Augmenting Pedagogical Absorptive Capacity: Designing an AI-Powered Case Study Builder to Support Sensemaking in Executive Education

Read full text Summarise with VIRAT
Authors: Taiwo Adedeji, Qudus Oladeji
Cited by: 0

Summary

No summary yet. VIRAT can read this paper and write a plain-language summary of its aim, methods, key findings and limitations.

Summarise with VIRAT Free to try with your Science Catalogs account.

Abstract

The transition to Industry 5.0 places executive education under mounting pressure to equip leaders with the competenciesrequired to navigate digital transformation, sustainability imperatives, and human-centric values simultaneously. Acritical bottleneck impedes this mission: educators’ well-documented capacity to acquire and assimilate externalknowledge is seldom matched by a corresponding capacity to transform and exploit that knowledge through pedagogicallyeffective teaching materials. This conceptual study addresses the gap by introducing pedagogical absorptive capacity, amicro-foundational capability that extends the PACAP–RACAP framework to the educator-as-knowledge-integrator,and proposes generative AI as the primary mediating mechanism bridging the two dimensions. Employing DesignScience Research (DSR) methodology structured around the six-phase Peffers process model and evaluated againstHevner’s seven guidelines, the paper presents the design of an AI-powered Case Study Builder. Human-AI symbiosisis theorised to operate through three discrete mechanisms: augmented problem sensing, which extends educators’perceptual reach across diverse knowledge domains; scaffolded solution assembly, which reduces the cognitive viscosityof narrative construction while preserving educator agency; and accelerated validation and reflection, which compressesiterative feedback cycles from weeks to minutes. The artifact embodies five design principles, augment rather thanautomate; support iterative refinement; provide structure with customisation; build in feedback loops; and designfor diverse institutional contexts, derived from the theoretical framework and formulated as transferable prescriptiveknowledge. The paper contributes an extension of absorptive capacity theory to pedagogical contexts, a mechanism-levelaccount of human-AI knowledge integration, and actionable design guidance for business schools seeking to enhancefaculty capability in the Industry 5.0 era.

Fields of Study & Subjects

Artificial Intelligence in Education Management and Marketing Education Knowledge Management in Higher Education

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
16 March 2026
Language
EN