The accelerating digitization and automation of work, known as the 4th industrial revolution, will have an enormous impact on individuals’ career experiences. Yet, the academic literature in vocational psychology and career research has been remarkably silent on this trend so far. This article summarizes some of the most important issues of the 4th industrial revolution as they pertain to career development. The author then critically reviews how current models and frameworks of career development are suitable for addressing these emerging issues. Opportunities for future career development research and practice are outlined.
Andrius Dzedzickis, Jurga Subačiūtė-Žemaitienė, E. Šutinys
et al.
This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.
Anna Lilja Secher,1 Mette Andersen Nexø,2 Erik Lykke Mortensen,3 Kirsten Nørgaard1,4 1Department of Clinical and Translational Research, Steno Diabetes Center Copenhagen, Copenhagen, Denmark; 2Department of Education, Steno Diabetes Center Copenhagen, Copenhagen, Denmark; 3Department of Public Health, University of Copenhagen, Copenhagen, Denmark; 4Department of Clinical Medicine, University of Copenhagen, Copenhagen, DenmarkCorrespondence: Anna Lilja Secher, Department of Clinical and Translational Research, Steno Diabetes Center Copenhagen, Borgmester Ib Juuls Vej 83, Herlev, Denmark, Tel +45 6171 8565, Email anna.lilja.secher@gmail.comAim: How well new technology is applied to the daily management of type 1 diabetes (T1D) may be highly influenced by personality traits. The number of studies examining how personality traits influence diabetes self-management technologies such as continuous glucose monitoring and carbohydrate counting with automated insulin bolus calculation based on carbohydrate ratios and insulin sensitivity is scarce. Derived from a randomized controlled trial, we aimed to examine the association between personality traits and glycemic and patient-reported outcomes in adults with T1D on multiple daily insulin injections initiating flash glucose monitoring and carbohydrate counting with automated bolus calculation.Methods: Personality trait scores from The Five-Factor Inventory-3 were analyzed in 170 individuals. We assessed baseline (n = 168) and changes (n = 34) in HbA1c, and patient-reported outcomes (validated questionnaires on diabetes distress, diabetes treatment satisfaction, diabetes psychosocial self-efficacy, diabetes quality of life) in bivariate and partial correlation analyses with personality traits (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness).Results: Adjusted for sex, age, and diabetes debut age, higher agreeableness correlated negatively with baseline HbA1c (partial correlation − 0.20, p < 0.05) but positively with HbA1c change over time (0.36, p < 0.05). Higher neuroticism score was associated with higher baseline distress (0.39, p < 0.001) and lower baseline psychosocial self-efficacy (− 0.43, p < 0.001), quality of life (− 0.32, p < 0.001) and treatment satisfaction (− 0.18, p < 0.05).Conclusion: This study confirms that personality traits are associated with glycemia and patient-reported outcomes in adults with T1D. Consequently, tailored diabetes management approaches are likely to enhance overall outcomes, especially when incorporating diabetes technology.Keywords: personality traits, diabetes, glycemic effect, patient-reported outcomes
Industrial symbiosis fosters circularity by enabling firms to repurpose residual resources, yet its emergence is constrained by socio-spatial frictions that shape costs, matching opportunities, and market efficiency. Existing models often overlook the interaction between spatial structure, market design, and adaptive firm behavior, limiting our understanding of where and how symbiosis arises. We develop an agent-based model where heterogeneous firms trade byproducts through a spatially embedded double-auction market, with prices and quantities emerging endogenously from local interactions. Leveraging reinforcement learning, firms adapt their bidding strategies to maximize profit while accounting for transport costs, disposal penalties, and resource scarcity. Simulation experiments reveal the economic and spatial conditions under which decentralized exchanges converge toward stable and efficient outcomes. Counterfactual regret analysis shows that sellers' strategies approach a near Nash equilibrium, while sensitivity analysis highlights how spatial structures and market parameters jointly govern circularity. Our model provides a basis for exploring policy interventions that seek to align firm incentives with sustainability goals, and more broadly demonstrates how decentralized coordination can emerge from adaptive agents in spatially constrained markets.
