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The purpose of this paper is to present a case study on the creation of an intelligent discussion board (IDB) that promotes active participation of all students, better threading, and re‐uses vast collections of discussions.

This paper presents an IDB that was modeled like an intelligent tutoring system with a similar set of data sources, coding schemes, and dialog patterns. The system was tested with two undergraduate courses and quantitative and qualitative analyses were conducted to compare discussions on the IDB and the traditional discussion board.

The results of the case study and analysis of discussion board postings showed that the IDB contained fewer unrelated postings than their traditional counterparts.

The IDB was created to overcome the challenges like students paraphrasing each other, lurking, and lack of cohesion in the postings. They can serve as an assessment tool for discussion forums. The IDB must guide the discussion without controlling the free flow of ideas. Further research with larger numbers of students and also in multiple domains is necessary.

IDBs can be created with the existing resources and technologies and can serve as an assessment tool. Promoting better communication can lead to improved learning with asynchronous discussion boards.

This paper presents the first description of an IDB that can overcome challenges to traditional discussion boards and can also serve as a valuable assessment tool.

Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers.

Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry.

On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing.

Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.

This paper aims to present part of the work of an ongoing research project that is looking at socio‐ cultural and technological developments from a mobile technology convergence view; in order to show how culturally aware convergence developments in mobile technology can be adopted and employed for the betterment of society.

The paper presents a scenario for a mobile technology enabled learning environment in support of the conventional learning approach with a focus on enabling parental involvement and contribution to the daily learning objectives of their children and hence enhancing a quality learning experience. It further critically discusses issues of interface design – at both the device and application levels – that will have an impact on the quality of e‐learning, with a focus on mobile technology.

The paper shows how interface design can positively enhance the quality defining characteristics of learning in an e‐learning environment. Ways of achieving these characteristics of learning through effective e‐learning are reported. This is done by addressing requirements for quality‐learning through effective interface‐design considerations, towards meeting the overall quality requirements of learning that should be intrinsic to a holistic e‐learning environment. The value of human computer interaction and the critical factors of promoting productive interaction are addressed.

There are several factors affecting quality of e‐learning as a tool and approach to flexible and independent learning. The advent and use of mobile technology has been investigated in this work from a socio‐cultural and technological perspectives in two continents. The limitations lie in the depth of investigations and how far the findings can be applied to the diversity of learners.

As the effects of cultures and the rapid technological advancements take toll on teaching and learning the findings reported in this paper have far reaching implications for learners from different cultures and also for attempts at bridging existing digital divide.

The approach adopted in the research is unique by virtue of new findings and ideas presented. The paper highlights the opportunities for mobile devices and technology to play a role in the development of communities through technology aided learning (e‐learning), with a focus on e‐learning systems and technology requirements for delivering a quality learning experience.

This paper aims to provide a methodological road for the next generation of e‐learning environments.

This paper considers a survey of recent publications (1995‐2002), which aim to provide practical and theoretical indications and advice, which are coupled with practical experimentations.

The paper provides road‐mapping elements, indicating the impact on services and systems to be expected by this design approach.

The survey is based on a selection of sources and it is not exhaustive. The methodology experiments that are used for argumentation are based on the authors’ platform.

The paper presents a useful source of knowledge for researchers and advanced students.

This paper identifies a road for advanced e‐learning systems, and can help researchers and those in industry who desire to introduce and understand the design methodological context of advanced e‐learning systems.

This paper reviews literature on the application of intelligent systems in the libraries with a special issue on the ES/AI and Robot. Also, it introduces the potential of libraries to use intelligent systems, especially ES/AI and robots.

Descriptive and content review methods are applied, and the researchers critically reviewed the articles related to library ESs and robots from Web of Science as a general database and Emerald as a specific database in library and information science from 2007–2017. Four scopes considered to classify the articles as technology, service, user and resource. It is found that published researches on the intelligent systems have contributed to many librarian purposes like library technical services like the organization of information resources, storage and retrieval of information resources, library public services as reference services, information desk and other purposes.

A review of the previous studies shows that ESs are a useable intelligent system in library and information science that mimic librarian expert’s behaviors to support decision making and management. Also, it is shown that the current information systems have a high potential to be improved by integration with AI technologies. In this researches, librarian robots mostly designed for detection and replacing books on the shelf. Improving the technology of gripping, localizing and human-robot interaction are the main concern in recent librarian robot research. Our conclusion is that we need to develop research in the area of smart resources.

