Background: Academic career development (ACD) in the context of work intensification in speech-language pathology and audiology (SLP-A) academia has received limited attention in recent years. Higher education institutions, such as the University of Interest (UoI), provide support to developing academics. Yet, little is known about how female academics experience accessing ACD while simultaneously trying to cope with the demands of academic work intensification. The impact of ACD on work–life balance (WLB) is also unknown for SLP-A academics.
Objectives: This study explores the experiences of ACD for female SLP-A academics at a South African university and the related impact on WLB.
Methods: Eight purposively sampled SLP-A academics from the UoI participated in qualitative semi-structured interviews within a hermeneutic phenomenological design. The data generated were analysed thematically.
Results: The UoI makes ACD support available to staff, with female academics experiencing more opportunities than their male counterparts. However, access to available ACD support was restricted by time constraints and a counterculture within the SLP-A disciplines. Work intensification further restricted ACD and led to poor WLB, with social life being compromised.
Conclusion: There is a need to explore means to optimise the flow and accessibility of ACD opportunities from university leadership to the discipline level for female academics, with minimal interference from the disciplines. A stronger policy position to promote improved WLB is necessary.
Contribution: This study provides a basis for discussing policy shifts concerning work intensification while supporting ACD and minimising the negative impact on WLB, particularly for developing female academics.
Vocational guidance. Career development, Social Sciences
The widespread use of smartphones has made photography ubiquitous, yet a clear gap remains between ordinary users and professional photographers, who can identify aesthetic issues and provide actionable shooting guidance during capture. We define this capability as aesthetic guidance (AG) -- an essential but largely underexplored domain in computational aesthetics. Existing multimodal large language models (MLLMs) primarily offer overly positive feedback, failing to identify issues or provide actionable guidance. Without AG capability, they cannot effectively identify distracting regions or optimize compositional balance, thus also struggling in aesthetic cropping, which aims to refine photo composition through reframing after capture. To address this, we introduce AesGuide, the first large-scale AG dataset and benchmark with 10,748 photos annotated with aesthetic scores, analyses, and guidance. Building upon it, we propose Venus, a two-stage framework that first empowers MLLMs with AG capability through progressively complex aesthetic questions and then activates their aesthetic cropping power via CoT-based rationales. Extensive experiments show that Venus substantially improves AG capability and achieves state-of-the-art (SOTA) performance in aesthetic cropping, enabling interpretable and interactive aesthetic refinement across both stages of photo creation. Code is available at https://github.com/PKU-ICST-MIPL/Venus_CVPR2026.
Recent progress in generative modeling has enabled high-quality visual synthesis with diffusion-based frameworks, supporting controllable sampling and large-scale training. Inference-time guidance methods such as classifier-free and representative guidance enhance semantic alignment by modifying sampling dynamics; however, they do not fully exploit unsupervised feature representations. Although such visual representations contain rich semantic structure, their integration during generation is constrained by the absence of ground-truth reference images at inference. This work reveals semantic drift in the early denoising stages of diffusion transformers, where stochasticity results in inconsistent alignment even under identical conditioning. To mitigate this issue, we introduce a guidance scheme using a representation alignment projector that injects representations predicted by a projector into intermediate sampling steps, providing an effective semantic anchor without modifying the model architecture. Experiments on SiTs and REPAs show notable improvements in class-conditional ImageNet synthesis, achieving substantially lower FID scores; for example, REPA-XL/2 improves from 5.9 to 3.3, and the proposed method outperforms representative guidance when applied to SiT models. The approach further yields complementary gains when combined with classifier-free guidance, demonstrating enhanced semantic coherence and visual fidelity. These results establish representation-informed diffusion sampling as a practical strategy for reinforcing semantic preservation and image consistency.