Large Language Models (LLMs) have demonstrated remarkable success across a wide range of industries, primarily due to their impressive generative abilities. Yet, their potential in applications requiring cognitive abilities, such as psychological counseling, remains largely untapped. This paper investigates the key question: \textit{Can LLMs be effectively applied to psychological counseling?} To determine whether an LLM can effectively take on the role of a psychological counselor, the first step is to assess whether it meets the qualifications required for such a role, namely the ability to pass the U.S. National Counselor Certification Exam (NCE). This is because, just as a human counselor must pass a certification exam to practice, an LLM must demonstrate sufficient psychological knowledge to meet the standards required for such a role. To address this, we introduce PsychCounsel-Bench, a benchmark grounded in U.S.national counselor examinations, a licensure test for professional counselors that requires about 70\% accuracy to pass. PsychCounsel-Bench comprises approximately 2,252 carefully curated single-choice questions, crafted to require deep understanding and broad enough to cover various sub-disciplines of psychology. This benchmark provides a comprehensive assessment of an LLM's ability to function as a counselor. Our evaluation shows that advanced models such as GPT-4o, Llama3.3-70B, and Gemma3-27B achieve well above the passing threshold, while smaller open-source models (e.g., Qwen2.5-7B, Mistral-7B) remain far below it. These results suggest that only frontier LLMs are currently capable of meeting counseling exam standards, highlighting both the promise and the challenges of developing psychology-oriented LLMs. We release the proposed dataset for public use: https://github.com/cloversjtu/PsychCounsel-Bench
Maciej Besta, Shriram Chandran, Robert Gerstenberger
et al.
We introduce MBTI-in-Thoughts, a framework for enhancing the effectiveness of Large Language Model (LLM) agents through psychologically grounded personality conditioning. Drawing on the Myers-Briggs Type Indicator (MBTI), our method primes agents with distinct personality archetypes via prompt engineering, enabling control over behavior along two foundational axes of human psychology, cognition and affect. We show that such personality priming yields consistent, interpretable behavioral biases across diverse tasks: emotionally expressive agents excel in narrative generation, while analytically primed agents adopt more stable strategies in game-theoretic settings. Our framework supports experimenting with structured multi-agent communication protocols and reveals that self-reflection prior to interaction improves cooperation and reasoning quality. To ensure trait persistence, we integrate the official 16Personalities test for automated verification. While our focus is on MBTI, we show that our approach generalizes seamlessly to other psychological frameworks such as Big Five, HEXACO, or Enneagram. By bridging psychological theory and LLM behavior design, we establish a foundation for psychologically enhanced AI agents without any fine-tuning.
In this paper, we introduce a novel psychological benchmark, CPsyExam, constructed from questions sourced from Chinese language examinations. CPsyExam is designed to prioritize psychological knowledge and case analysis separately, recognizing the significance of applying psychological knowledge to real-world scenarios. From the pool of 22k questions, we utilize 4k to create the benchmark that offers balanced coverage of subjects and incorporates a diverse range of case analysis techniques.Furthermore, we evaluate a range of existing large language models~(LLMs), spanning from open-sourced to API-based models. Our experiments and analysis demonstrate that CPsyExam serves as an effective benchmark for enhancing the understanding of psychology within LLMs and enables the comparison of LLMs across various granularities.
Mohammad Amin Abbasi, Farnaz Sadat Mirnezami, Hassan Naderi
This paper explores the intersection of psychology and artificial intelligence through the development and evaluation of specialized Large Language Models (LLMs). We introduce PsychoLex, a suite of resources designed to enhance LLMs' proficiency in psychological tasks in both Persian and English. Key contributions include the PsychoLexQA dataset for instructional content and the PsychoLexEval dataset for rigorous evaluation of LLMs in complex psychological scenarios. Additionally, we present the PsychoLexLLaMA model, optimized specifically for psychological applications, demonstrating superior performance compared to general-purpose models. The findings underscore the potential of tailored LLMs for advancing psychological research and applications, while also highlighting areas for further refinement. This research offers a foundational step towards integrating LLMs into specialized psychological domains, with implications for future advancements in AI-driven psychological practice.
