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ABSTRACT This paper addresses the challenge of improving the effectiveness and innovativeness of online teaching in higher education. It presents a blended learning course designed for faculty development and discusses the findings of surveying the opinions of participants. The course integrates synchronous sessions with digital learning tools and platforms to equip educators with essential skills for online teaching. A mixed‐methods design has been applied for evaluating the educational activity, incorporating both qualitative and quantitative data collection. The course had a completion rate of 91.13%. The opinions of 72 higher education members have been surveyed, and the findings revealed that the blended learning course enhanced instructors' ability to design and deliver online content that is both pedagogically sound and engaging. The necessity for such learning activities is strongly emphasised. The paper concludes by discussing the implications for future course design and faculty development programs in higher education. [ABSTRACT FROM AUTHOR]

Blockchain technology has been used with great effect in farm-to-fork traceability projects. However, this technology has a steep learning curve when it comes to its user interface. To minimize this difficulty, we created a solution based on a Large Language Model (LLM) conversational agent. Our implementation, starting with an existing knowledge base that is prepared and processed with an embedding model to be stored in a vector database, follows a Retrieval-Augmented Generation (RAG) approach. Other non-textual media like images and videos are aggregated with the embeddings to enrich the user experience. User queries are combined with a proximity search in the vector database and feed into an LLM that considers the conversation history with the user in its replies. Given the asynchronous nature of these models, we implemented a similarly asynchronous scheme using Server-Sent Events that deliver the models' replies to a UI that supports multimodal media types such as images and videos by providing the visualization of these resources. The end solution allows users to interact with advanced technologies using a natural language interface; this in turn empowers food traceability projects to overcome their natural difficulty in engaging early adopters. [ABSTRACT FROM AUTHOR]

Blockchain technology has been used with great effect in farm-to-fork traceability projects. However, this technology has a steep learning curve when it comes to its user interface. To minimize this difficulty, we created a solution based on a Large Language Model (LLM) conversational agent. Our implementation, starting with an existing knowledge base that is prepared and processed with an embedding model to be stored in a vector database, follows a Retrieval-Augmented Generation (RAG) approach. Other non-textual media like images and videos are aggregated with the embeddings to enrich the user experience. User queries are combined with a proximity search in the vector database and feed into an LLM that considers the conversation history with the user in its replies. Given the asynchronous nature of these models, we implemented a similarly asynchronous scheme using Server-Sent Events that deliver the models’ replies to a UI that supports multimodal media types such as images and videos by providing the visualization of these resources. The end solution allows users to interact with advanced technologies using a natural language interface; this in turn empowers food traceability projects to overcome their natural difficulty in engaging early adopters.

Background Scientific workflow systems are increasingly popular for expressing and executing complex data analysis pipelines over large datasets, as they offer reproducibility, dependability, and scalability of analyses by automatic parallelization on large compute clusters. However, implementing workflows is difficult due to the involvement of many black-box tools and the deep infrastructure stack necessary for their execution. Simultaneously, user-supporting tools are rare, and the number of available examples is much lower than in classical programming languages. Results To address these challenges, we investigate the efficiency of large language models (LLMs), specifically ChatGPT, to support users when dealing with scientific workflows. We performed 3 user studies in 2 scientific domains to evaluate ChatGPT for comprehending, adapting, and extending workflows. Our results indicate that LLMs efficiently interpret workflows but achieve lower performance for exchanging components or purposeful workflow extensions. We characterize their limitations in these challenging scenarios and suggest future research directions. Conclusions Our results show a high accuracy for comprehending and explaining scientific workflows while achieving a reduced performance for modifying and extending workflow descriptions. These findings clearly illustrate the need for further research in this area. [ABSTRACT FROM AUTHOR]

