Guest editorial: STARA (smart technology, AI, robotics and algorithms): implications for career research Free

Research on career transitions in the context of STARA has primarily emphasised the displacement of routine tasks and potentially roles, particularly in occupations where automation and robotics can substitute standardised work processes (Bahadure et al., 2024; Oosthuizen, 2019, 2022; Singh and Chandra, 2026; Singh et al., 2026). This has been accompanied by an emerging body of work highlighting the need to upskill and reskill workers in the face of organisational STARA adoption. Even in high-skilled jobs, workers are expected to adapt to technology-driven changes and integrate new digital competencies into their professional trajectories (Hani et al., 2025; Ibrahim and Abiddin, 2024; Singh and Chandra, 2026; Singh et al., 2026; Tariq, 2026).

However, one crucial and relatively poorly understood aspect of technology-induced career transitions is their degree of threat or voluntariness. Some transitions may be involuntary, such as job loss due to automation. In contrast, others are voluntary, such as moving into technology-intensive roles or professions out of personal interest or choice. The extent to which individuals “own” these transitions is likely to shape how meaningful the experience is perceived, how readily they can identify with their new roles and how they adjust psychologically (Bankins and Formosa, 2023; Selenko et al., 2022). Research on occupational groups that have long navigated technological disruption – for example, radiographers, radiologists (Stogiannos et al., 2025; Perez et al., 2024) and librarians (Nelson and Irwin, 2014) – can provide valuable insights into these processes.

Moreover, while research has primarily centred on high-skill contexts, less attention has been directed toward low- and medium-skill occupations, where STARA may also fragment or expand existing roles and limit, expand, or generate new career paths. These dynamics could pose unique challenges for workers navigating transitions without established trajectories, weak opportunities to voice concerns, or the necessary institutional support structures to buffer change, thereby potentially widening inequalities across occupational groups and particular cohorts of workers (Zajko, 2022).

Career development in the age of STARA highlights the expanding use of digital learning platforms and AI-based training tools as central mechanisms for supporting continuous skill acquisition and professional growth. In parallel, there is a growing recognition of self-managed, technology-supported approaches to career development, where individuals proactively engage with digital resources to source career advice, enhance their employability and navigate dynamic labour markets (Bankins et al., 2024a). For example, Yuan et al. (2026; in this special issue) demonstrate that AI usage at work can simultaneously enhance thriving as well as induce identity threat, with employees’ learning and performance goal orientations driving career growth. This highlights the simultaneous brighter and darker sides of AI usage, and the complex psychological processes through which technology influences professional development.

At the same time, important questions continue to challenge and potentially expand our understanding. One key issue is whether STARA-enabled development resources contribute to democratising access to opportunities or inadvertently reinforce existing inequalities by being more accessible to some workers than others (Özbilgin et al., 2025). Career advice and guidance that support career development may become more readily available via technology, but human advisors will still be needed, particularly for unique and complex cases (Bankins et al., 2024a). Another underexplored area is the long-term impact of algorithm-driven systems, such as AI-based career planning platforms and digital portfolio and performance trackers. These tools can influence career trajectories in multiple ways: for instance, they may provide on-demand, personalised feedback, suggest potential skill development pathways, or facilitate the management of digital career portfolios. At the same time, such systems may have limitations and unintended consequences. They can embed biases, amplify pressures for self-optimisation, provide only generic recommendations rather than tailored guidance and risk promoting a narrow view of what constitutes a “desirable” career – raising the question of whether optimal career trajectories can be defined or adequately captured by available data.

Research on career success in the context of STARA has increasingly focused on the use of algorithmic systems for productivity and performance monitoring (Giermindl et al., 2022; McCartney and Fu, 2022), highlighting both efficiencies and new pressures in technology-mediated work environments (Yang, 2022). Concurrently, attention has grown around the importance of well-being and work–life balance, as organisations and employees navigate the psychological and social implications of technologically enhanced performance tracking (Norlander et al., 2021), which can include the tracking of employees’ emotional and physiological responses at work and during their non-work time using sensors and other algorithmically enhanced data collection systems (Downie et al., 2025; Weston, 2015).

