Results 271 to 280 of about 1,819,138 (329)
New Directions for Adult and Continuing Education, EarlyView.ABSTRACT Mental well‐being is central to adult learner success, yet many adult education institutions lack capacity to provide timely and accessible support. This article examines how artificial intelligence (AI) can strengthen mental health–adjacent supports in adult and continuing higher education, with attention to professional practice and ...
Adam L. McClain, Thomas Wade
wiley +1 more source
New Directions for Adult and Continuing Education, EarlyView.ABSTRACT This article examines the evolving role of organizational leadership amidst the rapid advancements in artificial intelligence (AI). It explores a broadly experienced and documented crisis in leadership, due in part to the disruptive nature of AI and emerging technology.
Rachel Wlodarsky, Davin Carr Chellman
wiley +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Causal Discovery via Causal Star Graphs
ACM Transactions on Knowledge Discovery From Data, 2023Discovering causal relationships among observed variables is an important research focus in data mining. Existing causal discovery approaches are mainly based on constraint-based methods and functional causal models (FCMs). However, the constraint-based method cannot identify the Markov equivalence class and the functional causal models cannot identify
Boxiang Zhao +2 more
exaly +2 more sources
9th IEEE International Conference on Cognitive Informatics (ICCI'10), 2010 The standard causal discovery assumes that all variables are available from the beginning. In this paper, we consider an untouched scenario in which not all variables are available in advance. We call this scenario online causal discovery which assumes that the target of interest is given in advance while the other variables are unknown.
Kui Yu, Xindong Wu 0001, Hao Wang 0008
openaire +2 more sources
Local Causal Discovery Without Causal Sufficiency
Proceedings of the AAAI Conference on Artificial IntelligenceLocal causal discovery is crucial for revealing the causal relationships between specific variables from data. Existing local causal discovery algorithms are designed under the assumption of causal sufficiency, which states that there are no latent common causes for two or more of the observed variables in data.
Zhaolong Ling +7 more
openaire +2 more sources
A survey of causal discovery based on functional causal model
Engineering Applications of Artificial IntelligenceShanshan Huang, Jun Liao
exaly +2 more sources
Causal Discovery Using A Bayesian Local Causal Discovery Algorithm
2004This study focused on the development and application of an efficient algorithm to induce causal relationships from observational data. The algorithm, called BLCD, is based on a causal Bayesian network framework. BLCD initially uses heuristic greedy search to derive the Markov Blanket (MB) of a node that serves as the “locality” for
Subramani Mani, Gregory F. Cooper
openaire +2 more sources
CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series
International Conference on Learning RepresentationsCausal discovery, or identifying causal relationships from observational data, is a notoriously challenging task, with numerous methods proposed to tackle it.
Gideon Stein +4 more
semanticscholar +1 more source
LLM-Driven Causal Discovery via Harmonized Prior
IEEE Transactions on Knowledge and Data EngineeringTraditional domain-specific causal discovery relies on expert knowledge to guide the data-based structure learning process, thereby improving the reliability of recovered causality. Recent studies have shown promise in using the Large Language Model (LLM)
Taiyu Ban +5 more
semanticscholar +1 more source