Rapid technological innovations are changing the jobs that Science, Technology, Engineering, and Mathematics (STEM) students are employed in after completing their education. Therefore, employers are looking for applicants with skills beyond the specialised professional competencies that can be acquired in the chosen subjects during formal education. Higher education institutions (HEIs) have an important role to play in this process, as they endeavour to equip graduates with the skills and knowledge for future jobs. Our study aims to identify the key competencies that Slovenian and Norwegian companies expect from graduates of selected STEM degree programmes in both countries. Based on these findings, the participating HEIs can tailor their degree programmes to the employability requirements of companies. This study first analysed European and international recommendations and initiatives relevant to skills development in STEM employment and education. We selected 30 key competencies and included them in the questionnaires in which companies’ human resources managers (HRMs) defined their attitudes towards employability skills in the future. We focused on companies employing graduates of the Ecotechnologies and Civil and Environmental Engineering programmes of the participating HEIs. The analysis of the results showed that companies in the two countries have different attitudes towards skills. Four competencies were identified as more important in both countries: (i) management and design of processes, technologies, and people’s work, (ii) adaptation to changes, (iii) ability to manage uncertainty, and (iv) innovation. Based on the analysis, we have developed a framework of ten employability skills for STEM students. In addition, this study provides the basis for operating a decision support system (DSS) to monitor the development of students’ competencies. The DSS is being developed as part of the RESPO-VI project supported by the Norway Grants to better match the skills of STEM students as future job seekers with the needs of potential employers. The study’s results will also help other formal and non-formal education adapt their curricula and thus reduce the employability skill gaps between current STEM education and future labour market needs.

Introduction: Recognizing and extracting key information from textual data plays an important role in intelligent systems by maintaining up-to-date knowledge, reinforcing informed decision-making, question-answering, and more. It is especially apparent in the food domain, where critical information guides the decisions of nutritionists and clinicians. The information extraction process involves two natural language processing tasks named entity recognition—NER and named entity linking—NEL. With the emergence of large language models (LLMs), especially ChatGPT, many areas began incorporating its knowledge to reduce workloads or simplify tasks. In the field of food, however, we noticed an opportunity to involve ChatGPT in NER and NEL.Methods: To assess ChatGPT's capabilities, we have evaluated its two versions, ChatGPT-3.5 and ChatGPT-4, focusing on their performance across both NER and NEL tasks, emphasizing food-related data. To benchmark our results in the food domain, we also investigated its capabilities in a more broadly investigated biomedical domain. By evaluating its zero-shot capabilities, we were able to ascertain the strengths and weaknesses of the two versions of ChatGPT.Results: Despite being able to show promising results in NER compared to other models. When tasked with linking entities to their identifiers from semantic models ChatGPT's effectiveness falls drastically.Discussion: While the integration of ChatGPT holds potential across various fields, it is crucial to approach its use with caution, particularly in relying on its responses for critical decisions in food and bio-medicine.

A result-oriented engagement system for performance optimisation (RESPO) has been developed to systematically monitor and improve the competencies of individuals in business, lifelong learning and secondary schools. The RESPO expert system was transferred for use in higher education institutions (HEIs) based on successful practical application trials. The architecture and functionality of the original RESPO expert system have been transformed into a new format that will collect information on the required competencies and the available educational programmes to help students effectively develop competencies through formal and non-formal education. First, the initial version of the RESPO system and its functionality were tested on a selected group of students and higher education staff to validate and improve its effectiveness for the needs of HEIs. This paper summarises the key findings and recommendations of the validators for transforming the RESPO application into an application for HEIs. In addition, the selection of competencies in the RESPO application database has been adapted to align with selected study programmes and the need to develop sustainability-related competencies. These findings can support professionals working in higher education institutions in developing students’ future competencies and fostering the targeted use of learning analytics tools.

Tracking food intakes using a mobile app can be an effective approach for some individuals, but it is crucial to consider their personal preferences and health status before adopting this method. Personalized nutrition apps can also serve as an efficient tool for collecting, analysing, and reporting dietary data to support dietary surveys, such as EU Menu by the European Food Safety Authority (EFSA). However, developing an easy-to-use and efficient app requires several essential components. This work presents the insights gained during the development of Eatvisor, a personalized nutrition mobile app. In Slovenia, there was a lack of such tools tailored to national food choices, dietary habits, and recommendations. The app utilizes a food composition database (FCDB) developed in Slovenia; however, the database had many missing compositional data for generic and branded foods. To address this issue, an in-house database management system (DBMS) was developed to support the compilation of different types of data from various sources, such as foreign FCDBs, GS1 Global Data Synchronisation Network (GDSN), database of waste streams, and crowdsourcing, while adhering to the food standards of CEN and respecting the FAIR (Findable, Accessible, Interoperable, Reusable) principles. It is worth emphasizing that compiling a FCDB is a complex task that requires up-to-date knowledge and technical solutions to streamline and expedite the process. Specifically, the DBMS was integrated with semantic resources for organizing knowledge about food (FNS-Harmony ontology linked with FoodOn), as well as food-related regulatory issues. Additionally, methodologies were developed to support missing data imputation in a semi-automated way, and DBMS was designed to manage missing dietary recommendations in a machine-readable format. Moreover, DBMS allows collecting and linking data and knowledge required for personalized advising, including food composition data, biomarker reference intervals, and tailored dietary advice, for both domain experts and machines. Finally, this work evaluates DBMS from the perspective of the data and knowledge required for the development of a personalized nutrition mobile app, such as Eatvisor. The results suggest that DBMS can effectively support the development of a personalized nutrition app, and the methods used can serve as a framework for developing similar apps in other regions.