The use of generative AI (genAI) in higher education is rapidly evolving, provoking both optimism and concern among educators. While some students embrace genAI tools as learning aids, others, including many educators, remain cautious about their implications for critical thinking and academic integrity. Accepting that genAI is readily available and banning its use is not feasible, we explore how it might be integrated meaningfully into pedagogy through the lens of Transformative Learning Theory (TLT). This study investigates how genAI tools influence student learning in an IT Forensics course using diverging assessments, a form of assessment-as-learning where students receive the same authentic tasks but unique data inputs. We examine three research questions addressing genAI’s impact on learning strategies, its role in supporting assessment-as-learning tasks, and how it fosters critical reflection and transformation in student learning. Drawing on interviews with 14 students, our findings suggest that, when scaffolded appropriately, genAI use within diverging assessments can catalyze transformative learning by provoking disorienting dilemmas, encouraging reflection, and reshaping problem-solving approaches. Finally, implications for teaching practice and assessment design are discussed.