Maritza A. Lara-Lopez, I. Rebollido, A. Vidal-Garcia
et al.
We present the results of a comprehensive survey conducted among members of the Spanish Astronomical Society (Sociedad Espanola de Astronomia, SEA) to assess well-being, professional satisfaction, and family-work balance of researchers in astronomy. The survey addressed multiple aspects of professional life, including happiness, career stability, publication pressure, and access to childcare services during scientific meetings. Responses were examined across gender and career stages to identify trends and sources of dissatisfaction.
Amidst constant societal, environmental and labour market changes, people are urged to be resilient and adaptable in their careers. Career guidance often portrays career resilience and adaptability as measurable traits, overlooking the privileging, discriminating, oppressing or marginalising impact of multiple systems (e.g. social, economic, political and educational) on them. The systems theory framework of career development offers a holistic view of the complex systems influencing career adaptability and resilience. This conceptual article proposes systems thinking as a perspective that links career adaptability and resilience with social justice towards more effectively addressing the needs of a diversifying population.
This study introduces the AI-Accentuated Career Transitions framework, advancing beyond binary automation narratives to examine how distinct AI usage patterns reshape occupational mobility. Analyzing 545 occupations through multivariate modeling, we identify six qualitatively distinct human-AI usage patterns that differentially predict placement across job preparation zones. Our findings empirically validate the "missing middle" hypothesis: automation-focused usage strongly predicts lower job zone placement while augmentative usage predicts higher zones. Most significantly, we identify specific Knowledge, Skill, and Abilities combinations with AI usage patterns that function as "skill bridges" facilitating upward mobility. The interaction between task iteration AI usage and cognitive skills emerges as the strongest advancement predictor, creating pathways across traditionally disconnected occupational categories. Counterintuitively, despite directive AI's negative main effect, its interaction with technical knowledge positively predicts advancement in specialized domains. Comparative model testing confirms that AI usage patterns represent a distinct dimension of occupational classification that adds significant explanatory power beyond traditional skill measures. These findings reveal AI as a skill amplifier that widens capability gaps rather than an equalizing force. The 2ACT framework provides strategic guidance for workers, curriculum designers, policymakers, and organizations navigating increasingly AI-mediated career pathways.
Responsible AI (rAI) guidance increasingly promotes stakeholder involvement (SHI) during AI development. At the same time, SHI is already common in commercial software development, but with potentially different foci. This study clarifies the extent to which established SHI practices are able to contribute to rAI efforts as well as potential disconnects -- essential insights to inform and tailor future interventions that further shift industry practice towards rAI efforts. First, we analysed 56 rAI guidance documents to identify why SHI is recommended (i.e. its expected benefits for rAI) and uncovered goals such as redistributing power, improving socio-technical understandings, anticipating risks, and enhancing public oversight. To understand why and how SHI is currently practised in commercial settings, we then conducted an online survey (n=130) and semi-structured interviews (n=10) with AI practitioners. Our findings reveal that SHI in practice is primarily driven by commercial priorities (e.g. customer value, compliance) and several factors currently discourage more rAI-aligned SHI practices. This suggests that established SHI practices are largely not contributing to rAI efforts. To address this disconnect, we propose interventions and research opportunities to advance rAI development in practice.
Ronja Mehlan, Claudia Hess, Quintus Stierstorfer
et al.
As artificial intelligence becomes increasingly integrated into digital learning environments, the personalization of learning content to reflect learners' individual career goals offers promising potential to enhance engagement and long-term motivation. In our study, we investigate how career goal-based content adaptation in learning systems based on generative AI (GenAI) influences learner engagement, satisfaction, and study efficiency. The mixed-methods experiment involved more than 4,000 learners, with one group receiving learning scenarios tailored to their career goals and a control group. Quantitative results show increased session duration, higher satisfaction ratings, and a modest reduction in study duration compared to standard content. Qualitative analysis highlights that learners found the personalized material motivating and practical, enabling deep cognitive engagement and strong identification with the content. These findings underscore the value of aligning educational content with learners' career goals and suggest that scalable AI personalization can bridge academic knowledge and workplace applicability.