Understanding how STARA reshapes the determinants and perceptions of career success requires further investigation. One underexplored area is the role of algorithmic gatekeeping in career advancement, including opaque promotion processes and AI-filtered evaluations that may privilege certain behaviours or skill sets while limiting transparency and equity (Hillebrand et al., 2025). Metrics of career success – both objective (e.g. income, status) and subjective (e.g. meaningful work, recognition, career satisfaction) – are evolving in the context of changing work environments (Spurk et al., 2019; Shockley et al., 2016). However, when promotion and reward systems rely heavily on algorithmic assessments, such indicators of success risk being overshadowed by easily quantifiable productivity metrics, potentially marginalising less tangible but still meaningful dimensions of professional achievement. This also raises questions about how AI-based evaluation tools shape perceptions of performance and career success and how these technologies may redefine which skills, behaviours, or outcomes are recognised and rewarded in the workplace.

Moreover, in non-traditional labour market contexts, such as the platform economy, performance and career success are increasingly captured through alternative, often real-time metrics, highlighting a growing divergence from traditional indicators and raising questions about how conventional and non-traditional measures can be integrated to fully understand career outcomes. This raises additional questions about how new forms of work – such as hybrid, gig-based, digital nomadism, or AI-enhanced – are redefining what it means to be successful in a career (Reichenberger, 2017).

Super’s (1957) work decomposes the career span into stages that reflect three broad periods across the life course (noting that we do not focus on the growth stage): exploration and establishment, where workers crystallise a career preference, undertake training to achieve it and begin stabilising their careers; maintenance, where workers largely consolidate their achievements, but with openness to new challenges and ongoing upskilling; and disengagement, where workers move toward transitioning out of the workforce (Greenhaus and Callanan, 2006). Super’s model is age-agnostic, allowing workers to cycle through different stages regardless of their life stage. STARA technologies are influencing careers across each of these stages (Bankins et al., 2024a).

In entry-level careers, STARA technologies are increasingly recognised as offering both barriers and accelerators. In terms of barriers, there are growing concerns that entry-level positions, including those in high-skill occupations, may be reduced or transformed by STARA (Brynjolfsson et al., 2025; Lichtinger and Hosseini, 2025), thereby altering traditional routes for progression from junior to more senior roles and the opportunities for juniors to develop the expertise that facilitates advancement (Beane, 2024). In terms of accelerators, STARA technologies can enhance entry-level workers’ productivity (Mayer et al., 2025) and, alongside disrupting existing career trajectories, may also generate entirely new ones (Armour et al., 2020) by shifting the importance of some skills, training and expectations over others, leading to new chains of work experiences and careers. It may well be that such accelerators, like digital literacy, AI fluency and familiarity with robotics or algorithmic systems, may determine who gains access to early career opportunities, such as more complex project work, shaping the initial conditions of professional trajectories (Bankins et al., 2024a; Mayer et al., 2025).

Nevertheless, several important aspects remain underexplored. The long-term effects of STARA-based onboarding and how early career experiences of using these technologies impact professional identity and development are not well understood (Selenko et al., 2022), raising questions about how technology-mediated entry points influence career attitudes and growth trajectories (Russell, 2003). Similarly, the integration of STARA tools may be altering traditional mentorship and apprenticeship models, potentially reducing opportunities for experiential learning and personalised guidance for junior staff (Beane, 2018, 2024). Moreover, the temptation to fully outsource routine or basic tasks to AI may limit learning opportunities for newcomers, hindering their skill development and integration into the workforce Finally, the proliferation of generative AI and other automation technologies may affect not only the quantity of entry-level positions available to high-skill graduates but also their quality, potentially redefining what early professional work entails and the kinds of skills that are valued (Brynjolfsson et al., 2025; Mayer et al., 2025).

For mid-career professionals, research has largely emphasised upskilling and reskilling initiatives as critical strategies for navigating digital transformation, with a particular focus on leveraging online learning platforms and flexible, platform-based work arrangements to facilitate career switching and skill renewal (Bankins et al., 2024a; Lent, 2025). These interventions are often presented as mechanisms to maintain employability and adapt to rapidly evolving technological requirements.

