Oct 2026· Journal of islamic education· 1 citation· ⚡ 1 influential· 46 references
Education and Character Development
Abstract
Digital Islamic learning presents challenges involving unverified religious claims, artificial intelligence outputs, privacy concerns, and unequal access. This study aims to reconstruct the philosophy of ta’dib as a conceptual foundation for character education in digitally mediated Islamic learning. It employs a qualitative conceptual approach through purposive document analysis and an integrative synthesis of literature on educational philosophy and digital ethics. The findings propose the Ta’dib-Oriented Digital Character Pedagogy (TDCP) model, comprising five dimensions: epistemic integrity, dignified interaction, responsible digital agency, equitable participation, and reflective self-discipline. These dimensions are implemented through a cycle of inquiry, deliberation, action, and reflection to support formative assessment and protect learner privacy. The model offers a pedagogical framework for developing ethical reasoning without equating observable behavior with piety. Expert validation and empirical testing are needed to assess its applicability in educational settings.
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
