Abstract:

With the growing adoption of Artificial Intelligence in higher education, universities worldwide have been investing in technological tools to enhance teaching and learning. However, much of this attention has been directed at the student learning experience (tutoring, ethical use of AI by students, simulations) while far less focus has been placed on the faculty learning process. Most professors have never been formally prepared to understand how learning works. Higher education faculty are, for the most part, specialists and researchers in their respective fields, with little or no training in Learning Science. At the same time, few Higher Education Institutions invest in effective faculty development efforts aimed at building this competency. Without mastering the fundamentals of learning management, the foundation of Assurance of Learning, faculty tend to receive training on how to use AI merely to optimize traditional teaching practices or craft better prompts, rather than to genuinely transform the student learning experience.

This paper presents the PDAF Framework (Planning, Dynamics, Assessment, and Feedback), a learning-centered approach to faculty development that places professors at the center of their own learning process. Developed at Insper, a leading Brazilian business school, the framework emerged from a qualitative study involving 109 faculty members, using design thinking methodology to map their real development needs. Rather than training professors on teaching methods, the PDAF proposes that faculty experience in their own development the very same principles they are expected to apply in the classroom: continuous cycles of planning, experimentation, assessment, and feedback.

The framework has been applied in the development of more than 600 faculty members and professionals across multiple institutional contexts, including universities, business schools, edtechs, and corporate organizations in Brazil, Chile, and Portugal, demonstrating its adaptability to different cultures and educational realities.

Over nearly a decade of implementation, the framework has consolidated an institutional culture of faculty learning, with measurable outcomes through descriptive rubrics, systematic classroom observations, and individual development plans. This paper argues that, in the era of AI in education, the most urgent competency for faculty is not learning how to use technological tools, but rather understanding the science of learning, and that the PDAF offers a replicable path for this development, with proven potential for adaptation across diverse institutional and cultural contexts.

Keywords:

Faculty development, learning management, learning science, assurance of learning, higher education, Artificial Intelligence in education.