Reinforcement learning (RL) has achieved remarkable success in various domains, but its "black box" nature poses a significant challenge for real-world deployment. Understanding the rationale behind an RL agent's decisions is crucial for trust, debugging, and improving performance. This paper proposes a novel approach to explainable AI (XAI) within reinforcement learning by leveraging causal reasoning. We model the environment and the agent's policy using a causal Bayesian network. By performing inference through this network, we trace the causal chain of events leading to a specific action, providing a transparent explanation. This method moves beyond simply observing the agent's behavior to understanding the underlying reasons for its choices. The core of our approach lies in identifying and representing the causal relationships within the RL system, enabling us to dissect the decision-making process and ultimately build more robust and reliable RL agents. The proposed framework offers a significant step toward interpretable RL and addresses a critical limitation of current techniques. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Enterprise platforms are evolving from systems that primarily record and analyze business operations into intelligent environments capable of predicting outcomes, recommending interventions, executing decisions, and learning from their consequences. This article proposes a conceptual Autonomous Enterprise Platform (AEP) based on continuous AI-guided decision loops integrating enterprise sensing, contextual intelligence, predictive analytics, decision intelligence, prescriptive policies, autonomous execution, learning, and governance. The proposed framework extends the classical Monitor, Analyze, Plan, and Execute model of autonomic computing by incorporating continuous prediction, intervention, evaluation, and adaptation. The study synthesizes research published between 2000 and 2022 on autonomous agents, autonomic computing, self-adaptive systems, predictive process monitoring, reinforcement learning, and prescriptive analytics. Three key studies provide the conceptual foundation: Kephart and Chess on autonomic computing, Metzger et al. on proactive process adaptation using deep learning, and Kubrak et al. on prescriptive process monitoring. The framework distinguishes operational, learning, and governance loops to support continuous enterprise adaptation. It emphasizes the transition from predicting business outcomes to selecting and executing appropriate interventions. The study also examines challenges involving causal reasoning, intervention timing, resource constraints, model drift, explainability, and human oversight. Overall, AI-guided decision loops provide a foundation for adaptive, intelligent, and governed enterprise platforms capable of continuous decision making, organizational learning, and operational optimization.
Shekar Vollem· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to artificial intelligence—Neuro-Symbolic Logic Programming with Reinforcement Learning—designed to address the limitations of current AI techniques. The core idea is to integrate the pattern recognition capabilities of neural networks with the reasoning and explainability offered by symbolic logic programming, guided by reinforcement learning. We present a hybrid system where a neural network learns a high-level representation of a task, translating sensory inputs into abstract concepts. This representation is then fed into a symbolic logic engine, which executes predefined rules and generates plans. Reinforcement learning is utilized to optimize the neural network's representation and the logic engine's rule selection, allowing the system to adapt and improve its performance over time. This approach aims to create AI systems that are not only capable of complex behavior but also provide verifiable, logically sound explanations for their actions. The system's architecture and the interaction between its components are detailed, highlighting the potential for robust and explainable AI. We demonstrate a conceptual framework, outlining the key components and their interplay, and discuss potential future research directions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Programming students are no longer only learning to write code; they are also learning in environments where AI tools can explain, debug, and generate code alongside them. This shift creates a tension for programming education: the same tools that can make learning more accessible may also encourage dependence when students use them as substitutes for their own reasoning. Using a conceptual and narrative review of recent literature, this paper examines student dependence on generative AI tools in programming education. Central to this review is an examination of learning outcomes, independent programming ability, self-regulated learning, critical thinking, problem solving, learner characteristics, and instructional design in AI-supported programming environments. Students learning to code are increasingly using AI tools to answer questions, explain concepts, help debug, and make information easier to access, but their use can also create problems. Students who rely heavily on them, especially when instructors provide little instruction, may spend less time thinking through problems on their own or reflecting on their solutions. From the literature, we see that the impact of AI is more dependent on the learner, the learning environment, and the use of the tools than on the technology. Existing studies also have important weaknesses, including heavy use of self-reported measures, small sample sizes, correlational research, and limited evidence about what sustained AI use could mean for independent thinking, problem solving, and programming development over time. More data is needed to understand these longer-term effects.
Kazeem Babatunde Abioye· Journal of Education Learnin...· 0 citations
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This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22161441. Does the introduction explain the objective of the research presented in the preprint? Yes Are the methods well-suited for this research? Somewhat appropriate Are the conclusions supported by the data? Somewhat supported Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly Is the preprint likely to advance academic knowledge? Moderately likely Would it benefit from language editing? No Would you recommend this preprint to others? Yes, but it needs to be improved Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.
Yuchen Song· Zenodo (CERN European Organi...· 0 citations
This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22161441. Does the introduction explain the objective of the research presented in the preprint? Yes Are the methods well-suited for this research? Somewhat appropriate Are the conclusions supported by the data? Somewhat supported Are the data presentations, including visualizations, well-suited to represent the data? Somewhat appropriate and clear How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Neither clearly nor unclearly Is the preprint likely to advance academic knowledge? Moderately likely Would it benefit from language editing? No Would you recommend this preprint to others? Yes, but it needs to be improved Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.
Yuchen Song· Zenodo (CERN European Organi...· 0 citations
Purpose - Examining how the interaction between Artificial Intelligence (AI) capabilities and organizational leadership dynamics can strengthen business strategic decision-making processes.
Design/methodology/approach – A Systematic Literature Review (SLR) methodology following the PRISMA protocol was employed. Data were screened from the Scopus database up to July 2026, resulting in a final cohort of 30 English-language scholarly journal articles for qualitative analysis.
