Paper-first knowledge engineering as a pedagogical framework for cognitive agency in AI-assisted learning

Abstract

This study investigates how a structured “paper-first” approach can support cognitive engagement during artificial intelligence (AI)-assisted academic work in higher education. Growing reliance on generative AI has increased concerns about surface-level learning, trial-and-error prompting, and reduced student ownership of academic writing. Using a two-year design-based research approach involving nine iterative cycles, the study developed and refined a framework that required students to construct manual logic diagrams before engaging with AI tools and expert system environments. Participants included a six-member interdisciplinary design team and 150 communication for academic purposes students from the faculty of information and communications technology at a South African university. Data were collected through iterative observations and focus group interviews and analyzed using grounded theory and constant comparative analysis. Findings indicate that the framework supported more structured reasoning, reduced inefficient prompting behaviors, and improved students’ ability to identify weaknesses in their own arguments before digital interaction. Students also reported increased confidence and greater ownership of their academic work. The study argues that structured pre-AI planning activities can strengthen critical thinking and academic literacy by positioning AI as a support tool rather than a substitute for cognitive effort. The findings contribute practical guidance for educators seeking to integrate AI into higher education while preserving student agency and deep learning.