Artificial Intelligence as a Catalyst for Changes in University-Level Science Education

Artificial intelligence is widely seen not just as a technological advancement in education, but as a catalyst for rethinking the epistemological, pedagogical, and institutional foundations of university-level science instruction. This article is a theoretical and literature-based study that does not present empirical data but instead synthesizes existing research and conceptual frameworks to explore AI’s transformative potential. It examines how AI can redefine university-level science education across five interconnected dimensions: cognition, epistemology, pedagogy, institutional de-sign, and ethics. Utilizing recent studies in science education, educational technology, and artificial intelligence, the paper contends that intelligent systems – such as adaptive tutors, learning analytics platforms, and generative models – can facilitate inquiry-based, student-centered learning environments that are congruent with genuine scientific practices. Nonetheless, their integration presents significant issues related to teacher autonomy, algorithmic bias, and institutional equity. The article contextualizes AI within extensive dialogues on knowledge creation and educational reform, promoting a values-oriented strategy that harmonizes technological advancement with humanistic objectives. This work reframes AI as a philosophical lens rather than merely a tool, contributing to the redefinition of science education’s future in a swiftly changing digital environment.