Oct 2026· International Journal of Educational Management· pp. 1-17· 37 references
Artificial Intelligence in Education
Abstract
Purpose This study examines how generative artificial intelligence (GenAI) is perceived as a decision-support mechanism in school principals’ decision-making, focusing on its perceived implications for decisional capital within a centralized education system. Design/methodology/approach Semi-structured interviews were conducted with 17 public school principals across different school levels in Türkiye. Data were analyzed using thematic analysis to identify how GenAI is interpreted and positioned within principals’ decision-making processes. Findings Principals reported decision-making challenges driven by bureaucratic procedures, restricted autonomy and competing stakeholder demands. Within these constraints, GenAI was seen not as a substitute for professional judgment but as cognitive support that may improve access to information, facilitate scenario-based reasoning and inform evidence-based judgments. Principals reported varying levels of readiness to integrate GenAI into organizational decision-making, with this variation associated with digital competence, institutional support and ethical awareness. Originality/value The study extends discussions of principals’ decision-making by linking GenAI-enabled decision support to the exercise of decisional capital under centralized governance constraints, highlighting conditions under which GenAI may contribute to human-centered and ethically grounded organizational decision-making processes.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
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...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
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.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9