Skip to content

Artificial Intelligence and Leadership Practices: Enhancing Engagement and Productivity

Sep 2026 · Administrative Sciences · 0 citations · 65 references

Abstract

This research investigates the impact of artificial intelligence (AI) and change leadership on educator engagement and productivity within the Lebanese educational sector. This study is grounded in both social cognitive theory (SCT) and dynamic capabilities theory (DCT), and it examines how AI adoption and change leadership, together with the human-centric factor of educator engagement, influence educators’ productivity in the context of a developing country. Data were collected from educators from various institutions in Lebanon. Confirmatory factor analysis (CFA) was used to verify the validity and reliability of the latent factors under consideration. The hypothesized model derived from SCT and DCT and the literature was tested with structural equation modeling (SEM). The results show that change leadership significantly improves educator engagement and productivity, highlighting its important role in supporting technological transformation in educational institutions. AI has an indirect impact on productivity through educator engagement, which acts as a key mediating factor. These findings stress the significance of promoting a human-centered approach to digital transformation in education. The research offers important insights for policymakers, educators, and institutional leaders who seek to use AI effectively, particularly in environments with limited resources.

Read PDF

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.

Jinhe Bi, Yifan Wang, Danqi Yan et al. · 73 citations · ⚡4
#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.

Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al. · 59 citations · ⚡8
#artificial intelligence Review Mar 2025

A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives

A comprehensive survey of the leading simulation environments and platforms used for multi-agent cooperative decision-making and an in-depth analysis for these simulation environments from various perspectives, including task formats, reward allocation, and the underlying technologies employed.

Weiqiang Jin, Hong-Yang Du, Biao Zhao et al. · 57 citations · ⚡6

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.