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PurposeThe present study aims to analyse the presumed relationship between VLC use and students' grades.Design/methodology/approachThe research strategy unfolds as a case study (Yin, 1994), framed by how undergraduate students of pharmaceutical sciences used video lecture capture (VLC) and the impact of VLC on pedagogic differentiation. Looking at the course of Mechanistic Toxicology (MecTox), the objective is to describe this case of pharmaceutical sciences in depth.FindingsThe findings reveal that over 90% of students engaged with VLC videos, with the average viewing time exceeding the total available video minutes, indicating strong student engagement. The study particularly highlights VLC's positive impact on students with lower academic performance (grades D and E), suggesting that VLC can help reduce the performance gap and support a more inclusive educational environment.Research limitations/implicationsThe findings may have limited generalisability beyond the specific context and sample used. However, this study allows the research findings to be compared with previous research (Remi & atilde;o et al., 2022), contributing to the debate on how pedagogic research can promote evidence-based decisions regarding innovative strategies. The meaning of educational inclusion processes and diversity is, thus, contingent on the institutionalisation of research as a practice of teaching and learning.Practical implicationsThe results of this study thus provide interesting insights for the design of strategic action, considering the diversity of students as seen in parents' academic qualifications and students' conditions (e.g. student-workers, living away from home, holding a grant of economic and social support).Social implicationsThe implications of research findings for society bring the issue of equity in education to the fore. By addressing the diverse needs of students, HEIs can contribute to greater educational equity.Originality/valueUsing VLC as a differentiated pedagogic device might give diversity real content insofar as institutional and national policies can mitigate the possible negative effects of parents' low academic qualifications and the students' conditions of living away from their residence area and holding a grant of economic and social support.

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Effective project management is crucial to the success of any industry, particularly in metalworking, where deadlines, resources, and costs play critical roles. However, accurately predicting project execution times remains a significant challenge, directly impacting companies' competitiveness and profitability. In this context, the integration of Artificial Intelligence (AI) tools emerges as a promising solution to improve the accuracy of time predictions and optimise project management in the metal-working industry.AI, particularly through techniques such as Machine Learning (ML), has demonstrated significant potential in predicting timeframes for engineering projects. Predictive activity-based models can be trained with historical data to identify patterns and forecast future durations with high accuracy. In the metalworking sector, where projects are often complex and subject to variability, AI can provide notable advantages in terms of precision and efficiency.This study aims to formulate an activity-based model, represented in IDEF0 (part of the Integration Definition for Function Modelling), for predicting activity durations using AI to support project management in the metalworking industry. By applying the principles of the IDEF0 tool, the objective is to develop a robust and adaptable system capable of analysing historical data, environmental factors, project characteristics, and other relevant inputs to produce more accurate time forecasts.With this work, we aim to contribute to the advancement of Project Management (PM) in the metal-working industry, particularly by providing an activity-based model to support the creation of an innovative AI tool for predicting execution times with greater accuracy.