Computing education and computing students are rapidly integrating generative AI, but we know relatively little about how different pedagogical strategies for intentionally integrating generative AI affect students' self-efficacy and career interests. This study investigates a SPIRAL integration of generative AI (Skills Practiced Independently, Revisited with AI Later), implemented in an introductory undergraduate creative media and technology course in Fall 2023 (n=31). Students first developed domain skills for half the semester, then revisited earlier material integrating using generative AI, with explicit instruction on how to use it critically and ethically. We contribute a mixed methods quantitative and qualitative analysis of changes in self-efficacy and career interests over time, including longitudinal qualitative interviews (n=9) and thematic analysis. We found positive changes in both students' creative media self-efficacy and generative AI use self-efficacy, and mixed changes for ethical generative AI use self-efficacy. We also found students experienced demystification, transitioning from initial fear about generative AI taking over their fields and jobs, to doubting AI capability to do so and/or that society will push back against AI, through personal use of AI and observing others' use of AI vicariously. For career interests, our SPIRAL integration of generative AI use appeared to have either a neutral or positive influence on students, including widening their perceived career options, depending on their view of how AI would influence the career itself. These findings suggest that careful pedagogical sequencing can mitigate some potential negative impacts of AI, while promoting ethical and critical AI use that supports or has a neutral effect on students' career formation. To our knowledge our SPIRAL integration strategy applied to generative AI integration is novel.
We examine how career concerns influence the behavior and mobility of financial advisers. Drawing on a uniquely comprehensive matched panel that combines employer-employee data with a longstanding national ranking, our study tests predictions from classic career concerns models and tournament theory. Our analysis shows that, in the early stages of their careers, advisers destined for top performance differ significantly from their peers. Specifically, before being ranked, these advisers are twice as likely to obtain a key investment license, experience customer disputes at rates up to seven times higher, and transition to firms with 80% larger total assets. Moreover, we find that top advisers mitigate the potential costs of their higher risk-taking by facing reduced labor market penalties following disciplinary actions. Leveraging exogenous variation from the staggered adoption of the Broker Protocol through an event-study framework, our results reveal dynamic sorting: firms attract high-performing advisers intensely within a short post-adoption period. These findings shed new light on the interplay between career incentives, risk-taking, and labor market outcomes in the financial services industry, with important implications for both firm performance and regulatory policy.
The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration status, or lack of flexible work options can significantly impact women's career progress, creating barriers for returning as lecturers, professors, or senior researchers. Although many companies promote gender diversity policies, such measures are less prominent and often under-recognized within academic institutions. Our goal is to explore the specific challenges women encounter when re-entering academic roles compared to industry roles; to understand the institutional perspective, including a comparative analysis of existing policies and opportunities in different countries for women to return to the field; and finally, to provide recommendations that support transparent hiring practices. The research project will be carried out in multiple universities and in multiple countries to capture the diverse challenges and policies that vary by location.
AbstractSustainability, a focus of attention in many contexts including career development, is a systems problem. Systems thinking is essential to consider, and find solutions to, sustainability. Career development’s responses to issues such as poverty, gender inequality, and environmental issues can be underpinned by social justice. This article considers the question “What are the implications of the sustainable development agenda for career development?” to enhance understanding of the UN Sustainable Development Goals (SDGs) and foster awareness of the relationship between career development and sustainability. Social justice and systems thinking are proposed as lenses for engaging with sustainability.
The present study focuses on persistence in research productivity over the course of an individual's entire scientific career. We track 'late-career' scientists - scientists with at least 25 years of publishing experience (N=320,564) - in 16 STEMM (science, technology, engineering, mathematics, and medicine) and social science disciplines from 38 OECD countries for up to five decades. Our OECD sample includes 79.42% of late-career scientists globally. We examine the details of their mobility patterns as early-career, mid-career, and late-career scientists between decile-based productivity classes, from the bottom 10% to top 10% of the productivity distribution. Methodologically, we turn a large-scale bibliometric dataset (Scopus raw data) into a comprehensive, longitudinal data source for research on careers in science. The global science system is highly immobile: half of global top performers continue their careers as top performers and one-third of global bottom performers as bottom performers. Jumpers-Up and Droppers-Down are extremely rare in science. The chances of moving radically up or down in productivity classes are marginal (1% or less). Our regression analyses show that productivity classes are highly path dependent: there is a single most important predictor of being a top performer, which is being a top performer at an earlier career stage.
