About the Program

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.

Roles & Responsibilities

Facilitators

Facilitators guide the Faculty Learning Communities using a Communities of Practice model grounded in peer-to-peer, collaborative learning. They lead cohort sessions, support module implementation, and connect faculty engagement to program leadership and evaluation.

Key responsibilities include:

• Facilitate FLC cohort sessions (bi-weekly, 90 minutes)

• Review AI literacy modules (Modules 0–5) and provide feedback

• Support faculty engagement with Module 4 applications in teaching practice

• Bridge cohort discussions with PI/Co-PI leadership by documenting key themes and insights

• Participate in evaluator debrief sessions with BrickRose Exchange

• Support integration of student AI Ambassador programming

• Attend all FLC sessions and the annual ASCEND-AI Symposium

Subject Matter Experts (SMEs)

SMEs ensure disciplinary rigor and instructional quality of the AI literacy modules while supporting implementation and evaluation within their academic contexts.

Key responsibilities include:

• Complete all AI literacy modules (Modules 0–5) via LMS

• Review modules for disciplinary accuracy, pedagogy, and alignment

• Integrate at least one module into a course each academic term

• Participate in FLC sessions and pre-session SME briefings

• Administer IRB-approved pre/post student assessments

• Provide structured feedback using evaluation rubrics

• Submit teaching artifacts (assignments, syllabus excerpts, etc.) to the ASCEND-AI repository

• Participate in evaluation interviews when selected

• Attend the annual ASCEND-AI Symposium

Faculty Learning Community Members

FLC Members participate in collaborative learning, module implementation, and reflective practice to advance AI literacy and responsible use across disciplines.

Key responsibilities include:

• Complete all AI literacy modules (Modules 0–5) on schedule

• Actively engage in FLC sessions and reflective activities

• Integrate at least one AI literacy module into a course each term

• Document student learning outcomes and instructional adaptations

• Administer IRB-approved pre/post student surveys

• Submit evaluation instruments and feedback forms on time

• Contribute teaching materials to the ASCEND-AI shared repository

• Participate in evaluation interviews when selected

• Attend FLC sessions and the annual ASCEND-AI Symposium

FLC Participation

1. Year-Long Commitment

   Faculty commit to participating in monthly FLC sessions throughout the academic year, with biweekly summer opportunities for intensive curriculum development work. New cohorts join annually, creating expanding networks of AI literacy expertise across both institutions.

2. Five Module Focus Areas

   Participants complete five core modules:

   • AI Literacy & Fundamental Concepts

   • Responsible AI Use & Ethical Foundations

   • Detecting Misinformation, Bias & Hallucinations

   • Discipline-Specific Integration

   • AI Innovation & Stewardship

3. 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.

4. 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.

5. 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.