For organizations, the error-free, consistent, and transparent management of business processes is crucial. Process management relates to the proper handling of data, achievable through enterprise resource planning (ERP) software. This software enables all departments, structured around a central database, to work together. From an organizational perspective, one of the most critical factors for the success of ERP software is its users. Users often require support when using ERP software for various reasons, such as lack of training, experience, or technical issues. Providing this support, whether internally or from external sources, incurs a cost. This study seeks to determine whether a structure can be developed where users can resolve their support requests independently. To achieve this goal, we propose a model for an intelligent user support system with an AI-based process engine for ERP users. Using this model, unstructured user support requests, created in natural language, are transformed into structured forms using natural language processing techniques and then classified using multi-class machine learning algorithms. The process relevant to the identified class is executed to generate a solution for the user's problem. In this study, 1,000 samples of text written in natural language across 10 different categories, based on real data, were used in the learning process. The data were analyzed using machine learning algorithms (K-Nearest Neighbors, Naive Bayes, C4.5, Support Vector Machine, Random Forest, Sequential Minimal Optimization, LibSVM) and deep learning algorithms (Long Short-Term Memory). The best classification was achieved with Sequential Minimal Optimization (SMO) using TF-IDF weighting. The developed chat and process robot helped convert requests or problems into structured forms and provided solutions. In addition to solving the problem through chat or process, technologies such as augmented reality and virtual reality were used. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of modern technology, the convergence of diverse technological advancements is reshaping how ERP systems function and perform. This transformation aims to bolster the utilization of ERP systems, ultimately leading to improved organizational performance. Despite these developments, limited insights are available regarding the factors that both foster ERP system usage and impede organizational performance. Consequently, the present study strives to gain a deeper comprehension of the determinants influencing ERP system utilization, consequently contributing to organizational advancement. Specifically, the study seeks to explore how ERP system usage acts as a mediator within the framework of internal and external factors, individual characteristics, ERP-specific elements, and overall organizational performance. To achieve this objective, survey questionnaires were administered to employees within Saudi Arabian companies, utilizing the convenience sampling technique. The subsequent analysis was conducted using Structural Equation Modelling (SEM) through Amos-16. The study's outcomes highlight the significance of User Support, Top Management Support, IT Expert/Consultant Support, Computer self-efficacy, and Communication Effectiveness in driving increased ERP system utilization, thereby fostering organizational performance enhancement. The outcomes of this study enrich the existing knowledge base by deepening researchers' comprehension of the various elements that impact both ERP utilization and overall firm performance. Furthermore, the study's findings provide valuable insights for industry practitioners, enabling them to strategically incorporate these factors to optimize ERP usage and consequently enhance firm performance. Notably, the examination of these interrelationships among variables, particularly within the context of Saudi Arabian organizations, remains a relatively underexplored area within the current body of research, based on the researchers' available information.

In the rapidly evolving landscape of modern technology, the convergence of diverse technological advancements is reshaping how ERP systems function and perform. This transformation aims to bolster the utilization of ERP systems, ultimately leading to improved organizational performance. Despite these developments, limited insights are available regarding the factors that both foster ERP system usage and impede organizational performance. Consequently, the present study strives to gain a deeper comprehension of the determinants influencing ERP system utilization, consequently contributing to organizational advancement. Specifically, the study seeks to explore how ERP system usage acts as a mediator within the framework of internal and external factors, individual characteristics, ERP-specific elements, and overall organizational performance. To achieve this objective, survey questionnaires were administered to employees within Saudi Arabian companies, utilizing the convenience sampling technique. The subsequent analysis was conducted using Structural Equation Modelling (SEM) through Amos-16. The study's outcomes highlight the significance of User Support, Top Management Support, IT Expert/Consultant Support, Computer Self Efficacy, and Communication Effectiveness in driving increased ERP system utilization, thereby fostering organizational performance enhancement. The outcomes of this study enrich the existing knowledge base by deepening researchers' comprehension of the various elements that impact both ERP utilization and overall firm performance. Furthermore, the study's findings provide valuable insights for industry practitioners, enabling them to strategically incorporate these factors to optimize ERP usage and consequently enhance firm performance. Notably, the examination of these interrelationships among variables, particularly within the context of Saudi Arabian organizations, remains a relatively underexplored area within the current body of research, based on the researchers' available information. [ABSTRACT FROM AUTHOR]