The decisions regarding a prospective career choice and the paths leading to it are life-changing actions for any student. Engineering students particularly have plenty of career opportunities to choose from after their graduation or even during campus placements. The wide gamut of opportunities may sometimes cause engineering students to end up in career paths that do not match their aptitudes, skills, and personality traits. Developing efficient career prediction and guidance systems exclusively for engineering students is a pressing priority. However, there is a scarcity of research studies on automated career prediction systems for engineering education settings. Against this backdrop, we propose a novel solution rooted in artificial intelligence titled Graphology-based Career Analysis and Prediction System (G-CAPS). Advanced graphology tools are employed to connect handwriting features with the personality traits of individual students. The Holland theory of vocational interests is adopted in G-CAPS to characterize and model individual career interests. Existing literature indicates that no such graphology-based prediction system was developed based on vocational personality traits. The G-CAPS model can be trained and tested using the handwriting samples collected from engineering students and working professionals with engineering degrees. Distinct handwriting features are captured and processed utilizing an array of Convolutional Neural networks (CNN). The system architecture development of the model and its working process is particularised in the paper. It is anticipated that the G-CAPS model can soundly address the career path selection issues of engineering students and graduates looking for a job. The innovative prediction system can be scaled to assist engineering students and graduates across the globe in selecting potential career paths most suitable for their specific character traits.
This study aims to evaluate the effectiveness of industry-education integration on student career development by assessing the vocational personality traits and achievement motivation levels of 994 first-year vocational students from Shanghai Business Accounting School. The MBTI (Myers-Briggs Type Indicator) career personality questionnaire and achievement motivation level questionnaire were employed to measure the outcomes of industry-education integration and provide guidance for career guidance education and enterprise internships for vocational students. The findings indicate that among the four temperament types, the largest proportion is observed among idealists, accounting for 31.3%, while empiricists have the smallest representation at 14.6% among the first-year vocational students. Regarding achievement motivation, the overall achievement motivation score of the first-year vocational students is below zero, indicating a relatively low level of achievement motivation. Specifically, both empiricists and idealists demonstrate a relatively low level of achievement motivation in terms of vocational personality traits. A follow-up assessment of achievement motivation levels was conducted after a short-term enterprise internship, revealing a significant improvement in the achievement motivation levels of the vocational students. This suggests that the dual-subject educational model under industry-education integration can effectively enhance students' vocational skills and activate their internal motivation, thereby improving their achievement motivation and overall competitiveness. By leveraging industry-education integration, the school aims to cultivate high-quality talents and address specific talent shortages in the market, ultimately achieving mutual success between talents and the market.
Transformers have been widely used for video processing owing to the multi-head self attention (MHSA) mechanism. However, the MHSA mechanism encounters an intrinsic difficulty for video inpainting, since the features associated with the corrupted regions are degraded and incur inaccurate self attention. This problem, termed query degradation, may be mitigated by first completing optical flows and then using the flows to guide the self attention, which was verified in our previous work - flow-guided transformer (FGT). We further exploit the flow guidance and propose FGT++ to pursue more effective and efficient video inpainting. First, we design a lightweight flow completion network by using local aggregation and edge loss. Second, to address the query degradation, we propose a flow guidance feature integration module, which uses the motion discrepancy to enhance the features, together with a flow-guided feature propagation module that warps the features according to the flows. Third, we decouple the transformer along the temporal and spatial dimensions, where flows are used to select the tokens through a temporally deformable MHSA mechanism, and global tokens are combined with the inner-window local tokens through a dual perspective MHSA mechanism. FGT++ is experimentally evaluated to be outperforming the existing video inpainting networks qualitatively and quantitatively.
Ronnie de Souza Santos, Luiz Fernando Capretz, Cleyton Magalhaes
et al.
Testing is an indispensable part of software development. However, a career in software testing is reported to be unpopular among students in computer science and related areas. This can potentially create a shortage of testers in the software industry in the future. The question is, whether the perception that undergraduate students have about software testing is accurate and whether it differs from the experience reported by those who work in testing activities in the software development industry. This investigation demonstrates that a career in software testing is more exciting and rewarding, as reported by professionals working in the field, than students may believe. Therefore, in order to guarantee a workforce focused on software quality, the academy and the software industry need to work together to better inform students about software testing and its essential role in software development.
