Luca Vogelgesang, Ahmed Mehdi Soltani, Mohammadhossein Khojasteh
et al.
Assistive robots have growing potential to support physical wellbeing in home and healthcare settings, for example, by guiding users through stretching or rehabilitation routines. However, existing systems remain largely scripted, which limits their ability to adapt to user state, environmental context, and interaction dynamics. In this work, we present StretchBot, a hybrid neuro-symbolic robotic coach for adaptive assistive guidance. The system combines multimodal perception with knowledge-graph-grounded large language model reasoning to support context-aware adjustments during short stretching sessions while maintaining a structured routine. To complement the system description, we report an exploratory pilot comparison between scripted and adaptive guidance with three participants. The pilot findings suggest that the adaptive condition improved perceived adaptability and contextual relevance, while scripted guidance remained competitive in smoothness and predictability. These results provide preliminary evidence that structured actionable knowledge can help ground language-model-based adaptation in embodied assistive interaction, while also highlighting the need for larger, longitudinal studies to evaluate robustness, generalizability, and long-term user experience.
Shailendra Vishwakarma, Priyanka Gupta, Ashok Soni
India's demographic landscape presents a striking contradiction that policymakers can no longer ignore. While university convocation ceremonies nationwide celebrate record numbers of graduates each year, corporate hiring managers increasingly voice frustration - nearly half of these degree holders lack the basic competencies required for entry-level positions. This disturbing trend has brought employment-oriented education (EOE) into sharp focus as educators grapple with systemic failures. Unlike traditional academic models that prioritise theoretical knowledge, EOE emphasises hands-on skill development through three critical pillars: industry-aligned training modules, mandatory internship requirements, and competency-based assessment frameworks. Yet implementing meaningful reforms faces multilayered challenges. In Maharashtra's industrial belt, for instance, engineering colleges continue teaching outdated manufacturing processes while local factories adopt Industry 4.0 technologies. Vocational training initiatives struggle with perception issues, often viewed as second-class alternatives to conventional degrees. Perhaps most critically, the absence of standardised career counselling leaves students navigating complex labour markets without proper guidance - a problem acutely visible in India's aspirational districts.
Torsten Schlesinger, Werner Pitsch, Michael Barth
et al.
ABSTRACT Given the comparatively high salaries of professional soccer players during their active athletic careers, investments in education during their career seem to be less prevalent than among other athletes. This exploratory study analyses to what extent professional soccer players participate in vocational training, and what factors influence their cost–benefit assessment of participating in such activities during their athletic career. We conducted semi-structured interviews with 25 German professional soccer players (9 still active, 16 retired). Of these, 18 had competed in the top two leagues, while 7 had played in the 3rd and 4th leagues. The data were interpreted using qualitative content analysis. The results reveal a range of investments by players in education during their careers: from pursuing high-quality education to not investing in further education at all; from low to high opportunity costs of vocational training; from high willingness to take risks to low willingness to take risks. Our findings shed light on the decisions not only specifically among professional soccer players when choosing an education path, but also more broadly among individuals that face high occupational career risks and earn high salaries, to develop approaches for sustainable education planning (e.g. career goal development or guidance on educational pathways).
Objective: To deepen the understanding and appreciation of cultural diversity, promoting the development of competencies that foster interculturality in pluricultural educational environments.
Methodology: Based on an updated bibliographic review, the relationship between educational guidance and interculturality is analyzed, highlighting the importance of promoting mutual understanding and social cohesion.
Results: The findings indicate that intercultural educational guidance is based on the premise that knowledge and appreciation of diverse cultures enrich individual and collective life, which overcomes stereotypes and prejudices. Furthermore, this perspective recognizes cultural diversity as a source of wealth that entails the responsibility to build more inclusive and respectful societies. To this end, emphasis is placed on implementing educational strategies such as the pedagogy of questioning and critical reflection, which are key to consolidating an intercultural educational guidance approach. This approach not only promotes equity and inclusion in educational settings, but also prepares individuals to face the challenges and seize the opportunities of a globalized world.
Conclusions: In this sense, the role of the guidance professional is fundamental in promoting equity and social justice within educational settings. Their work involves guiding learning from an intercultural perspective and promoting awareness-raising and training processes that empower students, teachers, families, and communities to understand and respect diversity, thereby building more just and supportive communities enriched by values that enhance social and cultural coexistence.
