AUIQ Humanities and Social Sciences

Purpose: This study examines the limited integration of behavioural theory—specifically the Myers–Briggs Type Indicator (MBTI)—in the design of e-learning systems. MBTI is considered because personality variation influences learner motivation, engagement, and behavioural responses, yet most digital platforms rely on uniform instructional strategies. The study focuses on higher-education e-learning, where behavioural disengagement and psychological strain are increasingly reported.

Design/methodology/approach: A qualitative conceptual content-analysis approach is used to synthesise secondary evidence from peer-reviewed literature, policy documents, and institutional reports (2015–2024). The analysis identifies three theoretical categories—behavioural reinforcement, personality-based differentiation, and adaptive learning interaction—which shape the proposed behavioural–personality framework.

Findings: Three core patterns emerged: (1) behavioural reinforcement is inconsistently embedded within e-learning systems; (2) personality differences affect motivation, cognitive load, and persistence but remain insufficiently addressed; and (3) aligning reinforcement mechanisms with MBTI preferences can enhance self-regulation, emotional stability, and engagement. These insights support the need for a unified behavioural–personality model for digital pedagogy.

Practical implications: The study offers actionable guidance for universities, instructional designers, e-learning developers, and higher-education policymakers seeking to personalise learning pathways and strengthen student–teacher interaction in virtual learning environments.

Social implications: Embedding behavioural theory in e-learning promotes equity, digital inclusion, and psychological well-being by recognising diverse learner profiles.

Limitations: As a conceptual synthesis, the study excludes primary data and non-English sources; empirical testing across institutional and cultural contexts is recommended.

Originality/value: The paper proposes a unified behavioural–personality framework linking reinforcement theory with MBTI profiling to support adaptive, human-centred e-learning design.

Macroeconomic forecasting remains difficult because aggregate dynamics are nonlinear, regime-dependent, and increasingly informed by high-dimensional, mixed-frequency data, conditions under which traditional econometric models often lose accuracy, especially around turning points. This study systematically reviews how artificial intelligence (AI) is being used to address these challenges and what reliable evidence exists on its forecasting value. Using a PRISMA-aligned protocol, we searched Web of Science, Scopus, and ScienceDirect, identifying 1,627 records; after de-duplication and multi-stage screening, 178 studies were retained for qualitative synthesis. The review develops an evidence-based taxonomy of AI model families in macroeconomic forecasting and synthesizes their motivations, performance patterns, and limitations across targets such as gross domestic product (GDP) growth, inflation, and labor-market indicators. This study shows that AI methods, particularly tree-based ensembles, deep sequence models, and hybrid econometric–AI systems, frequently improve short-horizon forecasting and nowcasting in data-rich settings and during crisis regimes, but do not dominate uniformly across variables or horizons. The study's contributions are threefold: a structured taxonomy that organizes a fragmented field, a consolidated assessment of where AI gains are robust versus conditional, and a clear articulation of the main methodological and policy-relevant constraints shaping future macro-AI forecasting research

This study examines how green core competence contributes to competitive advantage in Iraqi firms, and how this relationship depends on both organizational and governance conditions. Drawing on the Resource-Based View, the Job Demands–Resources model, and stakeholder theory, the study tests a mediation–moderation model in which positive mental health mediates the effect of green core competence on competitive advantage, while trust in government and top management support strengthen key links in the process. Using survey data from top-management respondents in Iraq and partial least squares structural equation modeling, the results show that green core competence is positively associated with competitive advantage, and that positive mental health partially mediates this effect. The findings further indicate that greater trust in government and stronger top management support amplify the benefits of green competence by reinforcing the capability-to-well-being and well-being-to-advantage links. Overall, the study extends sustainability and management research by demonstrating that the competitive returns to green capability are shaped not only by firm resources but also by human well-being and the credibility of the institutional environment.

Civil liability for damages arising from the use of artificial intelligence in the practice of robotic medicine is considered a modern topic of significant importance across various countries worldwide, particularly in the United Arab Emirates, which is well known for its progress in all sectors, especially the healthcare sector. The adoption of artificial intelligence in this field aims to employ advanced technologies to enhance the quality of medical services and provide sophisticated healthcare to the community. The importance of this study lies in highlighting the role of artificial intelligence in the practice of robotic medicine, with a focus on the position of UAE legislation regarding this use, including the Federal Medical Liability Law No. (4) of 2016 and its Executive Regulations No. (40) of 2019. The study also reviews the report of the Legal Affairs Committee of the European Union concerning the European resolution on civil law rules related to robotics issued on 16 February 2017.The study further examines the legal nature of artificial intelligence, the different AI systems, their applications and entities, as well as the fundamental elements of civil liability arising from the actions of robots, namely fault, damage, and the causal relationship between the physician’s error and the harm suffered by the patient. Finally, the study analyzes the legal consequences resulting from the establishment of robot liability, including judicial compensation and contractual compensation.The findings indicate that healthcare authorities in the UAE have achieved notable success in implementing artificial intelligence technologies. The Ministry of Health and Prevention relies on these technologies in X-ray devices within medical examination procedures for residency, while the Dubai Health Authority has achieved remarkable progress in employing artificial intelligence across several medical fields.

Diagnosis of heart disease is a procedure that requires fast, accurate predictions and conventional fixed machine learning (ML) pipelines may not fully accommodate. Although models, such as Logistic Regression and Naïve Bayes are effective on structured clinical data however they do not have self-assessment or adaptive reasoning mechanisms. In this work, we present an agentic AI framework that turns the classical models into self-evaluating diagnostic agents managed by a performance conscious supervisory component. We trained and tested the system on the UCI Heart disease dataset over three different splits (70:30, 80:20, 85:15) to test its diagnostic stability with respect to data availability. The Logistic Regression model had the best overall (up to 85% accuracy) and F1-score performance across the experiments, while Naïve Bayes outperformed in recall for heart disease cases, indicating its beneficial use in sensitivity- oriented tasks. In each situation the supervisory agent automatically selected the superior model, demonstrating that agents can coordinate with one another even at relatively low levels of complexity. This work mediates interpretability with accountability by the construction classical models in a simple self-contained framework for a reliable transparent diagnosis without any additional computational effort. Conclusion: Agentic supervision may be the key to taking classical algorithms and developing them into scalable intelligent decision support tools for real-world cardiovascular care.