Competency Modeling in a Digital Age: Redefining skills and capabilities for a technologically evolving workforce

Abstract: The rapid advancement of artificial intelligence (AI) and emergent technologies has revolutionized how tasks are performed across various domains (Dwivedi et al., 2021), in turn requiring a shift in the traditional competency model. These models now require more frequent updates to reflect the dynamic nature of technological evolution. In some contexts, AI surpasses human capabilities entirely (Zhang et al., 2020), consequently reshaping the landscape of required skills and knowledges for the tasks within the job, and requiring a deeper focus on improved decision-making. This transformation introduces a dual challenge: identifying and emphasizing new competencies to support decision-making while simultaneously reassessing tasks that are either obsolete or augmented by AI systems. For example, in areas like aviation, emergent aircraft designs have created a shift in which tasks that were previously reliant on humans (Vempati et al., 2021) such as controlling 8-rotors on an electric vertical takeoff and landing (eVTOL) aircraft, are now feasible only through AI systems. These agents often achieve optimal performance levels unattainable by humans, rendering traditional training for these tasks unnecessary. Conversely, in fields where AI complements rather than replaces human capabilities, such as cyber and intelligence, new responsibilities and knowledge requirements are being appended to existing roles. These additional knowledge, skills, and/or task requirements can lead to increased cognitive and operational workloads for trainees (Strauch, 2017). This dichotomy highlights the importance of distinguishing between the roles where training could be minimized due to automation and those where training must be expanded to accommodate the new tasks due to these technologies. Competency models in this digital age need to adapt to consider tasks based on their relevance and the level of AI integration. Models should be updated to include more decision making and highlight collaboration with AI systems to address the balance between task automation and human involvement. These changes can help to ensure that training programs remain efficient and relevant. The current work explores these challenges and offers a framework for designing competency models that reflect the evolving technological landscape. Further we propose strategies for identifying and incorporating updated competencies, emphasize the need for continuous model refinement, and outline methods to balance training requirements with operational demands. Key considerations include integrating AI-awareness into competency frameworks, reducing redundant training efforts, and fostering skills that enhance human-AI collaboration.By addressing these evolving needs, competency models can better prepare individuals for the demands of the digital age while promoting efficiency and adaptability in training programs. The paper aims to provide actionable insights and key considerations for organizations and educators tasked with developing competency frameworks. Ultimately, this work seeks to bridge the gap between technological capabilities and human potential, empowering individuals to thrive in increasingly AI-driven environments.ReferencesDwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994.Strauch, B. (2017). Ironies of automation: Still unresolved after all these years. IEEE Transactions on Human-Machine Systems, 48(5), 419-433.Vempati, L., Geffard, M., & Anderegg, A. (2021, October). Assessing human-automation role challenges for urban air mobility (UAM) operations. In 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) (pp. 1-6). IEEE.Zhang, X. Y., Liu, C. L., & Suen, C. Y. (2020). Towards robust pattern recognition: A review. Proceedings of the IEEE, 108(6), 894-922.

Keywords: Competency Modelling, Training, Automation, Human-Agent Teams

DOI: 10.54941/ahfe1006650

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