The main objective of this paper is to discuss different approaches, identify challenges, and to select a framework for delivering effective sustainability assessments. Sustainable development is an idealistic concept and its assessment has always been a challenge. Several approaches, methodologies and conceptual frameworks have been developed in various disciplines, ranging from engineering to business and to policy making. The paper focuses mainly on various linkage-based frameworks and demonstrates that the driving force-state-exposure-effect-action (DPSEEA) framework can be used to achieve sustained health benefits and environmental protection in accordance with the principles of sustainable development, especially because of its resemblance to the environmental risk assessment and management paradigms. The comparison of linkage-based frameworks is demonstrated through an example of sustainability in a higher educational institution.

Adaptive instructional systems (AISs) refer to educational interventions designed to accommodate individual learner differences. These systems employ various approaches, such as artificial intelligence (AI), machine learning (ML), and data analytics, to analyze student performance and personalize the learning experience. This article presents a review of the current state-of-the-art of AI methods used in the development of AISs for maritime safety training. The main objective of this systematic literature review is to determine the use of AI/ML techniques in AIS and how they can contribute to the development of AIS for maritime education and training (MET) applications in addressing small data problems. Answering the research questions of the review identifies the fundamental purposes of using AI/ML techniques in developing AIS for MET. Further, the review highlights several crucial research areas, including AI techniques for modelling student and instructor knowledge, as well as ML algorithms for predicting student performance in situations with limited datasets.

The International Code for Ships Operating in Polar Waters (Polar Code) was adopted by the International Maritime Organization (IMO) and entered into force on 1 January 2017. It provides a comprehensive treatment of topics relevant to ships operating in Polar regions. From a design perspective, in scenarios where ice exposure and the consequences of ice-induced damage are the same, it is rational to require the same ice class and structural performance for such vessels. Design requirements for different ice class vessels are provided in the Polar Code. The Polar Operational Limit Assessment Risk Indexing System (POLARIS) methodology provided in the Polar Code offers valuable guidance regarding operational limits for ice class vessels in different ice conditions. POLARIS has been shown to well reflect structural risk, and serves as a valuable decision support tool for operations and route planning. At the same time, the current POLARIS methodology does not directly account for the potential consequences resulting from a vessel incurring ice-induced damage. While two vessels of the same ice class operating in the same ice conditions would have similar structural risk profiles, the overall risk profile of each vessel will depend on the magnitude of consequences, should an incident or accident occur. In this paper, a new framework is presented that augments the current POLARIS methodology to model consequences. It has been developed on the premise that vessels of a given class with higher potential life-safety, environmental, or socio-economic consequences should be operated more conservatively. The framework supports voyage planning and real-time operational decision making through assignment of operational criteria based on the likelihood of ice-induced damage and the potential consequences. The objective of this framework is to enhance the safety of passengers and crews and the protection of the Arctic environment and its stakeholders. The challenges associated with establishing risk perspectives and evaluating consequences for Arctic ship operations are discussed. This methodology proposes a pragmatic pathway to link ongoing scientific research with risk-based methods to help inform recommended practices and decision support tools. Example scenarios are considered to illustrate the flexibility of the methodology in accounting for varied risk profiles for different vessel types, as well as incorporating input from local communities and risk and environmental impact assessments.

The digital transformation of the offshore and maritime industries will present new safety challenges due to the rapid change in technology and underlying gaps in domain knowledge, substantially affecting maritime operations. To help anticipate and address issues that may arise in the move to autonomous maritime operations, this research applies a human-centered approach to developing decision support technology, specifically in the context of ice management operations. New technologies, such as training simulators and onboard decision support systems, present opportunities to close the gaps in competence and proficiency. Training simulators, for example, are useful platforms as human behaviour laboratories to capture expert knowledge and test training interventions. The information gathered from simulators can be integrated into a decision support system to provide seafarers with onboard guidance in real time. The purpose of this research is two-fold: (1) to capture knowledge held by expert seafarers, and (2) transform this expert knowledge into a database for the development of a decision support technology. This paper demonstrates the use of semi-structured interviews and bridge simulator exercises as a means to capture seafarer experience and best operating practices for offshore ice management. A case-based reasoning (CBR) model is used to translate the results of the knowledge capture exercises into an early-stage ice management decision support system. This paper will describe the methods used and insights gained from translating the interview data and expert performance from the bridge simulator into a case base that can be referenced by the CBR model.