Research into the external behaviors and internal mechanisms of large language models (LLMs) has shown promise in addressing complex tasks in the physical world. Studies suggest that powerful LLMs, like GPT-4, are beginning to exhibit human-like cognitive abilities, including planning, reasoning, and reflection. In this paper, we introduce a research line and methodology called LLM Psychology, leveraging human psychology experiments to investigate the cognitive behaviors and mechanisms of LLMs. We migrate the Typoglycemia phenomenon from psychology to explore the "mind" of LLMs. Unlike human brains, which rely on context and word patterns to comprehend scrambled text, LLMs use distinct encoding and decoding processes. Through Typoglycemia experiments at the character, word, and sentence levels, we observe: (I) LLMs demonstrate human-like behaviors on a macro scale, such as lower task accuracy and higher token/time consumption; (II) LLMs exhibit varying robustness to scrambled input, making Typoglycemia a benchmark for model evaluation without new datasets; (III) Different task types have varying impacts, with complex logical tasks (e.g., math) being more challenging in scrambled form; (IV) Each LLM has a unique and consistent "cognitive pattern" across tasks, revealing general mechanisms in its psychology process. We provide an in-depth analysis of hidden layers to explain these phenomena, paving the way for future research in LLM Psychology and deeper interpretability.
Recent studies of multimodal industrial anomaly detection (IAD) based on 3D point clouds and RGB images have highlighted the importance of exploiting the redundancy and complementarity among modalities for accurate classification and segmentation. However, achieving multimodal IAD in practical production lines remains a work in progress. It is essential to consider the trade-offs between the costs and benefits associated with the introduction of new modalities while ensuring compatibility with current processes. Existing quality control processes combine rapid in-line inspections, such as optical and infrared imaging with high-resolution but time-consuming near-line characterization techniques, including industrial CT and electron microscopy to manually or semi-automatically locate and analyze defects in the production of Li-ion batteries and composite materials. Given the cost and time limitations, only a subset of the samples can be inspected by all in-line and near-line methods, and the remaining samples are only evaluated through one or two forms of in-line inspection. To fully exploit data for deep learning-driven automatic defect detection, the models must have the ability to leverage multimodal training and handle incomplete modalities during inference. In this paper, we propose CMDIAD, a Cross-Modal Distillation framework for IAD to demonstrate the feasibility of a Multi-modal Training, Few-modal Inference (MTFI) pipeline. Our findings show that the MTFI pipeline can more effectively utilize incomplete multimodal information compared to applying only a single modality for training and inference. Moreover, we investigate the reasons behind the asymmetric performance improvement using point clouds or RGB images as the main modality of inference. This provides a foundation for our future multimodal dataset construction with additional modalities from manufacturing scenarios.
Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on `well-being', then compared them against research ideas conceived by doctoral scholars and those produced solely by the LLM. Interestingly, our combined approach of a LLM and causal graphs mirrored the expert-level insights in terms of novelty, clearly surpassing the LLM-only hypotheses (t(59) = 3.34, p=0.007 and t(59) = 4.32, p<0.001, respectively). This alignment was further corroborated using deep semantic analysis. Our results show that combining LLM with machine learning techniques such as causal knowledge graphs can revolutionize automated discovery in psychology, extracting novel insights from the extensive literature. This work stands at the crossroads of psychology and artificial intelligence, championing a new enriched paradigm for data-driven hypothesis generation in psychological research.