Background: Despite progress in global campaigns supporting lesbian, gay, bisexual, transgender, queer or questioning, intersex, asexual, and other (+) (LGBTQIA+) rights, many LGBTQIA+ employees continue to face discrimination, harassment and violence, especially in countries where same-sex relationships remain criminalised. In South Africa, although legal protections exist, societal and workplace challenges persist, particularly in industries such as telecommunications.
Objectives: This study explored the lived workplace experiences of LGBTQIA+ employees in South African telecommunications companies, examining how organisational culture shaped inclusion and whether diversity and inclusion initiatives effectively supported them.
Methods: A mixed-methods approach, grounded in queer theory and institutional theory, was used. Phase one involved reflective diaries with participants, followed by in-depth interviews. Phase two employed thematic analysis to identify patterns in participant experiences.
Results: Findings revealed ongoing challenges, including discrimination, harassment and limited organisational support. Some participants resigned as a coping mechanism. Trust and psychological safety were significant. The industry’s reluctance to engage in the research also highlighted broader issues of openness and inclusion.
Conclusion: The study provides empirical insights into the experiences of LGBTQIA+ employees in South Africa, highlighting systemic barriers to inclusion and organisational challenges in supporting diversity.
Contribution: This research offers a conceptual framework informed by queer and institutional theory for understanding workplace inclusion. It contributes to Management, Business Ethics, and Sociology literature and provides recommendations for creating safer, more inclusive organisational environments, with a call for further research across other sectors.
Vocational guidance. Career development, Social Sciences
The adoption of generative AI and large language models (LLMs) in education is still emerging. In this study, we explore the development and evaluation of AI teaching assistants that provide curriculum-based guidance using a retrieval-augmented generation (RAG) pipeline applied to selected open-source small language models (SLMs). We benchmarked eight SLMs, including LLaMA 3.1, IBM Granite 3.3, and Gemma 3 (7-17B parameters), against GPT-4o. Our findings show that with proper prompting and targeted retrieval, SLMs can match LLMs in delivering accurate, pedagogically aligned responses. Importantly, SLMs offer significant sustainability benefits due to their lower computational and energy requirements, enabling real-time use on consumer-grade hardware without depending on cloud infrastructure. This makes them not only cost-effective and privacy-preserving but also environmentally responsible, positioning them as viable AI teaching assistants for educational institutions aiming to scale personalized learning in a sustainable and energy-efficient manner.
One approach to using prior experience in robot motion planning is to store solutions to previously seen problems in a database of paths. Methods that use such databases are characterized by how they query for a path and how they use queries given a new problem. In this work we present a new method, Path Database Guidance (PDG), which innovates on existing work in two ways. First, we use the database to compute a heuristic for determining which nodes of a search tree to expand, in contrast to prior work which generally pastes the (possibly transformed) queried path or uses it to bias a sampling distribution. We demonstrate that this makes our method more easily composable with other search methods by dynamically interleaving exploration according to a baseline algorithm with exploitation of the database guidance. Second, in contrast to other methods that treat the database as a single fixed prior, our database (and thus our queried heuristic) updates as we search the implicitly defined robot configuration space. We experimentally demonstrate the effectiveness of PDG in a variety of explicitly defined environment distributions in simulation.
Reinforcement Learning from Verifiable Rewards (RLVR) has been widely adopted as the de facto method for enhancing the reasoning capabilities of large language models and has demonstrated notable success in verifiable domains like math and competitive programming tasks. However, the efficacy of RLVR diminishes significantly when applied to agentic environments. These settings, characterized by multi-step, complex problem solving, lead to high failure rates even for frontier LLMs, as the reward landscape is too sparse for effective model training via conventional RLVR. In this work, we introduce Agent-RLVR, a framework that makes RLVR effective in challenging agentic settings, with an initial focus on software engineering tasks. Inspired by human pedagogy, Agent-RLVR introduces agent guidance, a mechanism that actively steers the agent towards successful trajectories by leveraging diverse informational cues. These cues, ranging from high-level strategic plans to dynamic feedback on the agent's errors and environmental interactions, emulate a teacher's guidance, enabling the agent to navigate difficult solution spaces and promotes active self-improvement via additional environment exploration. In the Agent-RLVR training loop, agents first attempt to solve tasks to produce initial trajectories, which are then validated by unit tests and supplemented with agent guidance. Agents then reattempt with guidance, and the agent policy is updated with RLVR based on the rewards of these guided trajectories. Agent-RLVR elevates the pass@1 performance of Qwen-2.5-72B-Instruct from 9.4% to 22.4% on SWE-Bench Verified. We find that our guidance-augmented RLVR data is additionally useful for test-time reward model training, shown by further boosting pass@1 to 27.8%. Agent-RLVR lays the groundwork for training agents with RLVR in complex, real-world environments where conventional RL methods struggle.
