aceBeyond Generative Intelligence: A Comprehensive Review of Emerging Artificial Intelligence Paradigms, Explainability Challenges, Ethical Risks, and Future Directions
The artificial intelligence landscape has undergone a profound transformation from narrow, task-specific automation to sophisticated, multi-paradigm systems capable of autonomous reasoning, emotional understanding, and creative generation. This systematic literature review synthesizes 141 peer-reviewed studies published between 2018 and 2026 to map the evolution of AI paradigms beyond the dominant Generative AI breakthrough. Following PRISMA guidelines, we analyzed 4,250 initial records across six major academic…
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By July 2026, this systematic review synthesized 141 studies to map seven emerging paradigms beyond generative AI, including Emotional and Empathetic AI, Social AI, Agentic AI, Multimodal AI, Explainable AI, and Responsible AI, documenting a shift from purely technical research to socio-technical integration.
The persistence of the black box problem matters because it limits trust and deployment in healthcare, finance, and criminal justice despite SHAP, LIME, and attention visualization, while dark AI threats increase pressure for international governance; uncertainty remains because the proposed six-layer ecosystem framework is conceptual and lacks stakeholder validation and empirical testing.
- Systematic review of 141 peer-reviewed studies from 2018 to 2026 following PRISMA guidelines from 4,250 initial records
- Bibliometric analysis identified five thematic clusters: Intelligence and Learning, Generative Ecosystems, Human-Centric AI, Governance and Trust, and Autonomous Systems
- XAI techniques cited include SHAP, LIME, and attention visualization as partial responses to explainability challenges
AI systems exhibit a persistent black box problem that blocks trust and adoption in high-stakes domains including healthcare, finance, and criminal justice, while dark AI uses like deepfakes and AI-powered cyberattacks create governance risks.
The rundown
The review screened 4,250 records across six major academic databases and included 141 studies, using a modified Mixed Methods Appraisal Tool with ICC = 0.87, 95% CI: 0.83-0.91, and a three-phase thematic synthesis yielding 47 first-order codes and 18 second-order themes.
Authors propose an Integrated AI Ecosystem Framework with six interdependent layers: Intelligence, Creation, Human Interaction, Autonomous, Governance, and Security, with Trustworthy AI as integrating principle, positioned against EU High-Level Expert Group Trustworthy AI, NIST AI Risk Management Framework, and IEEE Ethically Aligned Design.
Sources
- Peer-reviewedInternational Journal of Latest Technology in Engineering Management & Applied Science2026-07-11
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