| Sumario: | The current research examines how the domains of data science and artificial intelligence (A.I.) (collectively termed data-based A.I.) could improve teaching and learn in higher education. The current research raises awareness of the paths, dangers, and opportunities of data-based A.I. with a thrust on teaching and learning. Thus, it reacts to the teaching of programming to non-science disciplines through A.I., data science, and big data processing. The paper links A.I. and data science with pedagogy and curriculum design. From a multidisciplinary perspective, the paper explores the applications of data-based A.I. to inform students' learning and how higher education institutions teach and develop. Connecting with and reacting to the challenges faced, the author examines some models for teaching, learning, student support, and administration. Conclusively, the author argues for a data-based AI-enabled pedagogical approach. Instead of replacing teachers or administrators or using teacherbots for teaching and learning, data-based A.I. in higher education should extend human abilities in teaching, learning, and research with relevant administrative and leadership roles. Given the intercom-nectedness of data-based A.I., pedagogy, and curriculum design, the implycation from the findings of the research is thus that instead of education being a technology-centric endeavour, it should be human-centric, with humancentric- machine solutions. This approach allows humans to identify and critique human-centric risks and solutions continuously; hence, the universities would have to encourage and ensure that it nurtures creativity by maintaining academic skepticism as a health-check process in education.
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