Despite these insights, several key areas require further work. One concern is the potential for career plateaus or stagnation arising from STARA-induced role simplification or task automation, which may limit the scope of work and constrain opportunities for skill application and advancement. Conversely, technology can also create avenues for enriched career paths by reducing administrative burdens and enabling employees to engage in more strategic and meaningful work. However, more diverse empirical evidence on such outcomes across sectors is needed. Additionally, there is limited understanding of the extent to which organisations invest in reskilling and upskilling consistently across all career stages, or whether they focus on perceived entry-stage “digital natives,” raising questions about equity, access to development resources and long-term retention within digitally transforming workplaces.

Challenges to mid-career stages also arise from STARA disrupting established, often effective and practiced ways of working and problem-solving that workers may seek to maintain. If STARA is seen as taking important aspects of work away, or leading to disrupted task processes, identity threat perceptions may be likely, leading organisational initiatives to upskill workers to fail (Selenko et al., 2022).

Research on older workers in the context of STARA has primarily highlighted digital skill gaps and the associated risk of early exit from the labour market, as well as strategies to maintain professional status and expertise in the face of technology adoption (Aisa et al., 2023; Bankins et al., 2024a; Beane and Anthony, 2024). However, emerging evidence suggests that age and experience can support more effective use of generative AI by connecting the technology to deep domain knowledge and capabilities, such as through critical assessment and stronger quality control (Bozkurt et al., 2025). Older workers represent a valuable resource as mentors, knowledge integrators and even developers of AI agents and generative systems, contributing to organisational adaptation during technological transitions. Additionally, resistance to STARA adoption should not be framed solely as a deficit; it may also reflect critical engagement by providing feedback, ethical oversight, or nuanced perspectives that shape more effective and responsible technology integration (Bankins and Formosa, 2026).

The breadth of available STARA technologies means their application spans multiple, diverse sectors. Some industries, such as manufacturing, have been navigating technological changes since the first industrial revolution. Moreover, professions such as radiologists and surgeons in healthcare, and frontline service providers in retail and hospitality, have been facing occupational adaptation for decades. Creative workers have also experienced technological disruptions over time that have changed the creation and perception of art, music, film and other creative outputs. These workers are now also experiencing AI as a potential co-creator, contributing to design, writing, music and other artistic outputs (Magni et al., 2024), but also as a potential competitor for their work. Studies often examine how technological integration reshapes workflows, efficiency and occupational demand, providing a foundational understanding of sectoral change.

There will continue to be both patterns (i.e. recurring, generalisable trends) and particularities (i.e. context-specific, unique effects) in how different occupations experience changes brought by this new wave of STARA technologies, which warrant examination in their sectoral contexts. For example, in terms of patterns, comparative analyses of STARA-induced transitions versus previous waves of technological change in these sectors are scarce, limiting our ability to contextualise contemporary transformations within historical patterns of labour adaptation. In terms of particularities, in the creative industries context, career ownership and attribution in AI-enhanced outputs present significant challenges, as it is often unclear how credit and responsibility should be allocated between human creators and algorithmic systems (Chesterman, 2025; Epstein et al., 2020; Formosa et al., 2025). The proliferation of text-, image- and audio-generating AI is reshaping understandings of creative work and its collaborative value, calling for a reassessment of what constitutes originality, skill and professional contribution in these evolving contexts (McGuire et al., 2024).

In non-standard types of work, particularly within the platform economy, research has documented high levels of precarity and algorithmic control, highlighting how digital management systems regulate task allocation, performance evaluation and work intensity (Hofer and Spurk, 2025; Hofer and Spurk, in press). Scholars have also explored concepts such as “calling” in platform-based work (Affolter et al., 2024; Affolter et al., 2026, in this special issue) and the implications for career development in these flexible but often fragmented work arrangements (Zwettler et al., 2024).

This area of scholarship would benefit from exploring wider time horizons for the outcomes of this type of work. For example, long-term career narratives and the sustainability of platform-based or digital nomad careers are not well understood, raising questions about how these workers navigate professional growth over time in highly flexible environments (Hofer and Spurk, 2025; Zwettler et al., 2024). Additionally, STARA technologies may contribute to career fragmentation by limiting clear advancement ladders, reducing feedback opportunities and constraining traditional growth pathways (see Duggan et al., 2021). Conversely, these same technologies could enable greater engagement with entrepreneurial career paths by, for example, lowering barriers associated with resource constraints; yet empirical evidence on such outcomes remains limited.

