The article reviews how generative AI influences the long‑term retention of knowledge, skills, and judgment—collectively termed cognitive capital—in both individuals and organizations. It identifies four mechanisms (substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation) through which AI interaction can either deplete or enhance cognitive capital, and proposes seven evidence‑based principles and five design levers for organizations. The authors argue that cognitive capital provides a multilevel framework for governing AI adoption and call for longitudinal, multilevel research to test their propositions.
arXiv:2609.15624v1 Announce Type: cross Abstract: Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We...
The paper proposes a method for assessing AI suitability for workplace tasks by profiling both AI agents and tasks along shared cognitive capability dimensions. It uses benchmark performance to infer agent capabilities and expert input to weight task requirements, allowing independent updates as models and roles evolve. The authors validate the approach on synthetic agents, six AI systems, and 410 employees across six domains, showing that AI systems vary more by cognitive dimension than by model family, while workplace tasks share a common cognitive core.
arXiv:2606. 12441v1 Announce Type: cross Abstract: The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative artificial intelligence (AI) proliferates in educational settings.
arXiv:2512. 12413v2 Announce Type: replace Abstract: Generative AI tools are increasingly embedded in everyday work and learning, yet their fluency, opacity, and propensity to hallucinate mean that users must critically evaluate AI outputs rather than accept them at face value.
arXiv:2606. 15601v1 Announce Type: cross Abstract: We introduce SCAN -- a human-centric decision-making framework to facilitate learners for effective task allocation with Generative Artificial Intelligence (GenAI) based on Vygotsky's Zone of Proximal Development and Metacognition.
arXiv:2606. 17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings.
arXiv:2608. 07779v1 Announce Type: new Abstract: Artificial intelligence is changing the task composition of computing work faster than curricula and training typically adapt.
arXiv:2412. 19754v4 Announce Type: replace-cross Abstract: Artificial Intelligence (AI) is transforming the nature of work, yet there is limited empirical evidence on how it affects demand for human skills.
arXiv:2407. 10247v3 Announce Type: replace-cross Abstract: The integration of Artificial Intelligence (AI) into corporate strategy has become critical for organizations seeking to maintain competitive advantage in the digital age.
arXiv:2605.27864v5 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in finance, yet most existing work emphasizes trading signals or financial NLP tasks centered...
OfficeTown is a generative simulation framework that models professional organizations, where tasks and context evolve from ongoing work. Starting from a natural‑language description of an organization’s purpose, the system initializes members who coordinate, delegate assignments to assistant agents, and review deliverables, while artifacts and decisions persist to influence future tasks. The authors built OfficeTown Bench with 406 tasks and 4,836 evaluation criteria across 23 simulated organizations in eight domains, and show that fine‑tuning and on‑policy distillation on this data improves Qwen3.5‑9B on external professional‑work benchmarks, with a principal–assistant setup outperforming single‑ and multi‑agent baselines.
arXiv:2609.14425v1 Announce Type: cross Abstract: Generative artificial intelligence raises a central question for scientific training and organization. Is research shifting from deep specialization...