The fast-evolving labor market requires a transformative educational approach that aligns curricula with the competencies demanded by employers. This is in line with Education 4.0 framework, which focuses not only on providing learners with knowledge but also on developing critical abilities, skills, and values necessary for success in dynamic, technology-driven environments. This study presents a structured method for redesigning courses to integrate essential skills such as problem-solving and critical thinking, along with values like adaptability and curiosity, into course materials and assessments. The method was developed iteratively, integrating insights from literature reviews, workshops, and interviews with instructors. The result is a comprehensive tool that offers practical guidelines for enhancing educational content and ensuring alignment with Education 4.0 principles.

The proposed method incorporates 16 detailed tables, which serve as self-assessment checklists and actionable guides for instructors, addressing key elements such as problem-solving, critical thinking, adaptability, and curiosity in both course materials and assessments. These checklists enable instructors to evaluate how well their courses align with desired competencies. Additionally, the action tables provide detailed recommendations for improvement based on assessment results. The method assesses the alignment of courses with the selected emphasized elements namely, problem-solving, critical thinking, adaptability, and curiosity. This will guide instructors in taking targeted actions to strengthen areas where competencies are lacking. The recommended actions are also tailored according to the level of alignment of the courses with the Education 4.0 framework.

The method was developed and tested through workshops and experimentation sessions with university instructors from different programs. Feedback highlighted its effectiveness in identifying gaps in course design and providing actionable steps for improvement. Instructors appreciated the clarity of the checklists and the adaptability of the action tables, which enabled them to customize interventions for their specific course contexts. The iterative nature of the development process ensured that the method remained user-friendly, flexible, and practical across diverse academic disciplines.

This paper's scientific contribution lies in its novel, systematic approach to operationalizing the Education 4.0 framework through a replicable methodology for incorporating critical skills and values into higher education curricula. By bridging the gap between theoretical concepts and practical application, it provides educators with actionable tools to redesign courses in alignment with workforce needs. Grounded in empirical testing and educator feedback, the iterative development process ensures the method's relevance and adaptability across diverse educational contexts. This method supports instructors in designing meaningful, student-centered learning experiences and promotes lifelong learning skills essential for thriving in a dynamic and complex world.

Industry 4.0 and the increasing use of artificial intelligence and machine learning have allowed the analysis of large amounts of data and improve performance across many businesses and sectors. These sectors have significantly increased their reliance on data when making decisions. This paper examines the use of data-driven decision-making on road maintenance planning in Sweden. Data related to road maintenance following the Swedish Road Maintenance Standard, were collected focusing on the International Roughness Index (IRI) and rut depth as primary features. Analyzing such data enabled the identification of maintenance needs within three separate timeframes: immediate, the next five years, and long-term. The model predicted maintenance needs based on the IRI with up to 96% accuracy. However, the model's accuracy dropped to only 67% when predicting maintenance needs over the next five years. In contrast, the model that predicted maintenance needs based on rut depth demonstrated high accuracy across all three timeframes, achieving up to 92% accuracy. The model demonstrated that modern road condition variables are crucial to prediction. In terms of predictions, 2023 IRI measurements were the most important. Based on our findings, this paper improves data-driven decision-making in Swedish road maintenance, resulting in more effective resource allocation and decreased emergency maintenance expenses. Moreover, the study highlights the value of collecting and utilizing more accurate and thorough road state data to enhance these models.

The rapid evolution of labor market demands, driven by technological and societal transformations, has intensified the need for higher education to foster future-oriented competencies. Frameworks such as Education 4.0 emphasize the development of abilities, skills, attitudes, and values; such as problem-solving, critical thinking, adaptability, and curiosity; alongside disciplinary knowledge. However, translating these competencies into course-level practice remains a challenge for educators, especially with regard to course materials and assessment methods. This study addresses this gap by proposing and evaluating a structured, instructor-led method for course redesign aligned with the Education 4.0 framework (E4CAM). Through an iterative research process involving literature reviews, expert workshops, and in-depth interviews, E4CAM integrates self-assessment checklists and targeted action guidelines to support competency integration. E4CAM was evaluated through two workshops and experimental applications involving university instructors. Results indicate E4CAM's practical relevance and adaptability across course levels and disciplines. By enabling instructors to systematically align course content and assessments with Education 4.0 competencies, the proposed approach offers a scalable tool for enhancing pedagogical practices and advancing competency-based education in higher education.

