A QUANTITATIVE ANALYSIS OF RECENT ADVANCES IN GENERATIVE AI AND THEIR IMPLICATIONS FOR PEDAGOGICAL INNOVATION - Restian, Arina; Belhassen, Yassine; Johnson, Zoe; Brown, Emma (Al-Hijr: Journal of Adulearn World, 2025)
The rapid proliferation of Generative AI (GenAI) presents a significant disruption to higher education. However, the academic discourse remains largely speculative, lacking large-scale, quantitative data on how recent AI advancements actually correlate with pedagogical innovation. This study quantitatively analyzes the relationship between the adoption of specific GenAI capabilities and the implementation of innovative pedagogical practices, modeling their subsequent impact on student learning outcomes. A cross-sectional, quantitative survey design was deployed, collecting data from N=1,245 faculty and N=3,512 students across 42 institutions using two validated instruments: the Generative AI Capabilities Adoption Scale (GACAS) and the Pedagogical Innovation Inventory (PII). The data reveals a significant misalignment between high student adoption and low faculty integration for advanced tasks. Faculty adoption is strongly skewed toward administrative efficiency rather than pedagogical redesign. Crucially, the analysis identified a “pedagogical mediation” effect: GenAI use showed no direct correlation with critical thinking (r=.04), but the relationship was polarized by faculty strategy, showing a positive correlation in high-innovation courses (r=.22) and a negative trend in low-innovation courses (r=-.17). The impact of Generative AI on learning is not deterministic; it is decisively mediated by faculty-led pedagogical innovation. The educator's role in designing cognitive frameworks, rather than the technology itself, is the critical factor for successful integration.