Context: Automated compliance testing faces challenges when requirements demand semantic understanding. Traditional tools evaluate at most 50% of compliance criteria, necessitating expensive manual verification. Objectives: This study evaluates Large Language Model (LLM) capabilities for web accessibility conformance testing across 39 WCAG 2 AA success criteria, examining performance, prompt engineering, and proposing an interpretive framework. Methods: We evaluated seven cost-effective LLMs on isolated HTML snippets from 384 W3C ACT cases. Three prompting strategies were tested with five replications, comparing LLMs against traditional tools. Results characterise snippet-level evaluation, which may differ from full-page testing. Results: The best configuration (deepseek-reasoner under provider-default settings) achieved 71.52% accuracy with example-based prompts—an 8.2% cross-model average improvement over basic prompts. Traditional tools achieved 22.9–33.9% real accuracy (treating coverage gaps as errors) and 22.9–44.4% decision accuracy. LLMs classified every test case (100% coverage), reflecting their always-answering design rather than superior capability, whereas tools selectively abstain. LLMs struggled with determining test applicability (45%–67% error rates) and Understandable criteria. Conclusion: Contributions include: (1) an unprecedented 39-criteria empirical evaluation; (2) a dual-metric framework (Real vs. Decision Accuracy) clarifying the structural difference between always-answering LLMs and abstaining tools; (3) evidence that prompt engineering improves performance; and (4) a post-hoc interpretive framework (based on expert judgment, not validated measurement) relating criterion characteristics to LLM amenability to generate future hypotheses. LLMs remain best suited for human-in-the-loop workflows.

Generative AI, particularly Large Language Models (LLMs), presents innovative opportunities to enhance software engineering education. Open source LLMs such as LLaMA and Mistral leverage the potential of generative AI offering distinct advantages over proprietary options including transparency, customizability, collaboration, and cost savings. This paper de-velops a catalog of LLM prompt examples tailored for software engineering training, mapped to knowledge areas from the Soft-ware Engineering Body of Knowledge (SWEBoK) framework. Example prompts demonstrate LLMs' capabilities in eliciting requirements, diagram generation, API simulation, effort esti-mation through role-playing, and other areas. The methodology involves evaluating prompt responses from ChatGPT, Mistral, and LLaMA on representative tasks. Quantitative and qualitative analysis assesses quality, usefulness, and correctness. Findings show ChatGPT and Mistral outperforming LLaMA overall, but no model perfectly executes complex interactions. We examine implications and challenges of integrating open source LLMs into classrooms, emphasizing the need for oversight, verification, and prompt design aligned with pedagogical objectives.

Speech Emotion Recognition (SER) is an important application in areas such as online gaming, e-learning, and medical care. However, recognizing emotion in speech is computationally difficult since it necessitates a thorough search for feature selection, algorithm hyperparameter tuning, or algorithm combinations, making ensemble use interesting. Although ensembles are frequently employed in SER, their application has not been greatly explored, and their potential benefits for enhancing recognition accuracy and robustness to variability in speech signals have not been fully realized. The purpose of this article is to assess the effectiveness of ensembles in SER by analyzing their performance under challenging scenarios. The experiment made in this study involved evaluating speech samples from various languages, using an out-of-date set of features, and using simple algorithms with default hyperparameters. For classifier set selection, a basic ensemble technique with decision-level voting and a rudimentary heuristic were applied. The results indicated that basic classifiers significantly improved the SER rate, with an absolute improvement ranging from 0.57% to 9.89%. The suggested ensemble approach outperformed state of the art SER methods, including deep learning-based ones, in terms of recognition rates. The findings justify the use of ensembles in SER applications, particularly in circumstances with insufficient data or out-of-date features and algorithms. The work recommends further investigation of ensembles to enhance recognition accuracy and improve robustness in the face of voice signal variability. Finally, the results of the experiment show that ensembles have the potential to increase SER accuracy, and future research in this field can benefit from the study’s conclusions.

Abstract This study conducts research on deepfakes technology evolution and trends based on a bibliometric analysis of the articles published on this topic along with six research questions: What are the main research areas of the articles in deepfakes? What are the main current topics in deepfakes research and how are they related? Which are the trends in deepfakes research? How do topics in deepfakes research change over time? Who is researching deepfakes? Who is funding deepfakes research? We have found a total of 331 research articles about deepfakes in an analysis carried out on the Web of Science and Scopus databases. This data serves to provide a complete overview of deepfakes. Main insights include: different areas in which deepfakes research is being performed; which areas are the emerging ones, those that are considered basic, and those that currently have the most potential for development; most studied topics on deepfakes research, including the different artificial intelligence methods applied; emerging and niche topics; relationships among the most prominent researchers; the countries where deepfakes research is performed; main funding institutions. This paper identifies the current trends and opportunities in deepfakes research for practitioners and researchers who want to get into this topic.

Students are supposed to work and learn when they are in the classroom, but how are they learning? Answering this question is not so easy, but perhaps knowing the learning activities of the student and following his or her learning progress can be good supporting mechanisms. The PresenceClick environment is intended to let teachers and students nimbly capture what happens in class to provide them, in return, with information about students’ behavior during class. The system involves two platforms −a web system and a mobile application− that working together allow recording of students’ interactions and provide updated graphical visualizations about their behavior.

Computer supported cooperative work (CSCW) environments enable users to interact with each other by using computers to conveniently share relevant data across the user interface. Awareness is an essential requirement in CSCW to convey precise information about the context in which the work in group is taking place, contributing thus to collaboration between users. In this kind of environments, we need to go beyond traditional human–computer interaction to embrace human–computer–human interaction (HCHI). It is necessary to devise flexible mechanisms to support HCHI in dealing with the diversity of contexts and group concerns. Furthermore, these mechanisms should endeavor to provide a seamless integration with current development techniques. This work presents a multi-purpose framework to include group awareness in HCHI systems in a seamless way. Proposed framework is based on the Dichotomic View of Plasticity approach. An experiment was conducted with two different versions of a specific groupware platform: the original platform and a new version of it, extended by means of the proposed framework. The goal was twofold: (i) to verify the benefits of applying this framework and (ii) to validate, in terms of user satisfaction, the improvement regarding groupware features introduced in the extended version. The results of the experiment backed up our hypothesis by showing that proposed framework is able to add awareness support to existing human–computer–Human (HCH) interfaces in a seamless way. It is also showed that added awareness components effectively contributed to achieve a higher level of collaboration among users.

Last technological advances have brought drastic changes affecting the way distributed systems are conceived. Designers have to tackle the fact that applications could be controlled by different end users on diverse computing platforms in assorted environments. However, these kinds of facilities only make sense when they are ruled by the well-known group awareness. It is necessary to devise new mechanisms to support and automate the creation of distributed applications flexible enough to cope with the increasing diversity of contexts and able to deal with different group situations. In this paper we present how the guidelines and software support defined in the Dichotomic View of Plasticity can be applied to develop components for particular systems aimed at providing and maintaining group awareness. The aim is to promote distributed interaction and real time coordination among remote users, contributing to real collaboration and a deeper understanding in multi-environment groupware scenarios. This approach includes some innovations that make it different from conventional groupware design methods in some important aspects. A case study is presented in order to show how the approach works.