The commitment or academic implication (engagement) of universitystudents has become a fundamental element for their welfare and academicperformance and, furthermore, it is also related to their professional futureand social commitment. For this reason, the definition of the concept and theprovision of assessment strategies and tools are essential to know the learningexperiences that lead to enhancing the academic involvement of the students.To develop our research, we have used a mixed quantitative and qualitativemethodology: exploratory and confirmatory factor analysis on the one hand,and discussion groups using the nominal groups technique on the other hand.We have set three different objectives: first, to delve into the multidimensionalmodel of the construct; second, to validate a questionnaire that allows forevaluation of the students’ perception of the learning methodologies used inthe classroom; and third, to check the manageability of the nominal groupsas a qualitative method of analysis. The results demonstrate that our newproposal provides a statistically valid instrument aimed at determining theperceptions of own engagement and an effective, efficient and motivatingqualitative method for students. However, regarding the multidimensionalityof the construct, contrary to the more accepted theoretical point of view thatconsiders three dimensions of engagement (behaviour, cognition and emotion),our results only reveal two dimensions (cognitive-emotional and behavioural).In the discussion and comments section we give possible explanations for thiscontradiction.

Drinking water production is subject to multiple water quality requirements such as minimizing disinfection byproducts (DBPs) formation, which are highly related to natural organic matter (NOM) content. For water treatment, coagulation is a key process for removing water pollutants and, as such, is widely implemented in drinking water treatment plants (DWTPs) facilities worldwide. In this context, artificial intelligence (AI) tools can be used to aid decision making. This study presents an environmental decision support system (EDSS) for coagulation in a Mediterranean DWTP. The EDSS is structured hierarchically into the following three levels: data acquisition, control, and supervision. The EDSS relies on influent water characterization, suggesting an optimal pH and coagulant dose. The model designed for the control level is based on response surface methodology (RSM), targeted to optimize removal for the response variables (turbidity, total organic carbon (TOC), and UV₂₅₄). Results from the RSM model provided removal percentages for turbidity (64.6%), TOC (21.9%), and UV₂₅₄ (30%), which represented an increase of 4%, 33%, and 28% as compared with the DWTP water sample. Regarding the entire EDSS, 62%, 21%, and 25% of turbidity, TOC, and UV₂₅₄ removal were fixed as the optimization criteria. Supervision rules (SRs) were included at the top of the architecture to intensify process performance under specific circumstances.

The development and application of new forms of automation and monitoring, data mining, and the use of AI data sources and knowledge management tools in the water sector has been compared to a ‘digital revolution’. The state-of-the-art literature has analysed this transformation from predominantly technical and positive perspectives, emphasising the benefits of digitalisation in the water sector. Meanwhile, there is a conspicuous lack of critical literature on this topic. To bridge this gap, the paper advances a critical overview of the state-of-the art scholarship on water digitalisation, looking at the sociopolitical and ethical concerns these technologies generate. We did this by analysing relevant AI applications at each of the three levels of the UWC: technical, operational, and sociopolitical. By drawing on the precepts of urban political ecology, we propose a hydrosocial approach to the so-called ‘digital water ‘, which aims to overcome the one-sidedness of the technocratic and/or positive approaches to this issue. Thus, the contribution of this article is a new theoretical framework which can be operationalised in order to analyse the ethical–political implications of the deployment of AI in urban water management. From the overview of opportunities and concerns presented in this paper, it emerges that a hydrosocial approach to digital water management is timely and necessary. The proposed framework envisions AI as a force in the service of the human right to water, the implementation of which needs to be (1) critical, in that it takes into consideration gender, race, class, and other sources of discrimination and orients algorithms according to key principles and values; (2) democratic and participatory, i.e., it combines a concern for efficiency with sensitivity to issues of fairness or justice; and (3) interdisciplinary, meaning that it integrates social sciences and natural sciences from the outset in all applications.