The rise of AI-based career coaching, chatbots and digital mentoring platforms has become a central focus in research on technology-enhanced career support (Ebner, 2025; Patil et al., 2024). These tools offer scalable guidance, personalised advice and flexible support to help workers navigate complex career paths.

While expanding access to career supports via technology is an important goal, it raises questions about the nature and extent of human input into these supports and whether widened access via STARA technologies may compromise or enhance the depth and nuance of career advice. For example, the experience of technology-mediated mentoring and coaching is not well understood, raising concerns about whether AI can replicate or complement the nuanced guidance offered by human mentors, coaches and counsellors (Terblanche et al., 2022). STARA systems in career management may, on the one hand, be less biased than a human HR decision-maker with less training, fewer skills and limited awareness of their own biases. On the other hand, these systems can also exhibit bias, as their decision-making depends solely on existing data, which may itself be skewed. Similarly, algorithmic advice may reproduce biases, overlook context-specific, human-centred considerations and possibly disregard niche or non-standard career paths that are underrepresented in available datasets, potentially limiting its effectiveness and fairness (Bankins et al., 2024a). On the positive side, digital platforms could significantly improve accessibility to career guidance and reduce its costs, ultimately widening access to career development resources and enabling previously underserved populations to engage with professional career guidance. More work is needed to understand the benefits and costs of technology-only, human-only and hybrid forms of career counselling, and to strive toward equal access to high-quality guidance.

Ethical concerns extend beyond specific tools or sectors, encompassing structural biases, transparency and accountability in automated decision-making that can be driven by STARA technologies. While examples of technology-informed recruitment and selection, mentoring and performance tracking have each highlighted instances of bias, unfair treatment and experiences of dehumanisation (Hunkenschroer and Luetge, 2022), there remains a need for a comprehensive understanding of how STARA systems collectively influence meaningful work (Bankins and Formosa, 2023) and access to career opportunities across various stages and contexts. Key issues include the ethical implications of automating critical human resource decision-making systems that can directly impact career outcomes – such as hiring, promotions, or terminations – and ensuring that these systems do not reflect or perpetuate structural inequities that may reinforce existing labour market disparities. Addressing these concerns requires socio-technical approaches that foreground not only important technical solutions but also identify appropriate governance frameworks, organisational policies, leadership approaches and deployment options that help ensure technology-mediated career systems are both fair and socially responsible.

Research on precariousness in the age of STARA has largely emphasised occupational and career insecurity (Hofer and Spurk, 2025; Hofer and Spurk, in press; Roll et al., 2023; Spurk et al., 2022). Global occupation insecurity refers to employees’ fear that their occupations might disappear, and content occupation insecurity addresses employees’ concern that the tasks comprising their occupations might significantly change due to automation (Roll et al., 2023). In uncertain times characterised by converging geopolitical, social, climate and technological instabilities, job insecurity, occupational insecurity and career insecurity (Hofer and Spurk, 2025; Hofer and Spurk, in press; Spurk et al., 2022), concerns are gaining momentum. Moreover, algorithmic management and STARA-driven automation are associated with more and varied forms of precarious work, such as those found in the platform economy, which are arguably contributing to increasing levels of uncertainty and unstable workloads for such workers (Hofer and Spurk, 2025; Hofer and Spurk, in press). For instance, Meijerink et al. (2026, in this special issue) demonstrate that career shocks in platform-based work, such as the sudden disappearance of Deliveroo in the Netherlands, trigger divergent responses: economically dependent workers tend toward career inaction, whereas those with strong core self-evaluations engage in career crafting (e.g. proactive career reflection and construction), highlighting how personal and contextual factors shape reactions to STARA-induced precarity. Affolter et al. (2026, in this special issue) also show divergent worker responses to living a calling through gig work.

Despite these insights, we highlight notable areas for future research. First, distinctions between job insecurity, occupation insecurity and career insecurity require further clarification, as each dimension may have different implications for worker behaviour, motivation and long-term development (Sinclair et al., 2024). It is also important to understand how the extent of STARA knowledge, understanding and experience with these technologies relates to such stressors. Second, the psychological impacts of constantly adapting to evolving technologies – such as experiencing burnout, stress and strain – are not well understood. Finally, while STARA presents challenges, it also creates opportunities for new jobs, roles and professions, highlighting the potential for career renewal and innovation even in precarious, rapidly changing labour contexts.

