Transformation of Madrasah Teachers’ Scientific Writing Competence: Integration of Artificial Intelligence, Self-Efficacy, and Demystification of Online Journal Systems Through Sustainable Publication Clinics
Aug 2026· Al Musa’adah Journal of Community Service STAI Minhajul Haq· Vol 1, pp. 78-98· 13 references
Abstract
Teachers’ scientific publication competence remains a major challenge in implementing Sustainable Professional Development (Pengembangan Keprofesian Berkelanjutan/PKB), particularly in madrasah education, where limited publication literacy, insufficient mentoring, and low digital scholarly competence often hinder publication outcomes. This study aimed to evaluate the effectiveness of a Sustainable Scientific Publication Clinic in improving madrasah teachers’ scientific publication competence through continuous individualized mentoring and ethical integration of digital scholarly tools. The study employed a mixed-methods approach using an explanatory sequential design involving 30 teachers from Madrasah Tsanawiyah (MTs) and Madrasah Aliyah (MA). Quantitative data were collected using a pretest–posttest scientific publication competence questionnaire, while qualitative data were obtained through observations and semi-structured interviews with ten purposively selected participants. The intervention was implemented over three months through academic writing workshops, individualized coaching, ethical use of Generative Artificial Intelligence (AI), Mendeley training, and Open Journal System (OJS) publication mentoring. Paired Sample t-test results showed a significant increase in the mean competency score from 2.26 to 4.36 (p < 0.001), with a very large effect size (Cohen’s d = 2.91) and an N-Gain of 77%. Furthermore, 73.33% of participants successfully submitted manuscripts, while 26.67% achieved article acceptance. Qualitative findings indicated that continuous mentoring, ethical AI utilization, and digital scholarly tools reduced writing barriers, strengthened self-efficacy, and improved participants’ readiness to complete the publication process. These findings suggest that the Sustainable Scientific Publication Clinic provides an effective, technology-supported, and output-oriented mentoring model for strengthening teachers’ scientific publication competence and supporting sustainable professional development.
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.· arXiv.org· 216 citations· ⚡17
Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method 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· EUROMICRO Conference on Soft...· 64 citations· ⚡6
In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
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.· Advances in Neural Informati...· 59 citations· ⚡8
Progress in the field of artificial intelligence has been accelerating rapidly in the past two decades. Various autonomous systems from purely digital ones to autonomous vehicles are being developed and deployed out on the field. As these systems exert a growing impact on society, ethics in relation to artificial intelligence and autonomous systems have recently seen growing attention among the academia. However, the current literature on the topic has focused almost exclusively on theory and more specifically on conceptualization in the area. To widen the body of knowledge in the area, we conduct an empirical study on the current state of practice in artificial intelligence ethics. We do so by means of a multiple case study of five case companies, the results of which indicate a gap between research and practice in the area. Based on our findings we propose ways to tackle the gap.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6
Molecular dynamics simulations hold great promise for providing insight into the microscopic behavior of complex molecular systems. However, their effectiveness is often constrained by long timescales associated with rare events. Enhanced sampling methods have been developed to address these challenges, and recent years have seen a growing integration with machine learning techniques. This Review provides a comprehensive overview of how they are reshaping the field, with a particular focus on the data-driven construction of collective variables. Furthermore, these techniques have also improved biasing schemes and unlocked novel strategies via reinforcement learning and generative approaches. In addition to methodological advances, we highlight applications spanning different areas, such as biomolecular processes, ligand binding, catalytic reactions, and phase transitions. We conclude by outlining future directions aimed at enabling more automated strategies for rare-event sampling.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 54 citations
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
