Feedback is an essential component of the teaching–learning process; however, it can vary in quality due to different contexts and students’ and professors’ individual characteristics. This research explores the effect of generative artificial intelligence (GenAI) in strengthening personalized and timely feedback by initially defining an adaptable framework to integrate GenAI into feedback mechanisms defined in theoretical frameworks. We applied a between-subjects analysis in an experimental research design with 263 undergraduate students across multiple disciplines based on an approach consisting of a pretest–post-test process and control and focus groups to evaluate students’ perceptions of artificial intelligence-enhanced feedback versus traditional professor-led feedback. The results show that students who used GenAI declared statistically significantly higher satisfaction levels and a greater sense of ownership in the feedback process. Additionally, GenAI scaffolded continuous improvement and active student participation through a structured and accessible feedback environment, determining that 97% of students are willing to reuse the tool. These findings show that GenAI is a valuable tool to complement professors in the creation of an integrated feedback model. This study draws directions on future research on the combination of artificial intelligence and innovative strategies to produce a long-term impact on education.

Amphiphilic Janus dendrimers (JDs) are repetitively branched molecules with hydrophilic and hydrophobic components that self-assemble in water to form a variety of morphologies, including vesicles analogous to liposomes with potential pharmaceutical and medical application. To date, the self-assembly of JDs has not been fully investigated thus it is important to gain insight into its mechanism and dependence on JDs’ molecular structure. In this study, the aggregation behavior in water of a second-generation bis-MPA JD was evaluated using experimental and computational methods. Dispersions of JDs in water were carried out using the thin-film hydration and ethanol injection methods. Resulting assemblies were characterized by dynamic light scattering, confocal microscopy, and atomic force microscopy. Furthermore, a coarse-grained molecular dynamics (CG-MD) simulation was performed to study the mechanism of JDs aggregation. The obtaining of assemblies in water with no interdigitated bilayers was confirmed by the experimental characterization and CG-MD simulation. Assemblies with dendrimersome characteristics were obtained using the ethanol injection method. The results of this study establish a relationship between the molecular structure of the JD and the properties of its aggregates in water. Thus, our findings could be relevant for the design of novel JDs with tailored assemblies suitable for drug delivery systems.

Biofilms, structured communities of microorganisms encased in an extracellular matrix, are a major cause of persistent infections, particularly when formed on medical devices. This study investigated the kinetics of biofilm formation by Escherichia coli and Pseudomonas aeruginosa, two clinically significant pathogens, on two medical-grade polymers: polyether ether ketone (PEEK) and polyamide 12 (PA12). Using a modified crystal violet staining method and spectrophotometric quantification, we evaluated biofilm development over time on polymer granules and catheter segments composed of these materials. Results revealed that PEEK surfaces supported significantly more biofilm formation than PA12, with peak accumulation observed at 24 h for both pathogens. Conversely, PA12 demonstrated reduced bacterial adhesion and lower biofilm biomass, suggesting surface characteristics less conducive to microbial colonization. Additionally, the study validated a reproducible protocol for assessing biofilm formation, providing a foundation for evaluating anti-biofilm strategies. While the assays were performed under static in vitro conditions, the findings highlight the importance of material selection and early prevention strategies in the design of infection-resistant medical devices. This work contributes to the understanding of how surface properties affect microbial adhesion and underscores the critical need for innovative surface modifications or coatings to mitigate biofilm-related healthcare risks.

The proliferation of large language models (LLMs) and generative artificial intelligence has catalyzed an unprecedented pedagogical paradigm shift within higher education. This study investigates the adoption, efficacy, and cognitive impact of these technological tools among undergraduate cohorts in engineering and life sciences. Recognizing that baseline familiarity with AI does not inherently translate into advanced operational competency or prompt engineering literacy, this study evaluates the deployment of both standard LLMs and retrieval-augmented generation (RAG)-based customized assistants. The investigation employs a rigorous dual-phase methodology: an exploratory assessment of technology acceptance using standard ChatGPT, followed by a tightly controlled quasi-experiment evaluating the impact of domain-specific “GPT Custom” mentors on academic performance on complex engineering tasks. The empirical results demonstrate that customized AI assistants significantly improve final academic outcomes, yielding average grade increases of more than 15% on data-intensive analytical assignments. Furthermore, the deployment of customized assistants notably reduced grade variability among students, indicating a homogenization of academic performance that effectively levels the learning environment without compromising rigor. This improvement was statistically amplified when students utilized premium, high-capacity versions of the models for extensive synthesis tasks. Ultimately, the data indicate that while AI offers robust adaptive scaffolding, its efficacy depends on users’ critical-thinking capacities, underscoring the urgent need for educational frameworks that cultivate prompt-engineering literacy and responsible human-AI collaboration.

Education for sustainable development requires innovative pedagogies to equip students with the competencies to address complex environmental challenges. This study examines the use of ChallengeBased Learning (CBL) to foster an understanding of sustainability as a transversal (cross-cutting) competence among undergraduates at a private university in Mexico, within a global sustainability context. An extensive literature review highlights the importance of real-world, collaborative learning in developing critical thinking, problem-solving, and cooperative skills essential for sustainability. Over two semesters, the CBL intervention engaged students in addressing real-world challenges, including energy consumption, waste management, water conservation, pollution control, and plastic reduction. Students $(\mathbf{N} \boldsymbol{\approx} \mathbf{1 5 0})$ worked in teams to develop 36 original projects targeting these issues. A mixed-methods approach was employed to evaluate outcomes, including pre- and post-surveys of sustainability knowledge and skills, as well as qualitative reflections. Results showed statistically significant gains in students' self-reported sustainability competencies, including a 48 % increase in understanding of sustainability concepts and a 53 % improvement in their ability to propose practical environmental solutions. Additionally, 80 % of students agreed that CBL made sustainability more tangible and personally meaningful than traditional classes. The results of this study revealed that CBL is an effective pedagogy for sustainability education, bridging theoretical knowledge with practical action and fostering the holistic competencies needed to advance sustainable development.