Reframing faculty development for digital transformation and generative AI in higher education

Area of Science:

• Educational Technology
• Higher Education Studies
• Faculty Development

Background:

• Digitalization and generative AI are transforming higher education teaching.
• Current faculty development often uses standardized training, failing to meet diverse teacher needs across career stages.
• There's a need for differentiated support in digital competence, curriculum innovation, and leadership.

Purpose of the Study:

• To systematically account for stage-specific developmental tasks of university faculty in the digital era.
• To advance a multi-level, developmental framework for faculty professional development.
• To guide faculty development beyond tool training towards pedagogical and organizational transformation.

Main Methods:

• Explicit review questions, database-based literature retrieval, and thematic synthesis informed the framework.
• Scholarship on teacher digital competence, professional development theory, and digital transformation in higher education was synthesized.
• The framework was formulated within the institutional context of Heilongjiang University of Chinese Medicine.

Main Results:

• A multi-level, developmental framework for faculty development in the digital era was proposed.
• The framework is structured around four stages: entry and adaptation, stable development, deep integration, and academic leadership.
• Implementation pathways include blended learning, cross-boundary linkage, community learning, mentoring, and evidence-informed practice.

Conclusions:

• Faculty development should shift from tool-centric training to systematic capacity building in pedagogy, digital ethics, and leadership.
• The proposed framework enhances stage-specific relevance, practical application, and organizational integration of faculty development.
• This provides a theoretical reference for designing faculty development systems in digitally transformed universities.