The increasing importance of workplace learning is evident as we continually encounter situations lacking predefined models, guidelines, interpretations, tools, or solutions. These complex scenarios demand immediate resolution within the workplace context (Harteis, 2022). However, constant changes and accelerating pace may induce stress and challenge mental well-being (Blomgren & Perhoniemi, 2022) if not addressed with appropriate tools and strategies (Hobfoll, 1989). The growing pressures on learning and skill development necessitate a re-evaluation of learning methods, practices, and techniques (Nissinen et al., 2022; 2023). The workplace is thus challenged to embrace flexible thinking and develop innovative tools for work transformation (Markauskaite & Goodyear, 2017). It is not just about surviving at work, but being able to feel a healthy enthusiasm and work engagement.

   The most recent variable, Artificial Intelligence (AI), brings demands for rapid and flexible renewal in the work context (Halonen et. al., 2023). However, people may not have enough energy and resources for learning new things if they are constantly in a state of overburden (Knight et al., 2021). Additionally, the ways job crafting is done, can become burdensome and even threaten well-being at work (Nissinen et al., 2023). AI stands out from earlier technologies due to its capacity for (semi-)independent action (Maedche et al., 2019; Rieder et al., 2020; Scherer, 2016). Recent advancements in generative AI, notably the advancing sophistication of Large Language Models (LLMs), are enhancing the significance and adoption of AI-driven technologies in organizational contexts (Dwivedi et al., 2023; Markus and Rowe, 2023)

   The role of artificial and supportive intelligences in workplace learning can be examined through system-theoretical lenses. Artificial Intelligence (AI) can be perceived as an integral system component, coexisting with human actors, essential for the collaborative creation of new knowledge. Consequently, AI can reshape the system (practices) and introduce novel inputs into discussions, which individuals or teams could not generate without technology (Halonen et al., 2023).

    In job crafting interventions, the rapid evolution of technology is seen as a driving force for the continual acceleration of workplace learning (Van Wingerden et al., 2017). We use Job Demands-Resources Theory (Demerouti et al., 2001) in developing a sustainable job crafting model, where AI is utilized as a systemic resource to reshape and craft work practices. Our goal is to interrupt possible burdensome cycles at work and introduce a model which aims to decrease workload, increase job crafting, work engagement, well-being and professional networks. Our model combines job crafting strategies, AI and network crafting, and leverages research on job crafting intervention models (Knight et al., 2021; Roczniewska et al., 2023), particularly from the perspective of sustainable work practices. We recognize the agentic role of AI technologies which radically changes the flow of information and interactions. Our perspective of AI extends beyond merely accelerating tasks and supplying pre-formulated solutions. We envision it as a catalyst for novel types of network intelligence, stimulating collective engagement and provoking epistemic emotions that cultivate creativity, dedication, and elements of higher-level learning (problem solving, critical thinking, creativity) which are also crucial at workplace learning. We presented the issue at a National School Principal Conference in Helsinki, Finland in November 2023. Twenty of the conference participants informed us that they were interested in participating in the pilot of the sustainable job crafting model. We aim to gather max. 50 participants in this study.

To test the hypotheses we will conduct two repeated self-evaluative measurements and multivariate analyses of covariance (MANCOVA). In measurements we utilize the job Crafting Scale to measure job crafting (Tims et al., 2012) and UWES-9 to measure work engagement (Schaufeli et al., 2006). We will also measure workload (van Veldhoven & Meijman, 1994) and we adopt measurement from Wang et al. (2024) to investigate network behavior. Pre-test and post-test also include semi-structured qualitative methods which strengthen the quantitative data, particularly in the use of AI.

Expected outcomes: We hypothesize that 1) participants´ job crafting behavior increase via sustainable job crafting, 2) participants´ workload decrease via sustainable job crafting, 3) participants´ job engagement increase via sustainable job crafting, 4) participants increase their conscious use of AI in their own job and in collaborative processes, and 5) participants´ increase their network size and network diversity through the mediation of tailored network crafting actions (i.e. using existing contacts, establishing new contacts, maintaining professional contacts).

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