A grounded theory approach to profiling student competencies in GAI-based teaching and learning environments

Objective The aim of this research is to design a model for identifying student competencies in using Generative Artificial Intelligence (GAI) in teaching and learning environments, based on Grounded Theory.Methodology Due to the limited prior research in this area, we utilized the systematic grounded theory approach developed by Strauss and Corbin (1997). We conducted interviews with 18 international students at the public university in Iran, employing a purposive sampling method combined with a random selection strategy.Findings The research results, encompass several dimensions: causal factors such as technological advancements, biological shifts, and changes within the higher education system; intervening variables that include political and economic influences; contextual elements reflecting the evolution of societal and academic cultures; actions and strategies focused on enhancing human capital, establishing legal frameworks, creating incentive and regulatory systems, targeted policymaking, and improving infrastructure; and developmental outcomes that incorporate improved educational and research performance at universities, along with personal development.Innovation This research explores uncharted territory by investigating students' skills in using GAI within higher education. The findings offer valuable insights for enhancing students' capacity to effectively utilize GAI for educational purposes and support the smooth integration of artificial intelligence technologies into higher education systems.