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Despite its recognition, the field of entrepreneurship faces significant challenges, particularly the fragmentation of knowledge due to excessive contextualization. In this context, Artificial Intelligence (AI), with its ability to identify patterns from data, holds the potential to address some of these challenges. Consequently, this article aims to explore the application of AI methods and techniques in entrepreneurship research and their impact on the field’s current knowledge base. Focusing on the area of student entrepreneurial intentions – a topic that encapsulates the challenges of entrepreneurship research – we conducted a scoping review of recent AI-based academic work in this subfield to discern emerging directions and trends. Specifically, the results indicate: (1) The predominant trend in this research domain is the use of classification techniques within the context of supervised machine learning (i.e. learning with significant human intervention); (2) These studies reaffirm established concepts in the research on students’ entrepreneurial intentions as well as the elusive and complex nature of entrepreneurial intentions and behaviors. Meanwhile, a few pioneering yet scarce insights come from analyzing extensive datasets over long durations; and (3) This work heralds a trend towards “statistical augmentation” of reality, aiming to enhance the accuracy of predictive models. This indicates a resurgence of the culture of quantification and a new wave of positivism in entrepreneurship research, devoid of preconceived models. These outcomes suggest that the increasing adoption of AI methodologies in this sphere may raise questions concerning the essence of reality and the scientific validity of the knowledge generated. JEL Codes: L26, N01, I23, O33, M13