Aligning large-scale text-to-image diffusion models with nuanced human preferences remains challenging. While direct preference optimization (DPO) is simple and effective, large-scale finetuning often shows a generalization gap. We take inspiration from test-time guidance and cast preference alignment as classifier-free guidance (CFG): a finetuned preference model acts as an external control signal during sampling. Building on this view, we propose a simple method that improves alignment without retraining the base model. To further enhance generalization, we decouple preference learning into two modules trained on positive and negative data, respectively, and form a \emph{contrastive guidance} vector at inference by subtracting their predictions (positive minus negative), scaled by a user-chosen strength and added to the base prediction at each step. This yields a sharper and controllable alignment signal. We evaluate on Stable Diffusion 1.5 and Stable Diffusion XL with Pick-a-Pic v2 and HPDv3, showing consistent quantitative and qualitative gains.
Classifier-free guidance (CFG) is a widely used technique for controllable generation in diffusion and flow-based models. Despite its empirical success, CFG relies on a heuristic linear extrapolation that is often sensitive to the guidance scale. In this work, we provide a principled interpretation of CFG through the lens of optimization. We demonstrate that the velocity field in flow matching corresponds to the gradient of a sequence of smoothed distance functions, which guides latent variables toward the scaled target image set. This perspective reveals that the standard CFG formulation is an approximation of this gradient, where the prediction gap, the discrepancy between conditional and unconditional outputs, governs guidance sensitivity. Leveraging this insight, we reformulate the CFG sampling as a homotopy optimization with a manifold constraint. This formulation necessitates a manifold projection step, which we implement via an incremental gradient descent scheme during sampling. To improve computational efficiency and stability, we further enhance this iterative process with Anderson Acceleration without requiring additional model evaluations. Our proposed methods are training-free and consistently refine generation fidelity, prompt alignment, and robustness to the guidance scale. We validate their effectiveness across diverse benchmarks, demonstrating significant improvements on large-scale models such as DiT-XL-2-256, Flux, and Stable Diffusion 3.5.
Against the backdrop of the increasing importance of higher vocational education in cultivating technical and skilled talents, and the challenges faced by vocational college students in terms of insufficient employment competitiveness and lack of intrinsic motivation for career development, this study focuses on the symbiotic mechanism between self-efficacy and career vision among vocational college students. The research findings indicate that the self-efficacy of vocational college students is generally at a medium level, and the clarity of their career vision needs improvement. Moreover, there is a significant positive correlation between the two, and self-efficacy can positively predict career vision. In terms of the symbiotic mechanism, self-efficacy influences career vision at the cognitive, emotional, and behavioral levels, while career vision exerts a counter-effect on self-efficacy through goal orientation and other means. Beliefs play a crucial reinforcing and guiding role in this process. Based on these findings, this study proposes strategies to enhance the self-efficacy and career vision of vocational college students from multiple perspectives, including optimizing curriculum settings, strengthening faculty development, and fostering campus culture in schools; promoting enterprise participation and public opinion guidance in society; and enhancing self-awareness and practical training among students themselves.
T. Setiadi, Laksamana Rajendra Haidar Azani Fajri, Susanti Dwi Ilhami
Vocational high school education is an important path to foster skilled professionals with skills in accordance with their fields and is increasingly attracting attention in the world of work. Vocational education emphasizes the development of practical skills and vocational competencies of students. This equips them with the ability to adapt quickly to market demands and contribute to social and economic development, one of which is at SMK Harapan Mulya Kendal. Along with the advancement of globalization and technology, career planning has evolved beyond just career selection; career planning has become a comprehensive process that requires consideration of various factors such as personal interests, values, skills, and job market requirements. The purpose of community service is to provide training for the implementation of smart career applications for vocational high school students, to help them better overcome challenges in the workplace and realize their respective career goals before graduating from school. In the implementation of community service activities with the Implementation and Implementation Method, Training, Mentoring, Monitoring and Evaluation with an emphasis on solving problems, namely student human resources and counseling guidance in order to improve the quality of student careers, and understanding of the career potential of vocational high school students. The conclusion of the training results for guidance and counseling teachers can provide stimulus to each student, in this case the teacher as a supervisor, to increase students' self-confidence in building personality and exploring the potential of each student so that students realize what potential they have in themselves to be confident in their future careers. Keywords: Smart Career Application, Student Career, Student Personality, Vocational High School Career Planning
Regina M. Thetsane, Motselisi C. Mokhethi, Mpheteli J. Malunga
et al.
