Jul 2026· Georgian Maritime Scientific Journal· Vol 3, pp. 41-51· 0 citations· 15 references
TL;DR
It is argued that AI should function as a complementary reasoning partner, improving outcomes without replacing human judgment in critical MSAR operations, though at the cost of greater cognitive effort.
Abstract
This paper examines how Generative AI and Large Language Models (LLMs) can support decision-making in Maritime Search and Rescue (MSAR) operations through a human-centered collaborative framework. The authors propose a theoretical Human-AI Deliberative Framework (HADF) based on the ExtendAI approach, into Maritime Rescue Coordination Centers. In this framework, the human operator first develops and explains an operational plan, after which the AI extends the reasoning by providing structured feedback, identifying cognitive gaps, and supporting reflection while keeping the final decision under human control. The model combines LLM interaction, optimization methods, and supporting datasets to enhance planning quality. Reported benefits include increased decision confidence and satisfaction, as well as improved reasoning depth, though at the cost of greater cognitive effort (the results are drawn from the main study Reicherts et al., not from primary data). Overall, the study argues that AI should function as a complementary reasoning partner, improving outcomes without replacing human judgment in critical MSAR operations.
This paper explores the integration of Generative AI (Gen AI) and Explainable AI (XAI) into Predictive Maintenance (PdM) within the Shipping 4.0 framework for educational use. The authors propose a conceptual framework called IPMDP (Intelligent Predictive Maintenance Decision Process for Education), which involves an architectural approach (System-of-System, SoS) for integrating LLMs (Gen AI approach) into the predictive maintenance of marine engines and systems, in order to support decision-making in the maintenance of systems at sea. The central idea of the conceptual framework lies in the utilization of a decision-making model using Gen AI techniques through the ExtendAI framework, which functions as a “cognitive partner” to a ship’s engine room crew. The ultimate goal is the educational use of such a framework in the training of Merchant Marine engineers and other ship engine room personnel. Essentially, this study is a conceptual framework paper, integrating existing models and literature to propose a novel application domain, rather than presenting original empirical findings. This cognitive maintenance approach improves operational safety and ensures human-centric, accountable decision-making, which is crucial for regulatory compliance and effective adoption in the maritime industry.
N. Tsigkris, E. Sklavoúnou, D. Tseles et al.· Georgian Maritime Scientific...· 0 citations
The integration of Artificial Intelligence (AI) as a collaborative partner is transforming the future of work. Rather than replacing human labor, modern AI systems enhance human capabilities through cognitive augmentation, adaptive workflows, and cooperative problem-solving. This paper presents a multidisciplinary analysis of human–AI collaboration across sectors such as healthcare, engineering, finance, education, and creative industries. A conceptual framework is proposed for dynamic task allocation between humans and AI based on uncertainty, contextual reasoning, and interpretability requirements. The study also examines socio-technical challenges including trust, ethical alignment, skill transformation, and organizational resilience. Using a domain-agnostic evaluation approach, collaboration effectiveness is measured through metrics such as cognitive load distribution, error reduction, adaptability, and explainability. The findings indicate that hybrid intelligence systems outperform both purely human and fully automated systems in complex and uncertain environments. The study concludes that the future of work will depend on co-evolutionary human-AI collaboration, requiring organizational restructuring, policy development, and ethical safeguards to ensure sustainable productivity and innovation.
V. Sethi· International Journal of Eme...· 0 citations
Sports analytics platforms have come a long way in their ability to handle data themselves and their performance in visualization, but lags remain when it comes to human-centric decision support, explainability, and contextual reasoning. This study aims to map out the Contextual Decision Intelligence for Sports Analytics (CoDI-SA) framework to a feasible reference architecture, based on the Design Science Research (DSR) method. The proposed architecture includes multi-modal data fusion, contextual reasoning, explainable artificial intelligence (XAI), and human-in-the-loop decision intelligence, which will allow for intuitive, context-aware and actionable suggestions to coaches, analysts and performance teams while keeping transparency. The proposed architecture is adaptive in nature and can be implemented on elite level, semi-professional level or grassroots level, all while having a common architectural foundation and is an alternative to the existing commercial systems which emphasize descriptive analytics. The usefulness and trustworthiness of the architecture as well as the value of the architecture is assessed by comparing it to commercial sports analytics platforms and by surveying twelve sports-technology practitioners with a grand mean of 4.26/5 and SD of 0.69 for the three questions, where the mean represents the average of the practitioners' responses. This study offers a reference architecture that can be reused, and seven design principles and a tiered deployment model of next generation sports analytics systems.
S. Sangle· 2026 International Conferenc...· 0 citations
Future human missions to Mars will place astronauts in a world that is scientifically rich but
physically unforgiving. The Martian surface has a thin atmosphere, extreme temperature swings, dust
activity, radiation exposure, delayed communication with Earth, limited resupply, and full dependence
on engineered life-support systems. These conditions make Mars exploration a safety-critical, distributed
teamwork problem rather than a simple task-planning problem. This article develops a conceptual
framework for human-artificial intelligence (AI) teamwork in Mars surface exploration. No detailed
mathematical model is proposed, no AI system is trained, no operational performance is claimed, and
no crewed test has been performed. Instead, the contribution is a research-grounded engineering concept
organized around Figure 1, in which mission readiness, in-mission support, human review, spacecraft
and habitat architecture, and data-to-value feedback form a closed safety loop. The framework argues
that AI should not replace astronauts or mission-control teams; rather, AI should act as a safety translator
that turns environmental data, system telemetry, robot reports, and science priorities into explainable
options for human approval. The proposed concept adds engineering soundness by mapping Mars
hazards to decision-support functions, defining human-authority requirements, identifying safety
and planetary-protection guardrails, and laying out a staged validation pathway from concept review
to tabletop exercises, analog missions, digital twins, and eventually certified operational systems.
