Abstract Book of the 9th World Conference on Teaching and Education
Year: 2025
[PDF]
Beyond Task Delegation: Reconceptualizing Instructional Design to Measure Competence in The Age of Generative AI
Jasmine Fernandez
ABSTRACT:
The rise of Large Language Models (LLMs) fundamentally invalidates content-recall assessments, serving as the catalyst enforcing the definitive shift toward Competency-Based Education (CBE). This paper argues that the faculty role must move beyond content delivery to engineering and assessing observable competencies, specifically focusing on Metacognition and Self-Regulated Learning (SRL)—the AI-proof skills that govern strategic human-AI partnership. Drawing on Behaviorism (Skinner, 1974) and Bloom’s Taxonomy, we propose a novel instructional design framework centered on the Reverse-Flipped Model, where AI handles low-stakes production tasks, freeing pedagogical time for critical judgment and refinement. This framework mandates the use of Analytic Rubrics and modality-specific data capture (e.g., Automated Process Portfolios) to quantify skill demonstration (Prompt Literacy, intervention) over final product quality. The paper concludes by detailing institutional implications, including faculty workload support and ensuring equity in this transition, thereby redefining success through verifiable student outcomes, not passive proxy measures.
Keywords: Generative AI, Competency-Based Education (CBE), Metacognition, Self-Regulated Learning (SRL), Instructional Design, Reverse-Flipped Model, Assessment.