Background: As the job market evolves, understanding factors influencing students’ major selection helps universities and policymakers design programmes aligned with industry needs and support career development.
Objectives: This study examines motivational factors shaping major selection among Bachelor of Commerce students in the Department of Business Administration (DBA) at the National University of Lesotho. By analysing three majors, it explores decision-making dynamics and their influence on academic choices and professional aspirations.
Methods: A descriptive quantitative survey was conducted with first year DBA students. Data were analysed using SPSS for factor extraction and correlation analysis to examine relationships among motivational factors across the three majors.
Results: Six factors, namely, academic convenience, accessibility, engagement, popularity, recommendations and financial prospects influenced students’ choices. However, these were statistically insignificant. Long-term career goals and professional development emerged as stronger determinants, aligning with the Theory of Planned Behaviour, which highlights intrinsic motivation and belief in future success.
Conclusion: Decision making is driven by mesosystemic factors such as academic offerings, labour market demands and career advancement opportunities. Institutions must align programmes with industry expectations and strengthen career support systems.
Contribution: The study offers insights for aligning academic programmes with national development goals, improving graduate employability, and supporting students in making informed, aspiration-driven choices.
Vocational guidance. Career development, Social Sciences
هدف پژوهش حاضر، طراحی الگوی شکلگیری واماندگی استعدادها در سازمانهای دولتی؛ با روش مدلسازی ساختاری تفسیری (ISM) است. این پژوهش از نظر هدف، کاربردی و از حیث ماهیت و روش، توصیفی پیمایشی است. جامعه آماری پژوهش، مدیران ارشد و مدیران حوزه منابع انسانی سازمانهای دولتی هستند که تعداد 20 نفر از آنها با استفاده از روش نمونهگیری هدفمند انتخاب شدهاند. ابزارگردآوری اطلاعات در بخش کیفی مصاحبه و در بخش کمی پرسشنامه است که روایی و پایایی آنها با استفاده از روش محتوایی و روایی نظری و پایایی درونکدگذار و میانکدگذار تایید شد. دراین پژوهش دادههای کیفی با روش تحلیل محتوا و نرمافزار مکس- کیو- دی- ای 2020 و نتایج کمی با روش مدلسازی ساختاری تفسیری تحلیل شد. یافتههای این پژوهش مشتمل بر عوامل موثر بر شکلگیری پدیدهی واماندگی استعدادها و ارائه مدل آن است. نتایج پژوهش مشتمل بر طراحی الگوی شکلگیری واماندگی استعدادها است، مدل واماندگی استعدادها در این پژوهش در چهار سطح اصلی و براساس بسترهای شکلگیری، ابعاد واماندگی استعدادها، عوامل مداخلهجو و همبسته آن و پیامدهای واماندگی استعدادها تدوین شدهاست.درواقع نتایج حاصل از این پژوهش می تواند درک و شناخت مدیران را در خصوص پدیده وامانـدگی اسـتعدادها و عوامل مؤثر بـر شکل گیری آن را در سـازمان هـای دولتی بهبود بخشد و همچنین، مبنایی برای تصمیمات مدیران و برنامه ریزان سازمان های دولتی در راستای مقابله با با این پدیده می باشد
Pinelopi Papalampidi, Olivia Wiles, Ira Ktena
et al.