In an ideal world, the way we learn should align with the way we are taught. However, this is not always the case, and we need to rethink our approach to teaching in academic institutions such as universities to help students learn at their own pace, where innovative technology plays a vital role in accomplishing this goal. This study proposes the use of artificial intelligence (AI) as a means of teaching in academic institutions. Specifically, an AI-powered conversational bot called conversational AI bot for efficient learning (COFFEE) is designed, implemented, and will used as an adaptive learning technology. COFFEE will analyze the students’ learning habits and adjust the teaching style based on their strengths and weaknesses. Teaching and learning are closely linked to the disciplinary areas of education, behavior, and technology. By combining these areas into a conversational bot equipped with AI algorithms and access to big data, we can disrupt traditional teaching methods. This will benefit students who require special attention, such as those with severe learning disabilities who may not receive the necessary attention from instructors due to time constraints or lack of experience. COFFEE is a valuable tool that can be used in academic institutions at all levels, including primary and secondary education. It will be based on the latest advancements in AI and data science and will be designed to be feasible, reliable, and customizable.

This study proposes a model to assess data-driven decision-making (DDDM) readiness in organizations. We present the results from investigating the DDDM readiness of a Swedish organization in the food industry. We designed and developed a questionnaire to collect data about the organization’s decision-making and IT systems. We conducted eleven interviews at the case study organization: ten with various functional decision-makers and one with the IT Manager about IT systems. The interview data were then analyzed against known decision theories and state-of-the-art DDDM. Based on the interview outcomes, we analyze the data according to the assessment model and recommend changes to the organization’s readiness for data-driven decisions. The findings show that while the organization was assessed as ready in the decision-making process and decision-maker pillars, it was not ready in the data or analytics pillars. Accordingly, we recommend a set of actions, including considering integration and decision systems, further developing dashboards, increasing data and analytics resources (such as enterprise data warehouse, big data management tools, data lake environment, and data analytics algorithms), and defining key roles necessary for digitalization and DDDM (such as Data Engineer, Data Scientist, Business Intelligence Specialist, Chief Data Officer, and Data Warehouse Designer/Administrator). The contribution of this study is the DDDM readiness assessment model, accompanied by a questionnaire for determining the readiness level in organizations.

Since the inception of civilization, humans have monopolized decision-making. Although human intelligence is capable of producing excellent decisions, our history is littered with cases of disastrous choices. In general, bad decisions result from many factors, including cognitive biases. It is important to avoid bad decisions at personal, organizational and societal levels, but are they avoidable? Artificial Intelligence (AI) has been upheld as the remedy for our biases. This opinion paper investigates if such a claim is warranted. We briefly review the literature on cognitive biases, AI biases, and hybrid intelligence. We conclude by suggesting that although AI may have tremendous benefits for decision-making, in its present form, it cannot eliminate all of our human biases, and can contrarily result in additional biases. More research on the use of AI in decision-making is needed. 

Human-machine collaboration has potentially led to higher quality and more informed data-driven decisions. However, evaluating these decisions is necessary to measure the benefits, as well as enable experiential learning and posterior rationalization of the results and consequences. Nevertheless, the multiplicity of human-machine collaboration modes, as well as the multi-faceted nature of data-driven decisions complicates evaluation, and evaluation solutions are lacking both in research and in practice. This is further reflected in the complexity of incorporating evaluation in the design of such data-driven decision making systems, since developers are left without theoretically grounded and practically feasible principles to guide implementation. In this paper, we propose a set of five design principles, explicated from theory and practice, for systems implementing data-driven decision evaluation as the output of design science research cycles. The design principles are: 1) multi-faceted evaluation criteria, 2) unified viewpoint, 3) collaborative rationality, 4) processual ex-post evaluation, and 5) adaptive feedback and learning loops. They are further contextualized in the case of AI-enabled menu design at Antell, an innovative pioneer in the restaurant business in Finland, and consequently evaluated by the development managers of the project. Accordingly, the design principles contribute to the knowledge base on metahuman systems and data-driven decision evaluation, by concretizing existing normative concepts into prescriptive knowledge, also guiding future research and generalizing towards a design theory. Furthermore, they provide implementable statements for designing and developing such systems in practice and can be used as a checklist to compare and evaluate existing systems.

This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to understand the literature, identify research gaps, and posit research questions for researchers, academic institutions, and governmental healthcare organizations. We intend to explain how contemporary analytics have been used to address healthcare concerns as well as to posit several research questions for future studies based on gaps which we have identified. The study has multi-folds contribution areas: first, it provides a state-of-the-art review to healthcare analytics, second, it posits a research agenda to advance the knowledge in this area further.

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions.