From AI Ideas to Institutional Priorities: A Case Study of a Faculty-Led Pipeline for AI-Enhanced Learning

Abstract

Background: Generative artificial intelligence has accelerated the diversity of proposed educational innovations, creating a practical challenge: how to evaluate, compare, and prioritize AI-enhanced learning ideas transparently. Existing guidance often emphasizes instructor-level decisions, while fewer models address institutional prioritization. Purpose: This case study describes a faculty-led process for identifying, prioritizing, refining, and recommending AI-enhanced learning use cases for leadership consideration. Approach: A teaching and learning AI task force representing seven professional schools developed a five-stage pipeline: structured ideation, transparent scoring and prioritization, deliberation and consensus building, use case refinement, and leadership-facing submission. This workflow supported proposal comparability, disciplined prioritization, and equity review before institutional decision-making. Outcomes: The task force evaluated 24 proposals and 234 scored entries. Bounded AI-assisted analysis supported score synthesis, comment-theme identification, and thematic grouping, informing rather than replacing faculty decision-making. Following deliberation, related high-priority proposals were synthesized into three categories for refinement: course-grounded virtual teaching assistants, simulation practice engines, and faculty augmentation systems. Implications: The pipeline offers a transferable model for managing innovation overload, evaluating innovation potential, and advancing responsible AI-enhanced learning.