Classifier-free guidance (CFG) is a cornerstone of text-to-image diffusion models, yet its effectiveness is limited by the use of static guidance scales. This "one-size-fits-all" approach fails to adapt to the diverse requirements of different prompts; moreover, prior solutions like gradient-based correction or fixed heuristic schedules introduce additional complexities and fail to generalize. In this work, we challeng this static paradigm by introducing a framework for dynamic CFG scheduling. Our method leverages online feedback from a suite of general-purpose and specialized small-scale latent-space evaluations, such as CLIP for alignment, a discriminator for fidelity and a human preference reward model, to assess generation quality at each step of the reverse diffusion process. Based on this feedback, we perform a greedy search to select the optimal CFG scale for each timestep, creating a unique guidance schedule tailored to every prompt and sample. We demonstrate the effectiveness of our approach on both small-scale models and the state-of-the-art Imagen 3, showing significant improvements in text alignment, visual quality, text rendering and numerical reasoning. Notably, when compared against the default Imagen 3 baseline, our method achieves up to 53.8% human preference win-rate for overall preference, a figure that increases up to to 55.5% on prompts targeting specific capabilities like text rendering. Our work establishes that the optimal guidance schedule is inherently dynamic and prompt-dependent, and provides an efficient and generalizable framework to achieve it.
Jincheng Zhong, Xiangcheng Zhang, Jianmin Wang
et al.
Recent advancements in diffusion models have revolutionized generative modeling. However, the impressive and vivid outputs they produce often come at the cost of significant model scaling and increased computational demands. Consequently, building personalized diffusion models based on off-the-shelf models has emerged as an appealing alternative. In this paper, we introduce a novel perspective on conditional generation for transferring a pre-trained model. From this viewpoint, we propose *Domain Guidance*, a straightforward transfer approach that leverages pre-trained knowledge to guide the sampling process toward the target domain. Domain Guidance shares a formulation similar to advanced classifier-free guidance, facilitating better domain alignment and higher-quality generations. We provide both empirical and theoretical analyses of the mechanisms behind Domain Guidance. Our experimental results demonstrate its substantial effectiveness across various transfer benchmarks, achieving over a 19.6% improvement in FID and a 23.4% improvement in FD$_\text{DINOv2}$ compared to standard fine-tuning. Notably, existing fine-tuned models can seamlessly integrate Domain Guidance to leverage these benefits, without additional training.
Classifier-Free Guidance (CFG) is an essential component of text-to-image diffusion models, and understanding and advancing its operational mechanisms remains a central focus of research. Existing approaches stem from divergent theoretical interpretations, thereby limiting the design space and obscuring key design choices. To address this, we propose a unified perspective that reframes conditional guidance as fixed point iterations, seeking to identify a golden path where latents produce consistent outputs under both conditional and unconditional generation. We demonstrate that CFG and its variants constitute a special case of single-step short-interval iteration, which is theoretically proven to exhibit inefficiency. To this end, we introduce Foresight Guidance (FSG), which prioritizes solving longer-interval subproblems in early diffusion stages with increased iterations. Extensive experiments across diverse datasets and model architectures validate the superiority of FSG over state-of-the-art methods in both image quality and computational efficiency. Our work offers novel perspectives for conditional guidance and unlocks the potential of adaptive design.
The recent development and use of generative AI (GenAI) has signaled a significant shift in research activities such as brainstorming, proposal writing, dissemination, and even reviewing. This has raised questions about how to balance the seemingly productive uses of GenAI with ethical concerns such as authorship and copyright issues, use of biased training data, lack of transparency, and impact on user privacy. To address these concerns, many Higher Education Institutions (HEIs) have released institutional guidance for researchers. To better understand the guidance that is being provided we report findings from a thematic analysis of guidelines from thirty HEIs in the United States that are classified as R1 or 'very high research activity.' We found that guidance provided to researchers: (1) asks them to refer to external sources of information such as funding agencies and publishers to keep updated and use institutional resources for training and education; (2) asks them to understand and learn about specific GenAI attributes that shape research such as predictive modeling, knowledge cutoff date, data provenance, and model limitations, and educate themselves about ethical concerns such as authorship, attribution, privacy, and intellectual property issues; and (3) includes instructions on how to acknowledge sources and disclose the use of GenAI, how to communicate effectively about their GenAI use, and alerts researchers to long term implications such as over reliance on GenAI, legal consequences, and risks to their institutions from GenAI use. Overall, guidance places the onus of compliance on individual researchers making them accountable for any lapses, thereby increasing their responsibility.