This study has a new approach to the literature review in this area. We compared the published papers in the field of ES/AI and robot and library from two databases, general and specific.

The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.

This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.

AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.

AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.

This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

This project developed and implemented a prototype WWW‐based instructional learning system modeled around a metacognitive research and development framework which mapped cognitive variables, to metacognitive learning strategies for those variables, to metadata for the instructional design of the media. The framework helped delineate learning strategies and related metacognitive attributes of young students acquiring knowledge in advanced science concepts in an Internet/browser‐based environment. The framework also provided a basis for learner‐specific Internet content personalization.

The purpose of this study is to verify the effect of smart pedagogy to facilitate the high order thinking skills of students and to provide the design suggestion of curriculum and intelligent tutoring systems in smart education.

A smart pedagogy framework was designed. The quasi-experiment was conducted in a junior high school. The experimental class used the smart pedagogy and smart learning environment. The control class adopted conventional teaching strategies. The math test scores of these two classes were compared to verify the effectiveness of smart pedagogy.

The smart pedagogy framework contains three sections including the situated learning (S), mastery learning (M), adaptive learning (A), reflective learning (R) and thinking tools (T) (SMART) key elements model, the curriculum design method and detailed teaching strategy. The SMART key elements model integrates the situated learning, mastery learning, adaptive learning, reflective learning and thinking tools to facilitate the high order thinking. The curriculum design method of smart pedagogy combines the first five principles of instruction and the SMART key elements model to design the curriculum. The detailed teaching strategies of smart pedagogy contain kinds of innovative learning methods. The results of the quasi-experiment proved that the learning outcome was significantly promoted by using smart pedagogy.

This research investigates a general framework that can be used to cultivate the high order thinking skills in different subjects and grades was one of the first to introduce high order thinking skills into smart education. The framework of smart pedagogy was innovative and effect in practice.

The purpose of this study is to propose a hybrid system to combine the self‐organizing map (SOM) of a neural network with the data‐mining (DM) method for course recommendation of the e‐learning system.

This research constructs a hybrid system with artificial neural network (ANN) and data‐mining (DM) techniques. First, ANN is used to classify the e‐Learner types. Based on these e‐Learner groups, users can obtain course recommendation from the group's opinions. When groups of related interests have been established, the DM will be used to elicit the rules of the best learning path. It is ideal for this system to stimulate learners' motivation and interest. Moreover, the hybrid approach can be used as a reference when learners are choosing between classes.

In order to enhance the efficiency and capability of e‐learning systems, the SOM method is combined to deal with cluster problems of DM systems, SOM/DM for short. It was found that the SOM/DM method has excellent performance.

This research is limited by the fact that its participants are from a business college of a university in Taiwan, and it is applied by SOM/DM to recommend courses of e‐learners. This research is useful in the domain of the e‐learning system.

The results of this research will provide useful information for educators to classify their e‐learners or students more accurately, and to adapt their teaching strategies accordingly to retain valuable e‐learners subject to limited resources. The experiments prove that it is ideal to stimulate learners' motivation and interest.

This paper aims to report on the bibliometric research trends on the application of machine learning/intelligent systems in the prediction of food contamination and the surveillance of foodborne diseases.

In this study, Web of Science (WoS) core collection database was used to retrieve publications from the year 1996–2021. Document types were classified according to country of origin, journals, citation and key research areas. The bibliometric parameters were analyzed using VOSviewer version 1.6.15 to visualize the international collaboration networks, citation density and link strength.

A total of 516 articles across 6 document types were extracted with an average h-index of 51 from 10,570 citations. The leading journal in publications was Science of the Total Environment (3.6%) by Elsevier and the International Journal of Food Microbiology (2.5%). The United States of America (USA) (24%) followed by the People's Republic of China (17.2%) were the most influential countries in terms of publications. The top-cited articles in this study focused on themes such as contamination from packaging materials and on the strategies for preventing chemical contaminants in the food chain.

This report is significant because the public health field requires innovative strategies in forecasting foodborne disease outbreaks to advance effective interventions. Therefore, more collaboration need to be fostered, especially in developing nations regarding food safety research.