Aug 2026· Annual Review of Organizational Psychology and Organizational Behavior· 0 citations
TL;DR
A wide variety of outcomes and emerging issues such as the ability of chatbots to show empathy, the ability to codify what had been tacit human knowledge, and the development of digital twins are described, which predict responses from specific individuals.
Abstract
We review recent research on the trio of artificial intelligence (AI) practices—electronic platforms, machine learning, and large language models—and their effects on those who use and work with them. Because these tools take over specific tasks from existing jobs, the important effects are how the remaining work changes and how workers react to it. Perhaps our main conclusion is that choices as to how they are used are the most important factor in their effects on those who use them. For example, do they take over boring tasks like note-taking or interesting ones like drafting reports? Do workers spend their saved time checking AI output or taking on new tasks? We describe the wide variety of outcomes and emerging issues such as the ability of chatbots to show empathy, the ability to codify what had been tacit human knowledge, and the development of digital twins, which predict responses from specific individuals.
An automated, data-driven approach to uncover patterns, which the authors term traits, of effective human-AI interaction that are aligned with task outcomes is explored and Principal Trait Analysis is proposed, a Principal Component Analysis-inspired algorithm for deriving common traits from patterns in LLM conversations.
Hunter McNichols, Kai Du, Andrew S. Lan· 0 citations
This research paper reviews the three mechanisms through which artificial intelligence affects the labour market: substitution, creation, and augmentation. Unlike the previous wave of automation, artificial intelligence can perform both manual routine operations and cognitive tasks that require knowledge, thereby expanding its effects on employment. The paper provides evidence that AI can substitute codifiable tasks, create jobs in AI and the digital economy, and increase employee productivity through human-machine cooperation. Nevertheless, these AI effects are not equally distributed. Changes in skills demand due to the introduction of artificial intelligence manifest as increased value placed on skills such as digital literacy, data processing and analysis, problem-solving across disciplines, and lifelong learning, while routine jobs and entry-level positions become more vulnerable. Moreover, changes in employment expectations due to AI affect students and young workers, generating both anxiety and increased motivation for skill development. Previous studies show that AI changes tasks rather than occupations, but differ in their assessments of its employment effects.
Hongjie Chen, Zhen-Wei Tang· Journal of Applied Economics...· 0 citations
This study examines how students work with artificial intelligence (AI) in learning, moving beyond the simple view of dependence versus control. It focuses on what students actually do when using AI and how they make decisions around it. The study is based on 18 in-depth interviews selected from more than 30 student responses. These were chosen for their clarity and detail, allowing a closer look at learning practices. The findings show that students rarely accept AI-generated content as it is. Instead, they check, adjust, and sometimes rewrite it before using it. In many cases, AI serves as a starting point rather than a final answer. This shifts cognitive effort toward evaluating, selecting, and making sense of information. To explain this, the study introduces the concept of negotiated intelligence, referring to how students regulate AI use while maintaining control over meaning. The results also highlight the role of contextual intelligence, especially when adapting content to local situations or specific audiences. These practices are linked to perceived development in critical evaluation, adaptive reasoning, and contextual understanding, which may be relevant for human capital formation in digital learning environments.
Thu Thi Dang, Hang, Dao San Tran et al.· Tạp chí Khoa học Đại học Côn...· 0 citations
Artificial intelligence is entering the workflow faster than most organizations are redesigning learning. This paper argues that the central L&D challenge is no longer individual AI literacy alone, but the collective capability of teams to reason, coordinate, challenge, remember, and improve with AI. An integrative review of peer-reviewed research and recent workforce studies is used to connect human-AI teaming, transactive memory, shared models, workplace learning, and capability development. The evidence is striking: 84% of executives expect regular human-AI collaboration within three years, yet only 26% of workers report being trained to collaborate effectively with AI; high-performing teams report higher AI use than other teams (78% versus 54%), but their advantage is also associated with trust, apprenticeship, agility, and human connection. The paper proposes the CYCLE framework: Clarify roles, Yield to evidence, Capture memory, Learn through correction, and Embed routines. It translates the framework into a practical operating model for L&D, including team simulations, decision-trace practices, peer challenge, AI debriefs, and measures of transfer at team level. The argument is deliberately human-centered: AI may accelerate access to knowledge, but collective capability develops only when people can question outputs, speak up, share judgment, and retain learning. The paper concludes with propositions and a field-research agenda for testing durable team capability over time.
Hemant Tale, Satwik P. M.· International journal of res...· 0 citations
Evidence suggests that purpose-built chatbot coaching systems may have some benefits as they are generally well received and can support short-term motivation and selected behavior change, but effects for sustained, meaningful outcomes are inconsistent.
Jason T. Potel, M. Kumashiro· Behavioral Science· 0 citations
An AI-enabled lifecycle of Creation, Transformation, Transmission, Evaluation, Evaluation, and Governance is proposed and an AI-eWOM fit perspective is developed and a TCCM-organized research agenda identifies priorities for future research.
A. Joyal· Journal of business and mana...· 0 citations
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