The resilience paradox: developing self-regulated capability in project teams

Digital technology has influenced the resilience paradigm for lifelong learning in projects. COVID-19 is seen as a precursor and accelerator to this change. We find ourselves in a hybridized world, combining (a)synchronous (in)formal teaching and learning in different physical and virtual spaces (Cohen et al., 2020). This seismic shift in teaching and learning has compelled the individual to self-regulate, adapt skills and capabilities in and for a new world of learning. This is depicted with individuals being immersed in a new reality and augmenting intelligence in the human-technology interface that enables big data collection (Raisamo et al., 2019). This data has the potential to track the learning process, recognize learning scenarios and connect learning communities, which can, in turn, enable a more authentic, flexible and adaptive learning process (Peng et al., 2019). The possibility for personalized adaptive learning to emerge in this process enables individuals to self-monitor their learning.
The next step to develop capable project teams is to strategically adopt Artificial Intelligence (AI) to meet new challenges or situations. This adaptation can be achieved through self-regulated learning situated in a shared context which occurs in project teams, where learning is situated and socially constructed with AI (Malmberg, 2017; Toivonen et al., 2019). This approach to learning emerges when group members work together with AI, complementing and sharing their perceptions and task goals. For example, group members explain a solution(s) to a task or problem, complementing and bringing new and additional information (Malmberg, 2017).
When successful, the socially shared regulation of learning allows individuals to achieve proactivity, coping ability, flexibility and persistence; the key attributes of resilience. At the project team level, this approach impacts the pedagogics of the learner, thus reinforcing self-directed learning in response to emerging complex challenges.
To experience the impact of this innovative learning design, and discover the nuances of creating this shared dynamic, the following complex case scenario is proposed: The Australian Victorian government announced plans to build a new Melbourne Airport Rail Link. A $AUD10 billion transformational smart public transport project aiming to connect Melbourne Airport to Victoria’s regional and metropolitan train network. Your project firm has been engaged to determine an innovative way to fund the project, and to manage the generation of ‘big data.’
We claim that this form of self-directed learning, in a socially reinforced regulated professional network, can enable contextually applicable ideas and solutions that enforce project team resilience. Pedagogically, this approach to learning is particularly appealing for project practitioners in the evolving human-computer interface, enabling them to augment their intelligence and adapt to technological changes in dynamic work settings. Here both professional and educational institutions have a remit to harness these new insights to enable practitioners to lead their profession to deliver a sustainable future.