Currently, food waste is a global concern, a problem that arises mainly at the consumption level and generates environmental, economic, and social impacts. One way to reduce the food waste problem is to use the food we already have at home. However, this causes another concern, which is what to cook with certain foods. Sometimes we do not know what recipes can be made. Knowing which ingredients can be mixed and how to mix them can be a difficult task for a beginner cook, so selecting the right ingredients for a recipe is essential. Therefore, it is proposed to develop a recipe recommendation system through image recognition of food ingredients. Presently, the system is a web application that recognizes an image given by the user and recommends recipes containing the recognized ingredient. For this, a convolutional neural network model, the ResNet-50, was built to perform image recognition and trained with a dataset that contains about 36 classes of vegetables and fruits. Through this training, the model reached 96% accuracy in classifying the dataset images. The recommendation system uses the label of the recognized ingredient to obtain the recipes, which are searched through the Edamam API.

Nowadays, food waste is seen as a complex problem with effects on the social, economic, and environmental domains. Even though this view is widely held, it is frequently believed that individual acts have little to no impact on the issue. But just like with recycling, there may be a significant impact if people start adopting more sustainable eating habits. We suggest using a cutting-edge convolutional neural network (CNN) model to identify food in light of these factors. To improve performance, this model makes use of several strategies, such as fine-tuning and transfer learning. Additionally, we suggest using the Selenium library to create a dataset by employing the web scraping technique. This strategy solves the problem that many open-source datasets have with the overrepresentation of foods from the Asian continent by enabling the addition of foods to the dataset in a customized way. First, using the PRISMA methodology, a thorough examination of recent research in this field will be carried out. We will talk about the shortcomings of the most widely used dataset (Food-101), which prevent the ResNet-50 model from performing well. Using this information, a smartphone app that can identify food and suggest recipes based on the ingredients it finds could be developed. This would prevent food waste that results from the lack of imagination and patience of most people. The food recognition model used was the ResNet-50 convolutional neural network, which achieved 90% accuracy for the validation set and roughly 97% accuracy in training.

This study investigated the crucial factors for measuring the success of the information system used in the e-learning process, considering the transformations in the work environment. This study was motivated by the changes caused by COVID-19 witnessed after the shift to fully online learning environments supported by e-learning systems, i.e., learning emphasized with information systems. Empirical research was conducted on a sample comprising teaching staff from two European universities: the University of Novi Sad, Faculty of Technical Sciences in Serbia and the Polytechnic Institute of Castelo Branco in Portugal. By synthesizing knowledge from review of the prior literature, supported by the findings of this study, the authors propose an Extended Information System Success Measurement Model—EISSMM. EISSMM underlines the importance of workforce agility, which includes the factors of proactivity, adaptability, and resistance to change, in the information system performance measurement model. The results of our research provide more extensive evidence and findings for scholars and practitioners that could support measuring information system success primarily in e-learning and other various contextual settings, highlighting the importance of people’s responses to work environment changes.

The integration of Artificial Intelligence into educational environments is reshaping the way digital resources support teaching and learning, which reinforces the need to understand how prompting strategies can enhance engagement, autonomy, and personalisation. This study examines the pedagogical role of prompt engineering in the transformation of static digital materials into adaptive and interactive learning experiences aligned with the principles of Education 4.0. A systematic literature review was conducted between 2023 and 2025 following the PRISMA protocol, comprising a sample of 166 studies retrieved from the ACM Digital Library and Scopus databases. The search strategy employed the keywords “artificial intelligence” OR “intelligent tutoring systems” AND “e-learning” OR “digital education” AND “personalised learning” OR “academic performance” OR “student engagement” OR “motivation” OR “ethical issues” OR “student autonomy” OR “limitations of AI”. The analysis identified consistent improvements in academic performance, motivation, and student engagement, although persistent limitations remain related to technical integration, ethical risks, and limited pedagogical alignment. Building on these findings, the article proposes a structured prompt engineering methodology that integrates interdependent components including role definition, audience specification, feedback style, contextual framing, guided reasoning, operational rules, and output format. A practical illustration shows that embedding prompts into digital learning resources, exemplified through PDF-based exercises, enables AI agents to support personalised and adaptive study sessions. The study concludes that systematic prompt design can reposition educational resources as intelligent, transparent, and pedagogically rigorous systems for knowledge construction.

It is important to work on educating children’s emotional intelligence, namely the re-awareness and control of emotions, both their own and those around them so that they feel empathy for others, establish and maintain positive relationships and make conscious decisions. This document describes a proposal for a gamified solution, based on the development of a multimedia product, which aims to help children, parents and teachers in the education of emotional intelligence in children. The solution makes it possible to present everyday scenarios to children, allowing adults to find out how they feel and, based on this information, to work on feelings and social behaviour. The solution was based on research into studies available in scientific databases on children’s emotional intelligence, as well as research into exercises that can help work on this same issue. Once implemented, the solution was tested with children from a primary school, where it was possible to collect feedback from them and their teachers and make improvements. This study presents the design, development and evaluation of a gamified application for children focused on emotional intelligence. The methodology used is based on a systematic literature review following the PRISMA protocol and the development of an iterative multimedia product. The study sample included around 200 elementary school children, where it was possible to collect qualitative feedback to evaluate the effectiveness of the application. The results obtained made it possible to make improvements to the design of the application and to obtain feedback from the teachers, which was very positive, but transmitted by direct interview.

The integration of Artificial Intelligence (AI) into educational environments is rede-fining how digital resources support teaching and learning, highlighting the need to understand how prompting strategies can enhance engagement, autonomy, and per-sonalisation. This study explores the pedagogical role of prompt engineering in trans-forming static digital materials into adaptive and interactive learning experiences aligned with the principles of Education 4.0. A systematic literature review, conducted under the PRISMA protocol, examined the use of educational prompts and identified key AI techniques applied in education, including machine learning, natural language processing, recommender systems, large language models, and reinforcement learning. The findings indicate consistent improvements in academic performance, motivation, and learner engagement, while also revealing persistent limitations related to technical integration, ethical risks, and weak pedagogical alignment. Building on these insights, the article proposes a structured prompt engineering methodology encompassing in-terdependent components such as role definition, audience targeting, feedback style, contextual framing, guided reasoning, operational rules, and output format. A practical illustration demonstrates how embedding prompts into digital learning resources, exemplified through PDF-based exercises, enables AI agents to facilitate personalised, adaptive study sessions. The study concludes that systematic prompt design can repo-sition educational resources as intelligent, transparent, and pedagogically rigorous systems for knowledge construction.