This comment builds on the example of chat generative pretrained transformer (ChatGPT) to discuss the implications of generative AI on industry and higher education, underlining the need for more transdisciplinary digital literacy education. The release of ChatGPT has generated significant academic and professional interest and instigated a vibrant discussion on the opportunities offered and challenges posed by powerful and readily accessible generative AI reshaping teaching and learning at universities. ChatGPT has reignited an age-old debate on the impact of disruptive technologies on occupations and the labor market, but recent discussions have paid little attention to how university offerings may need to adapt. We strive to open this discussion arguing that while recent GPT technology has, indeed, made more conceivable the substitution of many tasks of white-collar and knowledge workers, and suggested an acceleration of the labor market shift towards technology-centric occupations, it has simultaneously made a stronger-than-ever case for transdisciplinary competences. Consequently, we emphasize the need to foster more transdisciplinary digital literacy in universities with curricula that provide breadth of knowledge and flexibility of mind, bridging humanities with STEM disciplines. Digital humanities education is in a unique position to promote the responsible use of generative AI, while encouraging critical reflection on its socio-cultural embeddedness.

This study explores the multifaceted factors influencing student learning motivations and educational outcomes. Utilizing a diverse student body from Franklin University Switzerland, the study emphasizes the impact of internal factors, such as the psychological state of flow and a self-reported love of learning, alongside GPA and student cohort influences like year of study, academic discipline, country of origin, and academic travel. Through a cross-sectional survey of 112 students, the study evaluates how these factors correlate with and diverge from each other and student GPAs, aiming to dissect the influences of intrinsic motivations, demographic variables, and educational experiences. Our analysis revealed significant correlations between students’ self-reported love of learning, experiences of flow, and academic performance. Conversely, academic travel did not show a significant direct impact, suggesting that while such experiences are enriching, they do not necessarily translate into a greater love of learning, flow, or higher academic achievement in the short term. However, demographic factors, particularly discipline of study and country of origin, significantly influenced the students’ love of learning, indicating varied motivational drives across different cultural and educational backgrounds. This study provides valuable insights for educational policymakers and institutions aiming to cultivate more engaging and fulfilling learning environments.