Given an unconditional generative model and a predictor for a target property (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training. As a highly efficient technique for steering generative models toward flexible outcomes, training-free guidance has gained increasing attention in diffusion models. However, existing methods only handle data in continuous spaces, while many scientific applications involve both continuous and discrete data (referred to as multimodality). Another emerging trend is the growing use of the simple and general flow matching framework in building generative foundation models, where guided generation remains under-explored. To address this, we introduce TFG-Flow, a novel training-free guidance method for multimodal generative flow. TFG-Flow addresses the curse-of-dimensionality while maintaining the property of unbiased sampling in guiding discrete variables. We validate TFG-Flow on four molecular design tasks and show that TFG-Flow has great potential in drug design by generating molecules with desired properties.
With the vigorous development of higher vocational education in China and the deepening of the concept of continuous learning, the education of upgrading from junior college to undergraduate has become an important channel for cultivating high-level technical and skilled talents. As a special student group, engineering students upgrading from junior college to undergraduate not only have practical experience accumulated during their junior college stage but also have a strong expectation to enhance their theoretical level and professional competitiveness through undergraduate studies. However, this group faces unique challenges in terms of ideological cognition, academic adaptation, and career planning. This paper aims to take three consecutive cohorts of engineering students upgrading from junior college to undergraduate at Southwest Forestry University as the research sample, deeply analyze the group characteristics of these students, dissect the prominent problems existing in their ideological education and employment attitudes, and systematically propose effective paths to optimize ideological education and employment guidance from the dimensions of educational subjects, educational content, and educational models, with the expectation of promoting the comprehensive, healthy, and high-quality growth and development of this group.
Graduate employability has become a significant challenge in today’s global job market, especially for students in vocational programs such as Electrical Engineering Education. The persistent mismatch between graduates' competencies and industry demands highlights the need to evaluate students’ readiness for the workforce. This study aims to analyze the work readiness of final-year students in the Electrical Engineering Education Program at Universitas Negeri Padang using a descriptive quantitative approach. This method was employed to describe the current state of work readiness based on questionnaire data that had been tested for validity and reliability. The questionnaire was distributed to final-year students to gather their perceptions of their readiness to enter the workforce. The findings show that the students’ level of work readiness falls into the “moderate” category. Most students have clear career plans and sufficient internship experience, although the relevance of these experiences to industry needs remains limited. Additionally, aspects such as learning motivation, mastery of soft and hard skills, and self-competence still need further development. None of the respondents felt “very ready,” indicating that their overall readiness is not yet optimal. Therefore, improvement is needed through more relevant internships, structured soft skills training, intensive career guidance, and stronger collaboration with industry. These efforts are expected to enhance students’ preparedness to face the dynamic and competitive labor market
Technology is changing the way organizations and their employees need to accomplish their work. Empirical evidence on this topic is scarce. The aim of this study is to provide an overview of the effects of technological developments on work characteristics and to derive the implications for work demands and continuous vocational education and training (CVET). The following research questions are answered: What are the effects of new technologies on work characteristics? What are the implications thereof for continuous vocational education and training? Technologies, defined as digital, electrical or mechanical tools that affect the accomplishment of work tasks, are considered in various disciplines, such as sociology or psychology. A theoretical framework based on theories from these disciplines (e.g., upskilling, task-based approach) was developed and statements on the relationships between technology and work characteristics, such as complexity, autonomy, or meaningfulness, were derived. A systematic literature review was conducted by searching databases from the fields of psychology, sociology, economics and educational science. Twenty-one studies met the inclusion criteria. Empirical evidence was extracted and its implications for work demands and CVET were derived by using a model that illustrates the components of learning environments. Evidence indicates an increase in complexity and mental work, especially while working with automated systems and robots. Manual work is reported to decrease on many occasions. Workload and workflow interruptions increase simultaneously with autonomy, especially with regard to digital communication devices. Role expectations and opportunities for development depend on how the profession and the technology relate to each other, especially when working with automated systems. The implications for the work demands necessary to deal with changes in work characteristics include knowledge about technology, openness toward change and technology, skills for self- and time management and for further professional and career development. Implications for the design of formal learning environments (i.e., the content, method, assessment, and guidance) include that the work demands mentioned must be part of the content of the trainings, the teachers/trainers must be equipped to promote those work demands, and that instruction models used for the learning environments must be flexible in their application.