Context: Social aspects are of high importance for being successful using agile methods in software development. People are influenced by their cultural imprint, as the underlying cultural values are guiding us in how we think and act. Thus, one may assume that in multicultural agile software development teams, cultural characteristics influence the result in terms of quality of the team work and consequently, the product to be delivered. Objective: We aim to identify barriers and potentials that may arise in multicultural agile software development teams to provide valuable strategies for both researchers and practitioners faced with barriers or unrealized potentials of cultural diversity. Method: The study is designed as a single-case study with two units of analysis using a mixed-method design consisting quantitative and qualitative methods. Results: First, our results suggest that the cultural characteristics at the team level need to be analyzed individually in intercultural teams, Second, we identified key potentials regarding cultural characteristics providing key potentials such as a individual team subculture that fits agile values like open communication. Third, we derived strategies supporting the potentials of cultural diversity in agile software development teams. Conclusion: Our findings show, that a deeper understanding of cultural influences in multicultural agile software development teams is needed. Based on the results, we already prepare future work to validate the results in other industries.
Ronnie de Souza Santos, Brody Stuart-Verner, Cleyton Magalhaes
Diversity is an essential aspect of software development because technology influences almost every aspect of modern society, and if the software industry lacks diversity, software products might unintentionally constrain groups of individuals instead of promoting an equalitarian experience to all. In this study, we investigate the perspectives of transgender software professionals about a career in software engineering as one of the aspects of diversity in the software industry. Our findings demonstrate that, on the one hand, trans people choose careers in software engineering for two primary reasons: a) even though software development environments are not exempt from discrimination, the software industry is safer than other industries for transgenders; b) trans people occasionally have to deal with gender dysphoria, anxiety, and fear of judgment, and the work flexibility offered by software companies allow them to cope with these issues more efficiently.
The anecdotal connection between an interest in science fiction and career aspirations in astrophysics is well established. However strong statistical evidence for such a connection, and a quantitative assessment of its prevalence, has been missing. Here I report the results of two surveys examining the connection between science fiction enthusiasm and astronomical careers - first a case study of the University of Warwick Astronomy and Astrophysics group, carried out in February 2021, and second a larger survey of attendees at the UK National Astronomy Meeting in July 2022. In both surveys, a significant majority of respondents expressed an interest in science fiction. In the larger survey, 93% of UK astronomers (223 of 239 respondents) expressed an interest in science fiction, while 69% (164) stated that it had influenced their life or career choices. This study provides strong statistical evidence for the role of science fiction in influencing the adoption of astronomical careers.
The purpose of this study was to determine and examine how student career perceptions are handled by students related to the environmental impact, talents, and COVID-19 circumstances on career services at State Vocational School 3 Klaten. This study uses a qualitative approach. Respondents were students and teachers of Guidance and Counselling at state vocational high school 3 Klaten. The findings indicate that students' career conceptions are connected to the environmental influence, skills, and the COVID-19 settings surrounding career services and that these beliefs favourably affect students' self-development. The findings indicate that the results of interviews with students regarding factors influencing their perceptions of career counselling and career development, including talents, promising career planning with parental support and the impact of the environment, career exploration, career development, and support from learning facilities during the COVID-19.
There have been repeated calls made for theory-building studies in ICT4D research to solidify the existence of this research field. However, theory-building studies are not yet common, even though ICT4D as a research domain is a promising venue to develop native and indigenous theories. To this end, this paper outlines a theory-building study in ICT4D, based on the author's experience in developing a mid-range theory called 'Cultivating-Sustainability' of E-government projects, a native mid-range theory of ICT4D. The paper synthesizes the GTM literature and provides a step-by-step illustration of GTM use in practice for research students and early career ICT4D academics. It introduces the key strategies and principles of GTM, such as the theoretical sampling strategy, the constant comparison strategy, the concept-emergent principle, and the use of literature throughout the study process. Then discusses the steps involved in the data collection and analysis process to develop a theory using case studies as sources of empirical data; it concludes with a discussion on using the strategies and principles in the three case studies. It is expected that this paper contributes to the diversification of research methodology, particularly to our collective quest for developing native and indigenous theories in the ICT4D research domain.