Faculty Learning Communities
ASCEND-AI’s Faculty Learning Communities (FLCs) serve as the primary vehicle for faculty development, employing a facilitative rather than lecture-based instructional model. Guided by the ‘Learner’s Arc: From Understanding to Innovation’ framework, participating faculty progress through iterative stages of developing their ‘AI identity’—building competency across mind (technical understanding), heart (ethical frameworks), soul (disciplinary integration), and courage (innovative application) components. This approach prioritizes psychological safety and reflection-based assessment, ensuring that faculty engagement leads to genuine pedagogical transformation rather than surface-level tool adoption.
A Brief on FLCs
What are Faculty Learning Communities?
Faculty Learning Communities (FLCs) are groups of faculty from diverse disciplines who engage in sustained, collaborative exploration of teaching, learning, and scholarly practice. Unlike traditional professional development workshops that offer one-time training sessions, FLCs create year-long (or longer) spaces for ongoing dialogue, experimentation, reflection, and peer learning.
FLCs are grounded in communities of practice theory and constructivist learning principles, recognizing that meaningful professional development occurs through active engagement with new ideas, experimentation in authentic contexts, and reflection within supportive peer networks. Faculty learn not just from expert presentations but from each other's experiences, challenges, and innovations.
Faculty Learning Communities in ASCEND-AI
ASCEND-AI employs Faculty Learning Communities as its primary vehicle for faculty professional development in AI literacy. This approach is particularly well-suited to AI integration because:
Complexity Requires Sustained Engagement
AI literacy encompasses technical understanding, pedagogical innovation, ethical frameworks, and discipline-specific application—too complex for one-time workshops. FLCs provide time for deep learning and iterative refinement.
Rapid Evolution Demands Collaborative Learning
AI technologies change faster than traditional professional development can keep pace. FLCs create peer learning networks where faculty collectively stay current and share emerging practices.
Discipline-Specific Applications Benefit from Interdisciplinary Dialogue
AI manifests differently in humanities, social sciences, natural sciences, and professional programs. FLCs allow faculty to identify both common frameworks and discipline-specific considerations.
Uncertainty Requires Safe Spaces
Many faculty are uncertain about AI integration, what to teach, how to address academic integrity, when AI enhances versus undermines learning. FLCs provide judgment-free environments for exploring difficult questions.
Cross-Institutional Collaboration Extends Impact
ASCEND-AI FLCs connect Howard and Bowie State faculty, creating reciprocal capacity building where both institutions' expertise strengthens collective outcomes. This model can extend to other HBCU partnerships.
ASCEND-AI FLC Structure
Year-Long Commitment
Faculty commit to participating in monthly FLC sessions throughout the academic year, with summer opportunities for intensive curriculum development work. New cohorts join annually, creating expanding networks of AI literacy expertise across both institutions.
Five Module Focus Areas
FLC work is organized around ASCEND-AI's five programmatic areas: (1) foundational AI literacy, (2) responsible use and academic integrity, (3) detecting misinformation and hallucinations, (4) discipline-specific instruction, and (5) AI-driven entrepreneurship. Faculty engage deeply with each area while developing curriculum materials for implementation.
Curriculum Development Emphasis
Rather than passive learning, FLC participants actively create instructional materials, assessment tools, and assignment frameworks that can be adapted across disciplines. These materials become shared resources available to faculty beyond FLC participation.
Reciprocal Capacity Building
Howard and Bowie State faculty bring different institutional contexts, pedagogical traditions, and student populations. Cross-institutional dialogue strengthens both institutions' approaches while creating collaborative relationships that extend beyond the grant period.
Student Voice Integration
FLC curriculum development is informed by ongoing student feedback through surveys, focus groups, and student advisory panels. This ensures faculty work addresses actual student needs rather than faculty assumptions about what students require.
Assessment and Evaluation
External evaluation by BrickRose Exchange tracks FLC effectiveness through faculty professional development outcomes, student learning gains in courses taught by FLC participants, and sustainability of AI literacy integration beyond the initiative.