Responsible Data and AI Practices in Engineering Education: Preparing Graduates for Digital Transformation in Industrial Processes

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.