Research on the future of work has increasingly emphasised the importance of skills such as communication, critical thinking and emotional intelligence, highlighting their relevance for navigating STARA-driven transformations (Bankins et al., 2024c; Oosthuizen, 2022; Suvarna et al., 2024). These competencies are often framed as essential for sustaining employability in a dynamic, technology-enhanced labour market.

While these insights are important, predicting what new forms of work, skills and career avenues will emerge in response to technological progress is often more challenging. For example, a key gap concerns the creation of entirely new jobs and professions emerging from STARA technologies, as well as the pathways through which these opportunities develop. Equally important is understanding how workers engage in job crafting, adjusting the design of their roles to align with technological change and evolving organisational needs. For example, Tian et al. (2026) demonstrate that employees who effectively perceive AI-related opportunities develop personal knowledge management skills. This enables them to acquire, store and apply information efficiently, thereby enhancing their competencies and sustaining their career trajectories amid rapid technological changes in the workplace. Beyond immediate job adjustments, little is known about proactive career crafting (Tims and Akkermans, 2020) - how individuals strategically shape their career trajectories in STARA-exposed occupations to capitalise on emerging opportunities and maintain professional agency over time, or whether they notice STARA in their profession at all (Nazareno and Schiff, 2021), particularly in industries where the technology's impact might be more subtle or less visible. Such investigations could be informed by future-oriented theoretical frameworks, such as individuals’ perceptions of their future work selves and the relationship to engaging with STARA technologies at work (Voigt and Strauss, 2024).

By bringing together diverse methodologies and perspectives, this Special Issue illuminates how STARA technologies are reshaping careers across contexts and skill levels. Yuan et al. (2026) reveal that AI usage simultaneously fosters thriving and induces identity threats, highlighting the complex psychological pathways that influence career growth. Complementing this, Affolter et al. (2026) uncover how living a calling can be enacted in the precarious context of gig work, leading to both adaptive and maladaptive implications for people’s development in this form of work. Moreover, Tian et al. (2026) show that perceiving AI opportunities enables employees to develop knowledge management skills, supporting sustainable adaptation in rapidly evolving workplaces. Salcedo-Gil et al. (2026) extend this insight to human–robot collaboration, emphasising career adaptability as a key mechanism for integrating emerging technologies. At the same time, Meijerink et al. (2026) highlight the precarity of platform-based work, showing how career shocks elicit divergent responses depending on economic dependency and core self-evaluation. Finally, Neufeld et al. (2026) contextualise these dynamics, highlighting how socioeconomic status, education and work characteristics play an important role in access to AI-driven career opportunities. The following sections offer a closer examination of each contribution, highlighting how these studies collectively enhance our understanding of STARA’s multifaceted impact on careers, ranging from individual psychological processes to structural and contextual influences.

Yuan et al. (2026) examine how the use of artificial intelligence at work influences employee career growth through dual self-regulation mechanisms, drawing on Self-Regulation Theory. Using a three-wave study of 495 AI-active employees across various industries, the authors demonstrate that AI usage simultaneously fosters thriving at work and triggers an identity threat. Thriving enhances career growth, while identity threat motivates employees to increase effort, creating a complementary pathway to development. Notably, learning goal orientation strengthens the link between thriving and career growth. In contrast, performance goal orientation amplifies the effect of identity threat but weakens the relationship between thriving and career growth. This dual-pathway framework advances understanding of how AI can support career development while highlighting the complex psychological processes employees navigate in technology-intensive work environments.

Affolter et al. (2026) focus on the STARA-enabled context of gig work. They uncover how living a calling can generate both work-related benefits and drawbacks for gig workers. Using latent profile analysis, the authors identified four profiles of living a calling and excessive working within a sample of 723 workers who worked through online labour platforms. The results showed that the Excessive Calling Enactment profile had significantly higher burnout symptoms than the Balanced profile, although job satisfaction levels were similar between the two. Gig work challenges and platform surveillance also significantly predicted membership in the Excessive Calling Enactment profile over the Balanced profile, suggesting that precarious platform environments can trigger unhealthy ways of pursuing a calling. By connecting the gig work context with research on living out a calling, the authors shed light on experiences of technology-mediated careers.