Originality - Introducing the concept of "Algorithmic Bounded Rationality" to explain AI limitations (such as data bias, contextual rigidity, and factual inaccuracies) and developing a "Triple Helix" model that emphasizes the importance of synergy between machine precision and human cognitive curation (human-in-the-loop governance).
Findings and Discussion – The effectiveness of AI is not technologically deterministic; AI capabilities—ranging from predictive analytics to generative models—require organizational mediators such as ethical leadership, a digital mindset, and strategic ambidexterity. AI acts as a cognitive prosthesis that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders. Most literature focuses on quantitative or conceptual approaches, while in-depth, case-study-based qualitative research remains very limited.
Conclusion – AI does not replace the role of executive leaders; instead, it serves as a cognitive aid. Organizations that successfully integrate AI's computational precision with ethical and responsive human leadership achieve the greatest competitive advantage.
Keywords – Artificial Intelligence, Leadership Decision Making, Dynamic Managerial Capabilities, Algorithmic Bounded Rationality, Human AI Complementarity
Alifah Widya Rachmawati, Syamsul Hadi, Eni Purnasari et al.· INTERNATIONAL JOURNAL OF ECO...· 0 citations
Generative artificial intelligence (GAI) is becoming more incorporated into software engineering functions like code creation, debugging, requirement analysis, testing, and sharing knowledge. This research looks at how GAI affects software teams in terms of productivity and quality of the output in Agile environments. The research design used is quantitative, cross-sectional survey type using a questionnaire prepared for this research. The data used consists of 35 responses, with 34 usable cases in analyzing 30 Likert items. The measuring instrument consists of six concepts: use of GAI, efficiency of the software team, quality of the software output, GAI in Agile, team collaboration, and communication, and overall impact perceived. The descriptive results show positive feelings about the six concepts. The values on the mean for the different concepts varied from 3.54 to 3.78 on a scale of five, with GAI being the concept that received the highest mean (M = 3.78, SD = 0.47) while productivity was the one that received the lowest (M = 3.54, SD = 0.69). The instrument has a high level of internal consistency with α = 0.799 for the entire scale of 30 items. In terms of specific items, productivity, quality, and team collaboration had acceptable reliability, whereas GAI had low internal consistency and Agile and general have the upper limit of reliability therefore, construct-level findings should be interpreted cautiously. Pearson correlation analysis showed statistically significant positive associations between overall perceived impact and software output quality (r = 0.365, p = 0.034) and team collaboration and communication (r = 0.371, p = 0.031). Productivity was positively associated with overall impact but did not reach the conventional 0.05 significance level (r = 0.312, p = 0.073). In a multiple regression model, the five dimensions explained 23.5% of the variance in overall perceived impact (R² = 0.235); however, the overall model was not statistically significant (F(5, 28) = 1.719, p = 0.163). These findings support a cautious interpretation: respondents generally perceive GAI positively, but the present small sample does not provide strong evidence for broad causal claims.
ABDALMENAM KHALIF MASAUD ABUSWAH, ABDARRAHMAN KHALIF ALI ABOUSOWA, ZIAD OMAR SALEM WAREG· Al-Farooq Journal of Science...· 0 citations
Metal organic frameworks (MOFs) have emerged as promising electrode materials for supercapacitor (SC) due to their high surface areas, tunable porosity, and redox active sites. However, the vast chemical space of MOFs leads to millions of possible structures, makes experimental trial and error discovery inefficient. This review provides a focused perspective on how density functional theory (DFT) and machine learning (ML) are enabling the accelerated discovery and rational design of MOF-based SC electrodes. Key insights from DFT are discussed in relation to three critical performance descriptors as electrical conductivity, electrochemical and structural stability, and redox activity. In parallel, recent advances in ML-driven screening are reviewed, covering the development and use of large-scale MOF databases, descriptor engineering strategies, and predictive model architectures. Case studies demonstrating the successful integration of DFT and ML for identifying high-performance MOFs are highlighted in the review. Finally, the current limitations are analysed, including the discrepancy between idealized computational models and real polycrystalline electrodes, intrinsic trade-offs between conductivity and stability, and the need for interpretable and physics-informed ML models. Overall, this review outlines a computational roadmap for the rapid discovery and optimization of next-generation MOF-based SC electrodes. This is the comprehensive review on ML-driven prediction of electrochemical performance in MOF based electrode for SC applications. It integrates DFT-calculated electronic descriptors with ML models to discover hidden structure-property relationships. It identifies critical data gaps, model transferability issues, lack of dynamic ion-transport modelling in current studies. It proposed a multi-fidelity active learning framework combining DFT, ML and experiments for accelerated MOF discovery. It outlines standardized database protocols and explainable AI strategies to guide future high-performance MOF design.
Achal Siddharth Fulmali, H. Panda· Journal of Materials Science...· 0 citations
To improve integration, stakeholders should begin any new POCUS AI development project by first examining the different domains where POCUS AI applications are most needed, including education, clinical practice, workflow, research, and administration.
Robinson M. Ferre, Rachel Liu, HF Samuel Lam et al.· Journal of the American Coll...· 0 citations
This survey explores recent innovations in IoT-based Water Quality Monitoring Systems (IoT-WQMS) integrated with Machine Learning (ML) and Deep Learning (DL) to enable real-time, automated water quality assessment.
Kumar S. Ashok, Radhakrishnan C. V.· International Journal of Sci...· 0 citations
The experimental results show that the proposed Explainable AI-Based Deep Learning System for prediction of customer churn in telecommunication industry has high prediction accuracy, reliability and interpretability, and thus it is a valuable decision-support tool for telecom organizations that aim to reduce customer attrition and improve retention strategies.
K. Teja, M. Arathi· International Journal for Re...· 0 citations
