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Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning.

Our paper aims to explore the effectiveness of a constructivist approach to the teaching of engineering ethics through case studies, by putting forward a contextualization of the much discussed case study “Cutting Road Side Trees” (Pritchard, 1992) in light of the constructivist frame suggested by Jonassen (1999). First, we briefly analyse how the use of case studies for the teaching of engineering ethics eludes the complexity of the engineering professional environment before arguing that constructivism is a learning theory that can help to address this complexity. The final section proposes a constructivist reworking of the case method in a manner that aims to correct the deficiencies identified, followed by a discussion of the results of applying the contextualized exercise to First Year group of engineering students.