Gamified Career Paths: The Talent Tree as a Model for Human Resource Development

This paper introduces a talent tree model from gaming to human resource development, advocating for a gamified approach to professional growth. The model emphasizes scalable career progression, enhances employee engagement, and addresses ethical challenges in gamification. We explore the application of this model in HR practices, demonstrating its potential to modernize employee development and align work with evolving digital competencies. This paper highlights the benefits and obstacles of adopting game-based models for career development, suggesting that such integration can transform traditional talent management into a more dynamic and interactive process

Integration of Large Language Models and Digital Twins in the Public Sector

The potential of large language models (LLMs) for generative artificial intelligence that underpin chatbots like ChatGPT, Gemini, or Neuroflash to improve both personal and organizational work processes is enormous. In this paper, we discuss how LLMs could be integrated with another emerging digital technology concept, digital twins, to optimize and automate processes in the public sector. This integration can allow for accurate and detailed modeling of complex systems and interactions within society, and enhance decision-making, policy development and strategic planning through simulation and automation

Exploring Covert Bias in Large Language Models - Experimental Evidence of Racial Discrimination in Resume Creation and Selection

Fine-tuning efforts have led to progress in reducing overt, obvious gender and racial biases in the latest generation of large language models (LLMs). Here we study covert, non-obvious bias in LLM-based chat systems. We run a two-stage experiment in the hiring context consisting of resume creation and selection. We use ChatGPT-4o to create resumes for minority, ethnic candidates and majority, baseline candidates. After removal of all identifying markers, we run pair-wise selection tests and find that resumes of majority candidates are stronger, winning contests in 80% of the time. This suggests that racial markers lead to encoding of biases in resume generation in imperceptible ways. Covert biases are difficult to spot and hard to address, but they deserve urgent attention, as the latest models are becoming increasingly capable of inferring user characteristics from conversations, potentially biasing content in unwanted and unexpected ways. We discuss implications and avenues for future research

Not So Fast: Mapping the Learning Speed and Sophistication in GenAI

Among many functionalities, Generative Artificial Intelligence (GenAI) can model the topology and semantics of user-supplied datasets – a functionality required to evaluate learning levels through mind maps. Since GenAI evolves by orchestrated changes to the underlying algorithms and, organically, by learning, we need to understand this evolution’s speed and reliability. We conducted two experiments tasking ChatGPT with scoring mind maps drawn by 113 undergraduate students describing their motivation and deterrence towards entrepreneurship. Scoring used a five-dimensional model consisting of self-efficacy, internal locus of control, need for growth, intrinsic motivation, and resilience. We repeated the analysis on the original dataset after eight months to time the evolving pace and sophistication of the tools used. The results show that we should not fall into the “hype” curve typical of the beginning of any emerging technology. While the pace of learning in GenAI is unprecedented, caution is necessary when rechecking data and analytical techniques

I Trust You So You are Part of Our Team: the Influence of Group Trust and Trust in Social Robots on In-Group Perception

Social robots are technically capable of acting as robotic team members, enabled by the advances in artificial intelligence. However, research has shown the importance of also perceiving robots as part of the in-group instead of as out-group members, who are often met with avoidance and resistance. Research is lacking an understanding of the antecedents of in-group perception in robots and the interplay with trust. This study addresses this gap by conducting a between-subject lab experiment with 18 teams of three humans and one social robot. Our findings indicate that trust in the group can stimulate trust in the robot. Additionally, our findings demonstrate that trust skews with the perception of the robots’ actions, which are perceived as more favorable for the group. This, in turn, increases the in-group perception of the social robot. This research contributes to social categorization and trust research in human-agent team and human-robot interaction research

Artificial Intelligence in the Workplace a Paradox: Contributor to Loneliness and Enhancer of Organisational and Employee Health

The utilization of artificial intelligence (AI) is on the rapid rise in organizations across the world. The perceived benefits of AI, such as enhanced operations, improved efficiency, and a driver of growth, are some reasons for its increased adoption. The side effects of leveraging AI on people are becoming apparent, and one of these is loneliness. Loneliness can be the cause of or exacerbate mental and physical health issues. This paper outlines the current state of loneliness contributed by AI usage in the workplace. This paper hopes to inform future research to ideate AI solutions that ameliorate said issues. The Job Demands–Resources model and Bright ICT Initiative taxonomy are drawn on to inform the study. Causal loop diagrams are utilized to understand the effect of AI on mental and physical health. We leverage systems thinking to understand the issues and identify appropriate solutions for ameliorating loneliness in the workplace

Echolocation-based Smartphone Assistive Applications in Spatial Perception and Navigation for Blind and Low Vision Users: A Systematic Review

With the expected increase of blind and low vision (BLV) individuals, supporting spatial perception and navigation is critical. Echolocation is recognized as effective in conveying spatial information, and the advancement of smartphone technologies has prompted interest in its use as assistive technology. This systematic review assesses the potential of echolocation-based smartphone assistive applications (EBSAs) and formulates a set of design principles to guide future research and implementation. Of the 17 included studies, nine described echolocation-based assistive technologies (EBATs) and eight discussed smartphone assistive applications. Findings reveal that EBATs and smartphone assistive applications facilitate information gain, ease-of-use, and independence. However, EBATs can present steep learning curves and technological limitations, whilst smartphone applications are limited by sensory inaccuracy and compatibility issues. The findings of this study highlight the need to improve user-centric design, integrate existing technologies and multimodal feedback, as well as encourage stakeholder collaboration in design, development, and testing

Vocabulary knowledge towards L2 reading and listening performance

The current study investigates whether vocabulary breadth, vocabulary depth, and vocabulary fluency contribute differentially to L2 reading and listening comprehension. One hundred and thirty-eight EFL learners first took three vocabulary knowledge tests that measured vocabulary fluency, vocabulary breadth, and vocabulary depth. Subsequently, they completed the reading and listening components of an IELTS practice test. The structural equation model reveals that vocabulary breadth explained a significant proportion of the variance in both reading and listening scores. Vocabulary fluency demonstrates a significant predictive power over listening comprehension but not reading comprehension. Vocabulary depth has a significant predictive power for listening but not for reading. Pedagogical implications of the results were discussed

Spatial repertoires in mixed-reality-based simulations for L2 teacher telecollaboration

In telecollaboration research, scholars have broadened their focus from the purely linguistic details of online intercultural encounters to include its multimodal dimensions. Yet, no study to date has explored spatial repertoires, namely the totality of semiotic resources (e.g., speech, image, objects) embedded in a particular environment and used during teaching and teacher telecollaboration. To add to the literature on this topic, this telecollaborative project invited language teachers in Taiwan and the U.S. to first use a mixed-reality (MR) simulation technology for enacting lessons with avatar students, in order to examine the spatial repertoires that unfolded during instruction, and then to reflect on their own as well as each others' teaching. Drawing on video recordings of teacher instruction, as well as lesson plans, written reflections, and post-lesson telecollaborative interactions with each other, we identified rich spatial repertoires emerging from deeply intertwined individual repertoires, from diverse semiotic resources afforded in the MR-based simulation space, and from the sequential telecollaborative tasks. The findings highlight the agentive and performative role of semiotic resources in this virtual space (especially the avatar teaching videos) in deepening L2 teachers’ intercultural understanding, which indicates the potential contributions of integrating MR simulations into telecollaboration for teacher intercultural learning