Yi Zhang, Jingyi Zhao Department of Economics and Management, Shanghai Institute of Technology, Shanghai, People’s Republic of ChinaCorrespondence: Yi Zhang, Department of Economics and Management, Shanghai Institute of Technology, No. 120 Caobao Road, Shanghai, People’s Republic of China, Tel +86 13564864307, Email km0513@sina.comBackgrounds and Aims: In the era of the service economy, the personalized needs of customers are increasing rapidly. It often occurs that frontline employees bend organizational rules to help customers. The study sought to explore the influence mechanism of specific dimensions of customer-oriented deviance on brand trust from the customer’s perspective, examine the mediating role of perceived benefits and perceived uncertainty, and the moderating role of customer involvement in the process.Methods: We conducted an online survey study in China from May 1 to 20, 2022. We use online survey questionnaire technique and random sampling method for data collection. Participants anonymously completed the measures of customer-oriented deviance scale, perceived benefits scale, perceived uncertainty scale, brand trust scale, and customer involvement scale.Results: The results show that deviant service adaptation and deviant use of resources positively affect brand trust through the mediation of perceived benefits. In contrast, deviant service communication has a negative effect on brand trust through the mediation of perceived uncertainty. Furthermore, customer involvement plays a negative moderating role in the relationship between deviant service adaptation, deviant use of resources, and perceived benefits. Customer involvement plays a negative moderating role in the relationship between deviant service communication and perceived uncertainty.Conclusion: Current results demonstrated that there is a double-edged sword effect of deviant customer-oriented behaviors on brand trust. Deviant service adaptation and deviant use of resources positively affect perceived benefits, and perceived benefits positively affect brand trust. Deviant service communication positively affects perceived uncertainty, and perceived uncertainty negatively affects brand trust. Customer involvement plays a negative moderating role in the above processes. This study enriches the study of customer psychological states of customer-oriented deviance, which helps managers use organizational resources rationally to guide and control different types of deviant customer-oriented behaviors effectively. It provides inspiration and references for management practice.Keywords: customer-oriented deviance, perceived benefits, perceived uncertainty, brand trust, customer involvement
Psychological trait estimation from external factors such as movement and appearance is a challenging and long-standing problem in psychology, and is principally based on the psychological theory of embodiment. To date, attempts to tackle this problem have utilized private small-scale datasets with intrusive body-attached sensors. Potential applications of an automated system for psychological trait estimation include estimation of occupational fatigue and psychology, and marketing and advertisement. In this work, we propose PsyMo (Psychological traits from Motion), a novel, multi-purpose and multi-modal dataset for exploring psychological cues manifested in walking patterns. We gathered walking sequences from 312 subjects in 7 different walking variations and 6 camera angles. In conjunction with walking sequences, participants filled in 6 psychological questionnaires, totalling 17 psychometric attributes related to personality, self-esteem, fatigue, aggressiveness and mental health. We propose two evaluation protocols for psychological trait estimation. Alongside the estimation of self-reported psychological traits from gait, the dataset can be used as a drop-in replacement to benchmark methods for gait recognition. We anonymize all cues related to the identity of the subjects and publicly release only silhouettes, 2D / 3D human skeletons and 3D SMPL human meshes.
This research paper focuses on the integration of Artificial Intelligence (AI) into the currency trading landscape, positing the development of personalized AI models, essentially functioning as intelligent personal assistants tailored to the idiosyncrasies of individual traders. The paper posits that AI models are capable of identifying nuanced patterns within the trader's historical data, facilitating a more accurate and insightful assessment of psychological risk dynamics in currency trading. The PRI is a dynamic metric that experiences fluctuations in response to market conditions that foster psychological fragility among traders. By employing sophisticated techniques, a classifying decision tree is crafted, enabling clearer decision-making boundaries within the tree structure. By incorporating the user's chronological trade entries, the model becomes adept at identifying critical junctures when psychological risks are heightened. The real-time nature of the calculations enhances the model's utility as a proactive tool, offering timely alerts to traders about impending moments of psychological risks. The implications of this research extend beyond the confines of currency trading, reaching into the realms of other industries where the judicious application of personalized modeling emerges as an efficient and strategic approach. This paper positions itself at the intersection of cutting-edge technology and the intricate nuances of human psychology, offering a transformative paradigm for decision making support in dynamic and high-pressure environments.