Celina Pinto Leao

INTRODUCTION: As artificial intelligence (AI) tools become increasingly integrated into higher education, understanding how engineering students perceive and apply principles of academic integrity, data integrity and responsible AI use has become increasingly important. OBJECTIVES: This study investigates how master’s engineering students perceive and apply principles of academic integrity, data integrity, and responsible AI use. It also explores the potential relevance of these practices for the development of competencies associated with participation in data-driven and AI-enabled professional environments. METHODS: A mixed-methods approach was employed using the PEDRA questionnaire (Perceptions on Ethics and Development of Responsibility in Academia), developed by the authors. The instrument was designed to explore students’ perceptions, experiences, and ethical reasoning regarding academic integrity and responsible research practices. The analysis focused on two questionnaire items addressing the influence of academic honesty on data use and the frequency of AI tool use in academic activities. The study involved master’s engineering students from two higher education institutions. RESULTS: Based on the 48 complete responses analysed, the findings indicate awareness of data integrity principles, particularly regarding anonymity, confidentiality, informed consent, and rigorous data analysis. Ethical referencing and transparency in data handling were mentioned less frequently, suggesting areas for further educational support. Regarding AI adoption, 21 of 48 respondents (43.8%) reported occasional use of AI tools, reflecting a cautious and exploratory approach. Descriptive differences in AI use were observed across age and gender groups, with female respondents being more likely to explore AI while maintaining ethical awareness, although these findings should be interpreted with caution. CONCLUSION: The study highlights the importance of integrating responsible data and AI practices into engineering education. The results suggest that developing students’ capacity to use data and AI critically, transparently, and ethically may support the development of competencies associated with responsible participation in increasingly digital and AI-enabled environments. The PEDRA questionnaire demonstrates potential as an exploratory and diagnostic instrument for identifying educational needs related to academic integrity, responsible AI use, data integrity, and trustworthy digital practice.

INTRODUCTION: Contemporary engineering operates in a data-rich yet uncertainty-laden landscape, particularly under the technological shifts introduced by Industry 4.0. While foundational, deterministic models frequently fail to address the ambiguity, variability, and incompleteness inherent in real-world data, this paper examines the growing need to embed statistical reasoning within digital engineering decision-making processes to ensure robustness and interpretability.OBJECTIVES: The study aims to investigate how statistical thinking contributes to innovation, transparency, and adaptive decision-making in digitalized engineering systems. It identifies conceptual gaps and underexplored themes in current literature and emphasizes the strategic relevance of probabilistic reasoning in addressing uncertainty across complex industrial settings.METHODS: A hybrid scoping review methodology was applied, combining a semantic AI-driven search via Elicit with a structured bibliographic query in Scopus. The resulting corpus of 928 curated publications was analysed through bibliometric techniques and social network analysis using VOSviewer. This comprehensive process enabled the identification of co-occurrence patterns, thematic clusters, and evolving disciplinary linkages, ensuring the credibility and reliability of the findings.RESULTS: Five primary research clusters emerged: decision optimization, risk management and human factors, machine learning integration, digital information systems, and sustainability. These clusters represent key areas where probabilistic modelling and uncertainty quantification can significantly enhance engineering practices. Although AI and big data analytics are increasingly prevalent, the underrepresentation of probabilistic modelling and uncertainty quantification in these clusters reveals a disconnect between data-centric innovation and risk-aware engineering practice.CONCLUSION: A conceptual shift toward probabilistic reasoning is advocated as a necessary response to the complexity of modern digital engineering environments. Repositioning statistical thinking as a central enabler of digital transformation supports the development of resilient, interpretable, and future-ready engineering systems. Integrating these methodologies into engineering curricula, AI pipelines, and industrial decision-support infrastructures is essential for advancing strategic, uncertainty-aware innovation.