The role of over-reliance on AI in the negative consequences of student learning: The moderating effects of ethical concerns and institutional policies

Related research

1. Journal article2026Published PDF

   Self-efficacy and instructor support as determinants of ChatGPT effectiveness in student learning

   Abdulkadir Jeilani Mohamud, Mohamed Abdullahi Nor, Mohamed Sahane, Abdirahman Mohamed Dirie — Discover Artificial Intelligence, Volume 6, Article 529, 2026

   The rapid integration of generative artificial intelligence (AI) tools, such as ChatGPT, is reshaping learning experiences in higher education. However, the psychological and instructional conditions under which these tools enhance academic success remain insufficiently understood, particularly in low resource contexts. Using survey data from 286 university students in Somalia, this study investigates how ChatGPT usage influences students’ academic performance and learning outcomes, with particular attention to the mediating roles of academic self-efficacy and cognitive engagement (mental effort), and the moderating role of instructor support. Drawing on the Technology Acceptance Model, Social Cognitive Theory, and Cognitive Load Theory, eight hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that ChatGPT usage significantly enhances learning outcomes, self-efficacy, and cognitive engagement; however, its direct effect on academic performance is not statistically significant, suggesting that performance gains do not occur automatically from AI use. Mediation analysis reveals that self-efficacy significantly mediates the relationship between ChatGPT usage and academic performance, whereas cognitive engagement does not. Furthermore, instructor support significantly moderates the relationship between cognitive engagement and academic performance, strengthening the translation of mental effort into achievement. Overall, the findings highlight the critical role of psychological empowerment and instructional support in unlocking the educational benefits of generative AI, particularly in resource constrained higher education contexts.

   0CitePDFDOILink 26 reads
2. Journal article2026Published PDF

   Trustworthy AI Governance Framework for Autonomous and AI-Driven Networked Systems

   Abdinasir Ismael Hashi, Abdirizak Mohamed Hashi, Osman Abdullahi Jama — International Journal of Computer Trends and Technology, 74(1), 12–27, 2026

   The rapid expansion of autonomous and AI-driven networked systems across smart cities, transportation, healthcare, and cyber–physical infrastructures has intensified concerns related to trust, safety, transparency, and regulatory compliance. Existing approaches often address governance, ethics, and technical assurance in isolation, leaving a gap between high-level principles and practical system implementation. To address this challenge, this study proposes a comprehensive trustworthy AI governance framework that integrates system-theoretic modeling with governance constrained decision-making. The framework models autonomous systems as distributed cyber–physical–social systems and embeds ethical, safety, and legal constraints directly into the learning and optimization process using constrained Markov decision processes and Lagrangian optimization. A composite trustworthiness metric is formulated by aggregating Fairness, Robustness, Privacy, Explainability, Security, and Accountability dimensions. The methodology combines analytical modeling with simulated datasets representing multi-agent autonomous networks. Experimental results demonstrate a composite trust score of approximately 0.813, explainability stability of 0.905, and near-zero governance violations, confirming improved compliance, reduced risk, and enhanced trust compared to unguided AI systems.

   0CitePDFDOILink 27 reads
3. Journal article2025Published PDF

   Evaluating the Performance of AI-Based Software Tools in Intelligent Decision-Making Systems

   Abdinasir Ismael Hashi, Osman Abdullahi Jama — International Journal of Computing and Engineering, 7(22), 1–20, 2025

   This paper evaluates the performance of AI-based software tools within intelligent decision-making systems, emphasizing their application in Industry 4.0 environments. Various AI techniques, including machine learning, deep learning, and natural language processing—are assessed across domains such as predictive maintenance, quality control, supply chain optimization, and energy management. To advance this field, we introduce a novel framework, RAISE-DM (Real-time Adaptive Intelligence Software Evaluation for Decision-Making), which combines real-time data acquisition from IoT devices with adaptive AI models for continuous decision optimization. Performance evaluation considers key parameters such as scalability, response time, accuracy, and interpretability. The study also highlights critical technical barriers like data heterogeneity and integration complexity, offering targeted strategies to address them. By providing a structured performance analysis and proposing a scalable evaluation model, this research contributes to the design of more efficient, transparent, and resilient AI-driven decision support systems applicable across industrial and cross-sector settings.

   0CitePDFDOILink 23 reads