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Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.

Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.

The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.

This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.

This paper describes a computer supported ubiquitous learning environment for minority language learning. Hakka language involved various tones, art, craft, festivals, food, drink, medicine, religion and custom in different regions. The tones also vary across the dialects of Hakka. The majority of Hakka dialects have six tones in Taiwan. The context for Hakka language translation and use is extremely important. The purpose of this paper is to present the context-aware annotation service to help readers get the right information for Hakka language use and learning more easily.

This paper proposes the context-aware learning support system for Hakka culture-specific items or language, which is called Hakka culture-specific items annotation system (HACSIAS). HACSIAS is the context-aware annotation system, helping learners to add links of culture-specific items when they are reading the electronic journals, books, and web pages.

The HACSIAS provides learner appropriate information for Hakka culture-specific items deriving the learner’s situation and personal information. The context-aware computing will automatically provide translation service by searching a given culture-specific items database while these items or terminology appear in a document, either by displaying terms in the translation memory.

The context-aware annotation system for Hakka culture-specific language learning allows Hakka cultural resources to be smoothly integrated into learning materials.

This survey aims to study and analyze current techniques and methods for context‐aware web service systems, to discuss future trends and propose further steps on making web services systems context‐aware.

The paper analyzes and compares existing context‐aware web service‐based systems based on techniques they support, such as context information modeling, context sensing, distribution, security and privacy, and adaptation techniques. Existing systems are also examined in terms of application domains, system type, mobility support, multi‐organization support and level of web services implementation.

Supporting context‐aware web service‐based systems is increasing. It is hard to find a truly context‐aware web service‐based system that is interoperable and secure, and operates on multi‐organizational environments. Various issues, such as distributed context management, context‐aware service modeling and engineering, context reasoning and quality of context, security and privacy issues have not been well addressed.

The number of systems analyzed is limited. Furthermore, the survey is based on published papers. Therefore, up‐to‐date information and development might not be taken into account.

Existing surveys do not focus on context‐awareness techniques for web services. This paper helps to understand the state of the art in context‐aware techniques for web services that can be employed in the future of services which is built around, amongst others, mobile devices, web services, and pervasive environments.

Pervasive computing environments such as a pervasive campus domain, shopping, etc. will become commonplaces in the near future. The key to enhance these system environments with services relies on the ability to effectively model and represent contextual information, as well as spontaneity in downloading and executing the service interface on a mobile device. The system needs to provide an infrastructure that handles the interaction between a client device that requests a service and a server which responds to the client's request via Web service calls. The system should relieve end‐users from low‐level tasks of matching services with locations or other context information. The mobile users do not need to know or have any knowledge of where the service resides, how to call a service, what the service API detail is and how to execute a service once downloaded. All these low‐level tasks can be handled implicitly by a system. The aim of this paper is to investigate the notion of context‐aware regulated services, and how they should be designed, and implemented.

The paper presents a detailed design, and prototype implementation of the system, called mobile hanging services (MHS), that provides the ability to execute mobile code (service application) on demand and control entities' behaviours in accessing services in pervasive computing environments. Extensive evaluation of this prototype is also provided.

The framework presented in this paper enables a novel contextual services infrastructure that allows services to be described at a high level of abstraction and to be regulated by contextual policies. This contextual policy governs the visibility and execution of contextual services in the environment. In addition, a range of contextual services is developed to illustrate different types of services used in the framework.

The main contribution of this paper is a high‐level model of a system for context‐aware regulated services, which consists of environments (domains and spaces), contextual software components, entities and computing devices.

Context awareness is one of the key aspects of pervasive computing systems. In such systems, a plethora of dynamic context information needs to be constantly retrieved, soundly interpreted, rapidly processed, maintained in various repositories, and securely disseminated. Thus, a flexible, scalable and interoperable context representation scheme needs to be established and solid context management mechanisms need to be adopted, which will perform well in large‐scale distributed pervasive systems. This paper elaborates on the COMPACT context middleware that has been designed to cope with the issues above and saturate pervasive computing environments with context awareness functionality.

This study seeks to examine the concepts of context, context‐awareness, and context‐awareness technology needed for applying context‐awareness technology to the next‐generation of digital libraries, and proposed context‐aware services that can be applied to any situation by illustrating some library contexts.

The paper investigated both theoretical research and case analysis studies before suggesting a service model for context‐awareness‐based libraries by examining the context, context‐awareness, and context‐awareness technology in depth.

