Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to extend the TOE Framework

• URL: http://arxiv.org/abs/2207.12052v1
• Date: Mon, 25 Jul 2022 10:42:50 GMT
• Title: Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to extend the TOE Framework
• Authors: Ali Faqihi and Shah J Miah
• Abstract summary: This study aims to identify the new requirements for developing AI-oriented artifacts to address talent management issues. A design science method is adopted for conducting the experimental study with structured machine learning techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI has the potential to improve approaches to talent management enabling dynamic provisions through implementing advanced automation. This study aims to identify the new requirements for developing AI-oriented artifacts to address talent management issues. Focusing on enhancing interactions between professional assessment and planning attributes, the design artifact is an intelligent employment automation solution for career guidance that is largely dependent on a talent intelligent module and an individuals growth needs. A design science method is adopted for conducting the experimental study with structured machine learning techniques which is the primary element of a comprehensive AI solution framework informed through a proposed moderation of the technology-organization-environment theory.

Related papers

• Generative AI and Its Impact on Personalized Intelligent Tutoring Systems [0.0]
  Generative AI enables personalized education through dynamic content generation, real-time feedback, and adaptive learning pathways. Report explores key applications such as automated question generation, customized feedback mechanisms, and interactive dialogue systems. Future directions highlight the potential advancements in multimodal AI integration, emotional intelligence in tutoring systems, and the ethical implications of AI-driven education.
  arXiv  Detail & Related papers  (2024-10-14T16:01:01Z)
• Generative AI Application for Building Industry [10.154329382433213]
  This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
  arXiv  Detail & Related papers  (2024-10-01T21:59:08Z)
• The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies [0.7796141041639462]
  The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies.
  arXiv  Detail & Related papers  (2024-04-03T20:08:15Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Competency Model Approach to AI Literacy: Research-based Path from Initial Framework to Model [0.0]
  Research on AI Literacy could lead to an effective and practical platform for developing these skills. We propose and advocate for a pathway for developing AI Literacy as a pragmatic and useful tool for AI education.
  arXiv  Detail & Related papers  (2021-08-12T15:42:32Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Enabling Design Methodologies and Future Trends forEdge AI: Specialization and Co-design [37.54971466190214]
  We provide a comprehensive survey of the latest enabling design methodologies that span the entire edge AI development stack. We suggest that the key methodologies for effective edge AI development are single-layer specialization and cross-layer co-design.
  arXiv  Detail & Related papers  (2021-03-25T16:29:55Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.