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All Journal Journal of Computing Theories and Applications Methods in Science and Technology Studies
Adesola M. Falade
McPherson University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Impact of Generative AI on Student Learning in Higher Education using Robust Assessment Metrics Framework Adesola M. Falade; Ayobami E. Mesioye
Methods in Science and Technology Studies Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/msts.v2i1.2026.415

Abstract

The rapid emergence of Generative Artificial Intelligence (GAI) has transformed the landscape of higher education, influencing pedagogy, assessment, and student learning experiences. Despite its widespread adoption, a significant research gap persists regarding the empirical measurement of its impact on specific learning outcomes. While GAI tools are widely adopted, existing assessment frameworks often fail to distinguish between machine-generated efficiency and genuine cognitive development. This study addresses this gap by developing the Robust Assessment Metrics Framework (RAMF), evaluated through a mixed-methods approach involving students and faculty (N=295) at McPherson University. Quantitative findings reveal a significant "Efficiency-Cognition Trade-off": while frequent GAI usage significantly enhances task efficiency (p < 0.001), it correlates with a statistically significant decline in critical thinking (p < 0.01) and self-reported originality (p < 0.01). Interestingly, regression analysis shows that AI literacy and institutional policy clarity are stronger predictors of academic confidence than usage frequency. This suggests a psychological "confidence paradox" where students feel more capable despite lower cognitive engagement. Qualitatively, thematic analysis highlights a shift toward "shortcut learning" that necessitates a move from product-oriented to process-oriented evaluation. The RAMF introduces expert-validated protocols such as the ‘30/70 Synthesis Rule’ and "Process Logs," to safeguard academic rigor. This research provides institutional leaders with an expert-validated framework proposed for institutional trial to shift from product-oriented to process-oriented assessment in the AI era. By focusing on the interplay between human agency and algorithmic assistance, this research offers broader implications for pedagogical redesign in an AI-saturated academic environment.
ArchEvolve: A Collaborative and Interactive Search-Based Framework with Preference Learning for Optimizing Software Architectures Ayobami E. Mesioye; Adesola M. Falade; Kayode E. Akinola
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14990

Abstract

The use of Search-Based Software Engineering (SBSE) for optimizing software architecture has evolved from fully automated to interactive approaches, integrating human expertise. However, current interactive tools face limitations: they typically support only single decision-makers, confine architects to passive roles, and induce significant cognitive fatigue from repetitive evaluations. These issues disconnect them from modern, team-based software development, where collaboration and consensus are crucial. To address these shortcomings, we propose "ArchEvolve," a novel framework designed to facilitate collaborative, multi-architect decision-making. ArchEvolve employs a cooperative coevolutionary model that concurrently evolves a population of candidate architectures and distinct populations representing each architect's unique preferences. This structure guides the search towards high-quality consensus solutions that accommodate diverse, often conflicting, stakeholder viewpoints. An integrated Artificial Neural Network (ANN) serves as a preference learning module, trained on explicit team feedback to act as a surrogate evaluator. This active learning cycle substantially reduces the number of required human interactions and alleviates user fatigue. Empirical evaluation on two industrial case studies (E-Commerce System and Healthcare Management System) compared ArchEvolve to a state-of-the-art interactive baseline. Results indicate that ArchEvolve achieves statistically significant improvements in both solution quality and consensus-building. The preference learning module demonstrated over 90% accuracy in predicting team ratings and reduced human evaluations by up to 46% without compromising final solution quality. ArchEvolve provides a practical, scalable framework supporting collaborative, consensus-driven architectural design, making interactive optimization a more viable and efficient tool for real-world software engineering teams by intelligently integrating cooperative coevolutionary search with a preference learning surrogate.
Co-Authors Ayobami E. Mesioye Kayode E. Akinola