Classifier-Free Guidance (CFG) is a widely used technique for conditional generation and improving sample quality in continuous diffusion models, and its extensions to discrete diffusion has recently started to be investigated. In order to improve the algorithms in a principled way, this paper starts by analyzing the exact effect of CFG in the context of a low-dimensional masked diffusion model, with a special emphasis on the guidance schedule. Our analysis shows that high guidance early in sampling (when inputs are heavily masked) harms generation quality, while late-stage guidance improves it. These findings provide a theoretical explanation for empirical observations in recent studies on guidance schedules. The analysis also reveals an imperfection of the current CFG implementations. These implementations can unintentionally cause imbalanced transitions, such as unmasking too rapidly during the early stages of generation, which degrades the quality of the resulting samples. To address this, we draw insight from the analysis and propose a novel classifier-free guidance mechanism. Intuitively, our method smooths the transport between the data distribution and the initial (masked) distribution, resulting in improved sample quality. Remarkably, our method is achievable via a simple one-line code change. Experiments on conditional image and text generation empirically confirm the efficacy of our method.
Muhammad Rafli, Muzayyinah Al Usrah, Nusril Muchtadi
Abstract: This study aims to analyze how students at SMK Negeri 1 Bantaeng explore their major or career choices and how these choices relate to their future career well-being. The research employs a descriptive quantitative design. The sample consists of 198 active students from vocational high schools (SMK) in Bantaeng District, South Sulawesi, Indonesia. The scale used to assess career well-being is based on the development of Coetzee's (2021) Career Well-Being Inventory. The Career Well-Being Scale uses a Likert-type scale with response criteria ranging from 1 (strongly disagree) to 5 (strongly agree). Based on the research findings, students at SMK Negeri 1 Bantaeng demonstrate high overall career well-being, with aspects of affective career planning, career meaningfulness, and social support all falling within the high category. This reflects that students feel comfortable and supported in selecting their majors and planning their careers. The recommendations of this study include enhancing career planning support through strengthened guidance and counseling programs, as well as fostering partnerships with the industry to expand students' career networks. Keywords: career well-being, students; career path, vocational high school.DOI: http://dx.doi.org/10.61436/bsscd/v3i2.pp65-71
Abstract Vocational maturity and career aspirations are crucial components of adolescent development, influencing educational choices, employability, and long-term socioeconomic outcomes. Adolescence is a formative stage during which individuals begin to understand their interests, abilities, and future occupational roles. The present study aims to examine vocational maturity and career aspirations among adolescents and to compare these variables across types of institutions, namely government and private schools. A descriptive-comparative research design was adopted. The sample consisted of 600 adolescents (300 from government schools and 300 from private schools) aged between 14 and 18 years. Standardized tools, including the Vocational Maturity Inventory and the Career Aspiration Scale, were used for data collection. Statistical techniques such as mean, standard deviation, t-test, and graphical representations were employed for data analysis. The findings revealed significant differences between government and private school students in terms of vocational maturity and career aspirations, with private school students showing higher levels on both dimensions. The study highlights the role of institutional environment, availability of career guidance, and socioeconomic background in shaping adolescents’ vocational development. The findings have important implications for educational planners, school administrators, and policymakers to strengthen vocational guidance and career counseling services, particularly in government schools. Keywords: Vocational Maturity, Career Aspirations, Adolescents, Government Schools, Private Schools
This study surveyed 528 post-00s students from three higher vocational colleges in Shenzhen using a revised “Career Values Questionnaire for Higher Vocational Students” and conducted statistical analysis with SPSS 26.0. The findings reveal that the career values of post-00s higher vocational students in Shenzhen exhibit characteristics of “enterprising pragmatism,” with “personal skill development” and “salary and benefits” jointly constituting the core driving forces. In terms of career evaluation criteria, “corporate innovation atmosphere” and “fair competition mechanisms” significantly surpass traditional concepts in importance. Career orientation demonstrates a clear regional retention tendency and industry foresight, with “staying in Shenzhen and the Greater Bay Area for development” and “engaging in strategic emerging industries” emerging as mainstream choices. The study indicates that the regional innovation ecosystem significantly shapes career values, necessitating the establishment of a collaborative guidance system involving higher vocational institutions, the government, and enterprises.