This paper derived possible library services which could be provided if context‐awareness services are implemented by examining and analyzing case studies and systems constructed in other fields. A library‐applied context‐aware system could recognize users entering the library and provide optimal services tailored to each situation for both new and existing users. In addition, the context‐awareness‐based library could provide context‐awareness‐based reference services, context‐awareness‐based loan services, and cater to other user needs in the stacks, research space, and a variety of other information spaces. The context‐awareness‐based library could also recognize users in need of emergency assistance by detecting the user's behavior, movement path, and temperature, etc. Comfort or climate‐control services could provide the user with control of the temperature, humidity, illumination and other environmental elements to fit the circumstances of users, books, and instruments through context‐aware technology.

Next‐generation digital libraries apply new concepts such as semantic retrieval, real‐time web, cloud computing, mobile web, linked data, and context‐awareness. Context‐awareness‐based libraries can provide applied context‐awareness access service, reactive space according to the user's access, applied context‐awareness lobbies, applied context‐awareness reference services, and applied context‐awareness safety services, context‐awareness‐based comfort services and so on.

Real instances of libraries applying context‐aware technology are few, according to the investigative results of this study. The study finds that the next‐generation digital library using context‐awareness technology can provide the best possible service for the convenience of its users.

Context‐awareness is an essential property of any pervasive system perceiving its environment. Such a system captures and processes context, i.e. the features describing the relevant aspects of environment state and user behaviour. However, development of these systems still requires solving a number of research and engineering challenges. The purpose of this paper is to propose perception framework, a RESTful middleware which simplifies and accelerates the development of pervasive systems. Perception framework allows constructing services' application logic using rules and context. Moreover, it collects sensor data and produces the context information that is required for the rules. The authors present the architecture, design, complete implementation, and prototype‐based verification of perception framework.

Development of context‐aware services is achieved with a novel architecture supporting building of the logic of web services using rules which directly manipulate the available elementary context represented with the Web Ontology Language (OWL) ontology. These rules are described using the Rule Interchange Format (RIF) with support for different rule languages. The implementation of this framework is aligned with RESTful principles, providing a lightweight and flexible solution for large‐scale context‐aware systems.

The fully implemented prototype verifies the feasibility of constructing the logic of context‐aware web services with the rules supported by perception framework.

The contributions of this paper include: the requirement specification for a generic context‐aware pervasive middleware; and the design and implementation of the framework (i.e. perception framework) supporting the development of context‐aware web services. The perception framework includes a generic rule‐based reasoner allowing developers to use several RIF‐compliant rule description languages.

The purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.

The proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.

The experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.

To improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.

This paper aims to present a methodology for defining and modeling context-awareness and describing efficiently the interactions between systems, applications and their context. Also, the relation of modern context-aware systems with distributed computation is investigated.

On this purpose, definitions of context and context-awareness are developed based on the theory of computation and especially on a computational model for interactive computation which extends the classical Turing Machine model. The computational model proposed here encloses interaction and networking capabilities for computational machines.

The definition of context presented here develops a mathematical framework for working with context. Also, the modeling approach of distributed computing enables us to build robust, scalable and detailed models for systems and application with context-aware capabilities. Also, it enables us to map the procedures that support context-aware operations providing detailed descriptions about the interactions of applications with their context and other external sources.

A case study of a cloud-based context-aware application is examined using the modeling methodology described in the paper so as to demonstrate the practical usage of the theoretical framework that is presented.

The originality on the framework presented here relies on the connection of context-awareness with the theory of computation and distributed computing.

Pervasive computing enhances the environment by embedding many computers that are gracefully integrated with human users. The purpose of this paper is to describe the creation of a smart context‐aware environment in which computation follows people and serves them everywhere. Building such smart environments is still difficult and complex due to lacking a uniform infrastructure that can adapt to diverse smart domains.

To address this problem, the paper proposes an agent‐based pluggable infrastructure which integrates a mobile agent system named pvMogent, establishes an ontology‐based context model and introduces a workflow‐based application model with the open services gateway initiative (OSGi) framework. By plugging corresponding domain context in ontology model and different applications, the infrastructure can be customized to various domains.

Through the implementation of several context‐aware applications, it was found that the infrastructure can largely reduce the development complexity as well as keep the domain extensibility by plugging corresponding domain context in ontology model.

In this paper, a number of key techniques are explored which are suitable for building context‐awareness. The experiences and lessons learned from the system development could further facilitate and inspire the research in this direction.