The central message is that the smartest path to Mars is neither full automation nor unaided human
courage, but disciplined human-AI-robot teamwork that helps explorers remain safe, aware, ethical, and
scientifically productive.
Junyao Zhang, Hongsheng Shang· American Journal of Student...· 0 citations
Artificial Intelligence (AI) enables powerful capabilities that are transforming almost all sectors. However, the economic growth driven by AI comes at a cost, and its sociotechnical impacts are fraught with contradictions and paradoxes. As a result, several legal initiatives and risk management frameworks have been introduced to mitigate the various risks associated with AI systems. Agentic AI systems require even closer attention than traditional AI. While traditional AI has a narrow focus and responds to direct commands, Agentic AI emerges from combining multiple types of AI capable of planning, tool use, and multi-step execution. These systems can behave and interact autonomously, making decisions and performing tasks to achieve system objectives with minimal human oversight. Recognizing that Agentic AI represents a paradigm shift, this paper addresses its challenges from a Human-AI Interaction perspective. It examines the root causes and impacts of risks arising from the transition from Task Automation to Intentionality Automation, where the user manages outcomes and constraints rather than individual task steps. Key issues include the Open-Loop Control Gap and the Metacognitive Gap, whose relationship is fundamental to understanding the collapse of human oversight, as they represent two sides of the same coin in the loss of control. By analysing scenarios such as cybersecurity and healthcare, this paper identifies dimensions of user demand and identifies Ecological Interface Design as an ergonomic approach to ensure that as AI gains agency, the human retains authority and situational awareness.
M. Simões-Marques· AHFE International· 0 citations
Human–AI teaming is rapidly emerging as a defining paradigm in next-generation aviation operations, reshaping pilot roles, altering cockpit task distribution, and challenging established assumptions regarding expertise, decision-making, and training. As artificial intelligence systems evolve from deterministic support tools into adaptive, autonomous teammates capable of perception, prediction, and intent-driven action, the aviation training ecosystem faces a suite of unprecedented challenges. These challenges extend beyond purely technical skills and encompass deeper questions of trust calibration, cognitive adaptation, workload redistribution, ethical responsibility, and sustained human performance. This paper examines the central training challenges associated with preparing pilots, instructors, and organisational systems for effective human–AI teaming across current and expected future aviation environments.First, the paper analyses the shifting cognitive and operational landscape introduced by AI-enabled systems, including adaptive automation, predictive analytics, natural-language interfaces, and mixed-initiative control architectures. Whilst these technologies promise enhanced situational awareness, reduced workload, and strengthened predictive safety nets, they simultaneously introduce risks such as automation complacency, algorithmic over-reliance, erosion of manual competencies, and emergent forms of mode confusion. Training organisations must therefore rethink curriculum design to cultivate appropriate levels of trust in AI agents while strengthening pilots’ abilities to monitor, interrogate, and, where necessary, override AI behaviour during uncertainty or system drift. Traditional training paradigms based on linear automation logic are insufficient to address the probabilistic and at times opaque behaviour of modern AI systems.Second, the paper explores the pedagogical complexities inherent in developing joint human–AI decision-making skills. Effective teaming requires robust communication transparency, alignment of mental models, and the formation of shared situational awareness between human operators and algorithmic agents. Yet many AI systems operate as “opaque teammates,” offering outputs without interpretive depth or explainable reasoning. Training must therefore introduce strategies for evaluating machine-generated recommendations, identifying algorithmic bias, integrating AI insights with experiential human judgement, and managing discrepancies between human and AI interpretations. Scenario-based training, explainable AI (XAI) tools, and structured failure-mode exploration are presented as essential approaches for mitigating these challenges.Third, organisational, regulatory, and standardisation constraints are evaluated. The absence of harmonised human–AI competency frameworks, variability in AI system behaviour across aircraft types, and ambiguities regarding accountability pose obstacles for both initial and recurrent training. A critical need exists for evidence-based human factors methodologies that define the skills required for pilots operating in mixed-initiative or partially autonomous environments. Emerging competency-based training and assessment (CBTA/EBT) methodologies offer a promising foundation but require expansion to incorporate AI teaming competencies, error management strategies, and resilience-building mechanisms.The paper argues that training for human–AI teaming must remain fundamentally human-centric, preserving pilots’ adaptive expertise, situational awareness, and critical thinking while ensuring that AI systems remain compatible with human cognitive strengths and limitations. It concludes by proposing an integrated training model to support safe, resilient, and ethically aligned human–AI cooperation in future aviation operations.
Dimitrios Ziakkas, Ibrahim Sarikaya, Debra Henneberry· AHFE International· 0 citations