Arini Widyowati, Michelle Hood, Amanda Duffy
et al.
Abstract We tested a model in which discrepancy with parents’ career goals moderated the indirect path from young adults’ self-perceived career goal discrepancy to career indecision via negative emotions (regret and distress) and self-regulatory capacity. We surveyed 236 young adults ( M Age = 21.77 years; 71.2% female), finding that parent discrepancy strengthened the positive relationships between self-discrepancy and career regret and self-regulatory depletion, but not distress. However, career distress fully explained the self-discrepancy–career indecision relationship, not moderated by parent-discrepancy. Overall, our model explained 70% of the variance in indecision. This has implications for counsellors to assist young adults in managing discrepancy-related distress and indecision.
This article is based on an empirical study conducted between February and May 2023, employing methods such as online questionnaire surveys, content analysis of school websites, and secondary analysis of regulatory documents in the field of education in Russia and France. These documents include materials from organizations, centers, and other structures addressing issues related to vocational guidance, employment, training, and the development of student mobility in secondary schools and colleges. The work highlights the scientific problem of considering students’ personal interests, inclinations, and abilities as a critical factor in selecting a university training program. The study revealed differences between Russian and French applicants in terms of the importance attributed to institutional, social, and personal factors when choosing a university course. Both Russian and French applicants place equal emphasis on institutional factors when deciding on a course of study. However, French applicants prioritize personal motives, such as self-realization, self-development, interesting future work, and the potential to build a successful career. Conversely, Russian applicants are more influenced by social factors, including entrance exam results, the prestige of the university, the potential to secure a well-paying job after graduation, and future high earnings. French applicants tend to make more balanced and informed decisions, leveraging their existing abilities to secure fulfilling future employment, achieve self-actualization, and develop successful careers.
زمینه و هدف: امروزه با توجه به چالشهایی که سازمانها با آن مواجه هستند، ایجاد سازمان شاد یکی از نیازهای استراتژیک برای موفقیت سازمان میباشد. هدف از این مطالعه ارائه مدل ساختاری - تفسیری شادمانی سازمانی مبتنی بر ارزشهای کارتیمی (موردمطالعه شرکت گاز استان اردبیل) بود. روششناسی: این تحقیق از نظرهدف کاربردی از لحاظماهیت، آمیخته با طرحترکیبی اکتشافی – متوالی است. بخشکیفی با روش تحلیلتماتیک مبتنی بر ترید-استرلینگ (2001) و بخشکمی نیز با بهرهگیری از تکنیک مدلسازی ساختاری – تفسیری (ISM) انجام شد. جامعههدف پژوهش، خبرگانآگاه به موضوعتحقیق بودند که در بخشکیفی نمونهای با رویکرد هدفمند تا مرحله اشباع (18نفر) انتخاب شد و در بخشکمی نمونهای به تعداد 5 نفر پیشنهاد شد. بهمنظور جمعآوری و تحلیلدادهها در بخشکیفی از مصاحبههای نیمهساختاریافته و در بخشکمی از پرسشنامههای محققساخته استفاده شد و برای این منظور از نرمافزارهای Maxqda10 و Matalab2016a استفاده گردید. یافتهها: بعد از تحلیل مصاحبهها، به استخراج 227 مضمونپایه، 35 مضمون سازماندهنده و درنهایت 13 مضمونفراگیر تاثیرگذار بر شادمانی سازمانی مبتنی بر ارزشهای کارتیمی منجر شد و این عواملشناسایی شده در هفتسطح مدل ساختاری – تفسیری قرار گرفتند.نتیجهگیری: این نتایج میتواند مبنایی برای تصمیمهای مدیران و برنامهریزان در شرکت گاز استان اردبیل در راستای ارتقای شادمانی سازمانی مبتنی بر ارزشهای کارتیمی باشد.
Many organizations and career guidance professionals are curious about what personality traits can tell them about a person’s potential, qualities and attitude to work.