The research by Tian et al. (2026) examines how employees navigate opportunities and challenges to thrive and sustain their careers in workplaces that continuously introduce technology and AI in their operations. Employees’ ability to perceive AI opportunities prompts the development of personal knowledge management, enabling them to acquire, store and apply information efficiently. This capacity enables them to enhance their professional competencies and alleviate concerns about unemployment and other forms of career-related insecurity, thereby placing employees on a sustainable career trajectory. They can effectively adapt to meet the demands of dynamic and highly complex work designs accelerated by advanced technologies. The findings of this research present an overview of how employees can adapt to the rapid implementation of STARA in the workplace.

Salcedo-Gil et al. (2026) explore how employees respond to adaptability challenges when faced with the introduction of robots in their workplace. They interviewed 18 Dutch employees working in two logistics companies and an energy company who worked side by side cooperatively with robots, regarding their experiences, their experienced changes to their job tasks and responsibilities and how that affected their learning and training. They found that how workers framed those changes depended on what they called their personal adaptability resources: people who were confident and curious were more actively seeking help. These resources, however, were dependent on time and experience. The authors argue that adapting to robots does not only require a technical skill shift, but also a cognitive shift: people have to overcome fears of job insecurity, rethink how they work and redefine their work-related identity. The lesson for employers is that technological training and reskilling alone are not enough to assist workers with robots; support for sensemaking is also needed.

The research by Meijerink et al. (2026) delves into the precarity of careers in platform-based work. They examined how career shocks, marked by disruptive and extraordinary events, can trigger career crafting and inaction. The sudden disappearance of Deliveroo, an online food delivery platform operating in the Netherlands, pushed many riders out of the gig economy. Whether they engaged in career crafting or career inaction depends mainly on their economic dependency on the app and their core self-evaluation. Indeed, those who were economically dependent on the platform experienced it as a negative career shock, which increases career inaction. Meanwhile, those who exhibit core self-evaluations experienced it as a positive career shock, which increases career crafting. These results emphasise the cognitive processes that underpin how gig workers respond to career events.

Neufeld et al. (2026) explore the new phenomenon of generative AI use and AI literacy, in relation to work characteristics, demographic characteristics and birth family background, using a convenience sample of employees across a broad range of industry sectors and occupations. As expected, the authors found that a greater use of AI was related to a more positive attitude towards AI and a higher perceived AI literacy. They also found that people in more enriched jobs (higher in beneficial work characteristics) were more likely to think more positively, use and feel competent in their AI use as well. Perhaps more curiously, the authors also found a surprising effect of birth family: if people’s parents were blue-collar workers or farmers, they showed fewer positive attitudes, less frequent use and felt less competent in their AI use, compared to people whose parents were self-employed academic professionals. While these findings certainly ask for replication on a larger scale, they do resonate with established sociological research on the long-lasting effects of social class. If we presume that positive AI attitudes, more use and AI competence all contribute to better career outcomes, then this study paints a quite alarming picture: those in better starting positions, more highly educated and in already more enriched jobs are likely to fare better in their careers with AI over the longer term. The question this raises is how we can help all workers, not just those from advantaged backgrounds, to successfully utilise and adapt their jobs with the aid of AI.

This Special Issue highlights how STARA technologies are reshaping career transitions, career development and career success, while also exploring additional emerging topics in the field. The contributions demonstrate that STARA can enhance skill development, foster thriving environments and enable calling; however, they also introduce identity threats, precarity and complex pressures. Sectoral and contextual factors, from human–robot cooperation to platform-based work and socioeconomic conditions, further shape career trajectories.

However, there remains much to explore, particularly regarding long-term career trajectories, whether structural labour market inequalities persist or decline and the ethical implications of STARA-affected and STARA-mediated career systems. Adopting socio-technical approaches (e.g. in online labour platform design) will be crucial to emphasise both technical design and adaptation, as well as the social, emotional and contextual dimensions associated with technology deployment, while also foregrounding diverse populations and non-standard career paths. Future studies that adopt human-centred, inclusive and ethical approaches to career development and integrate individual, organisational and broader labour market perspectives will help provide evidence-based insights to support equitable, sustainable and meaningful career trajectories.