Aim/Purpose: This study aims to investigate the enabling technologies and applications of computer-assisted career guidance (CACG) tools in the career planning activities of students. Background: The choice of a career is an extremely significant lifetime decision for any individual. Students often struggle with their career choices mainly due to the lack of awareness in career planning and development. Therefore, students require the support of career counselors for proper career decision-making. Unfortunately, adequate career counseling resources are not readily available within educational institutes. CACG tools offer a workable solution for overcoming this challenge. Methodology: A systematic literature review was conducted based on a standard guideline for the period of 2011 through 2023. Initially, a comprehensive review protocol was defined and evaluated. In conducting the review, nine electronic databases: Scopus, Web of Science, IEEE Xplore, ACM Digital Library, Science Direct, SpringerLink, Wiley Online, Emerald Insight, and Sage Journals were queried. Then search results were narrowed down to 46 scholarly articles by applying predefined selection criteria. Contribution: This review study contributes to assessing the status of the existing body of knowledge on implementing and applying CACG tools for career path planning within the education domain. Significantly, this study identified a set of underlying technologies used in implementing modern CACG tools as well as a distinct set of parameters associated with users that can be used as input for offering personalized career decision support. Further, specific needs of applying CACG tools at distinct educational stages were assessed. Study outcomes support future research works by unraveling potential research directions based on identified research gaps. Findings: The key findings of this study revealed experimentation with a wide range of enabling technologies and techniques in the implementation of CACG tools for students’ career path planning. Within these tools, a distinct set of parameters associated with students has been considered as input for offering personalized career decision support. Further, it was found that the use of CACG tools in career guidance differs across distinct educational stages. Recommendations for Practitioners: CACG has been extensively used within the education domain for providing career guidance services to different student populations. With technological advancements, CACG has evolved as a viable alternative to in-person career counseling, rather than primarily serving as a supplementary tool used by career counselors during in-person counseling. Therefore, it is recommended that educational institutes utilize CACG tools in situations where adequate in-person career counseling services are not possible. Recommendation for Researchers: Continuous technological advancements make it advisable for researchers to continue further experimentation employing emerging cutting-edge technologies for improving the functionalities of CACG tools used in education. Particularly significant are improvements in personalization capabilities and integrating user profiling techniques to enhance the effectiveness of the services offered by CACG tools. Impact on Society: Technology-assisted career counseling can play a vital role in fulfilling the career guidance requirements of various student populations. This study has affirmed the potential of using CACG as a viable alternative to in-person career counseling within educational institutes. Future Research: In future work, the scope of this study can be extended to other educational guidance domains such as academic advising, pedagogical resource recommendation, academic program and course recommendation, and college and university recommendation. Moreover, future research may investigate the application of CACG tools in the career guidance activities of vocational education.
For higher vocational students, the college stage is an important period in their career development, and the college experience plays an important role in the relationship between their proactive personality and career adaptability, which in turn has a significant impact on their future career development. From the perspective of social cognitive career theory and taking 476 vocational students as samples, this paper explores the mediating role of college experience between proactive personality and career adaptability of vocational college students. The college experience scale is revised for higher vocational students, and it is verified to have good reliability and validity. SPSS and Amos were used to conduct correlation analysis，and the PROCESS macro was used for mediating effect analysis. The results show that the college experience of vocational students plays a partial mediating role in the effect of proactive personality on career adaptability. This work innovatively uses social cognitive career theory to explore the role of college experience in the relationship between proactive personality and career adaptability among vocational students. The theoretical models are established and empirical verification is conducted, confirming that higher vocational students’ college experience can affect their career adaptability. These results provide empirical evidence for vocational colleges to improve the career guidance of college students, and intervention measures are proposed to enhance students’ career adaptability during school years, thus promoting their sustainable development.