This study assessed the personality traits, work values and vocational interests of university students. The relationships between these three constructs were investigates, and more specifically, the extent to which personality traits and work value can predict vocational interest. Vocational interest determines one’s career development and work choice.
The study included 304 participants with a mean age of 25.4 years (SD=8.11). In our analysis, we used the HEXACO personality questionnaire, the Work Value Questionnaire measuring 15 subcategories and the RIASEC Vocational Interest Questionnaire. The aim of this study is to get a clearer picture of the relationship between personality traits, work values and vocational interests. The results show that personality traits are also predictive, but together with work value, they are stronger predictors of vocational interest. The results are presented in the light of career development.
Given an unconditional diffusion model and a predictor for a target property of interest (e.g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training. Existing methods, though effective in various individual applications, often lack theoretical grounding and rigorous testing on extensive benchmarks. As a result, they could even fail on simple tasks, and applying them to a new problem becomes unavoidably difficult. This paper introduces a novel algorithmic framework encompassing existing methods as special cases, unifying the study of training-free guidance into the analysis of an algorithm-agnostic design space. Via theoretical and empirical investigation, we propose an efficient and effective hyper-parameter searching strategy that can be readily applied to any downstream task. We systematically benchmark across 7 diffusion models on 16 tasks with 40 targets, and improve performance by 8.5% on average. Our framework and benchmark offer a solid foundation for conditional generation in a training-free manner.
Federico Rossi, Andrew Branch, Michael P. Schodlok
et al.
We propose a novel technique for guidance of buoyancy-controlled vehicles in uncertain under-ice ocean flows. In-situ melt rate measurements collected at the grounding zone of Antarctic ice shelves, where the ice shelf meets the underlying bedrock, are essential to constrain models of future sea level rise. Buoyancy-controlled vehicles, which control their vertical position in the water column through internal actuation but have no means of horizontal propulsion, offer an affordable and reliable platform for such in-situ data collection. However, reaching the grounding zone requires vehicles to traverse tens of kilometers under the ice shelf, with approximate position knowledge and no means of communication, in highly variable and uncertain ocean currents. To address this challenge, we propose a partially observable MDP approach that exploits model-based knowledge of the under-ice currents and, critically, of their uncertainty, to synthesize effective guidance policies. The approach uses approximate dynamic programming to model uncertainty in the currents, and QMDP to address localization uncertainty. Numerical experiments show that the policy can deliver up to 88.8% of underwater vehicles to the grounding zone -- a 33% improvement compared to state-of-the-art guidance techniques, and a 262% improvement over uncontrolled drifters. Collectively, these results show that model-based under-ice guidance is a highly promising technique for exploration of under-ice cavities, and has the potential to enable cost-effective and scalable access to these challenging and rarely observed environments.
The nano-meter beam size in future linear colliders requires very high resolution beam position monitor since higher resolution allows more accurate position measurement in the interaction point. We developed and tested a low-Q C-band beam position monitor with position resolution of nanometer. The C-band BPM was tested for the fast beam feedback system at the interaction point of ATF2 in KEK, in which C-band beam position monitor is called to IPBPM (Interaction Point Beam Position Monitor). The average position resolution of the developed IPBPMs was measured to be 10.1 nm at a nominal beam charge of $87\%$ of ATF2. From the measured beam position resolution, we can expect beam position resolution of around 8.8 nm and 4.4 nm with nominal ATF2 and ILC beam charge conditions, respectively, in which the position resolution is below the vertical beam size in ILC. In this paper, we describe the development of the IPBPM and the beam test results at the nanometer level in beam position resolution
In recent years, advancements in AIGC (Artificial Intelligence Generated Content) technology have significantly enhanced the capabilities of large text-to-image models. Despite these improvements, controllable image generation remains a challenge. Current methods, such as training, forward guidance, and backward guidance, have notable limitations. The first two approaches either demand substantial computational resources or produce subpar results. The third approach depends on phenomena specific to certain model architectures, complicating its application to large-scale image generation.To address these issues, we propose a novel controllable generation framework that offers a generalized interpretation of backward guidance without relying on specific assumptions. Leveraging this framework, we introduce LSReGen, a large-scale layout-to-image method designed to generate high-quality, layout-compliant images. Experimental results show that LSReGen outperforms existing methods in the large-scale layout-to-image task, underscoring the effectiveness of our proposed framework. Our code and models will be open-sourced.