less AI Makes Perfect: Partially Automated AI During Training Leads to Better Worker Motivation, Engagement, and Skill Acquisition

Author

Listed:
  • Mario Passalacqua

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal)

  • Robert Pellerin

    (MAGI - Département de Mathématiques et de Génie Industriel - EPM - École Polytechnique de Montréal)

  • Esma Yahia

    (LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies)

  • Florian Magnani

    (CERGAM - Centre d'Études et de Recherche en Gestion d'Aix-Marseille - AMU - Aix Marseille Université - UTLN - Université de Toulon, ECM - École Centrale de Marseille)

  • Frédéric Rosin

    (LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies)

  • Laurent Joblot

    (LISPEN - Laboratoire d’Ingénierie des Systèmes Physiques et Numériques - Arts et Métiers Sciences et Technologies)

  • Pierre-Majorique Léger

    (HEC Montréal - HEC Montréal)

Abstract

The increased prevalence of human-AI collaboration is reshaping the manufacturing sector, fundamentally changing the nature of human work and training needs. While high automation improves performance when functioning correctly, it can lead to problematic human performance (e.g., defect detection accuracy, response time) when operators are required to intervene and assume manual control of decision-making responsibilities. As AI capability reaches higher levels of automation and human-AI collaboration becomes ubiquitous, addressing these performance issues is crucial. Proper worker training, focusing on skill-based, cognitive, and affective outcomes, and nurturing motivation and engagement, can be a mitigation strategy. However, most training research in manufacturing has prioritized the effectiveness of a technology for training, rather than how training design influences motivation and engagement, key to training success and longevity. The current study explored how training workers using an AI system affected their motivation, engagement, and skill acquisition. Specifically, we manipulated the level of automation of decision selection of an AI used for the training of 102 participants for a quality control task. Findings indicated that fully automated decision selection negatively impacted perceived autonomy, self-determined motivation, behavioral task engagement, and skill acquisition during training. Conversely, partially automated AI-enhanced motivation and engagement, enabling participants to better adapt to AI failure by developing necessary skills. The results suggest that involving workers in decision-making during training, using AI as a decision aid rather than a decision selector, yields more positive outcomes. This approach ensures that the human aspect of manufacturing work is not overlooked, maintaining a balance between technological advancement and human skill development, motivation, and engagement. These findings can be applied to enhance real-world manufacturing practices by designing training programs that better develop operators' technical, methodological, and personal skills, though companies may face challenges in allocating substantial resources for training redevelopment and continuously adapting these programs to keep pace with evolving technology.

Suggested Citation

  • Mario Passalacqua & Robert Pellerin & Esma Yahia & Florian Magnani & Frédéric Rosin & Laurent Joblot & Pierre-Majorique Léger, 2024. "Practice With Less AI Makes Perfect: Partially Automated AI During Training Leads to Better Worker Motivation, Engagement, and Skill Acquisition," Post-Print hal-04487695, HAL.
  • Handle: RePEc:hal:journl:hal-04487695
    DOI: 10.1080/10447318.2024.2319914
    Note: View the original document on HAL open archive server: https://hal.science/hal-04487695v1
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    References listed on IDEAS

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    1. César Martínez-Olvera & Jaime Mora-Vargas, 2019. "A Comprehensive Framework for the Analysis of Industry 4.0 Value Domains," Sustainability, MDPI, vol. 11(10), pages 1-21, May.
    2. Macey, William H. & Schneider, Benjamin, 2008. "The Meaning of Employee Engagement," Industrial and Organizational Psychology, Cambridge University Press, vol. 1(1), pages 3-30, March.
    3. Monica Molino & Claudio G. Cortese & Chiara Ghislieri, 2020. "The Promotion of Technology Acceptance and Work Engagement in Industry 4.0: From Personal Resources to Information and Training," IJERPH, MDPI, vol. 17(7), pages 1-15, April.
    4. Shehan Jayasekera & Edward Hensel & Risa Robinson, 2021. "Feasibility of Using the Hexoskin Smart Garment for Natural Environment Observation of Respiration Topography," IJERPH, MDPI, vol. 18(13), pages 1-15, June.
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    Cited by:

    1. Loïc Couture & Mario Passalacqua & Laurent Joblot & Florian Magnani & Robert Pellerin & Pierre-Majorique Léger, 2024. "Adaptive System to Enhance Operator Engagement during Smart Manufacturing Work," Post-Print hal-04602719, HAL.
    2. Mario Passalacqua & Robert Pellerin & Florian Magnani & Philippe Doyon-Poulin & Laurène Del-Aguila & Jared Boasen & Pierre-Majorique Léger, 2024. "Human-centred AI in industry 5.0: a systematic review," Post-Print hal-04723054, HAL.

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    Keywords

    Human-centered AI; training curriculum; motivation; self-determination theory; industry 5.0;
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