Assessing the Academic Impact of Generative AI in Higher Education: Student Perceptions and Program Outcome Attainment

Abstract

Goal: This study investigates the pedagogical impact of integrating Generative Artificial Intelligence (GenAI) into design and engineering education, focusing on student perceptions, academic outcome attainment, and anxiety reduction during skill acquisition. Design/Methodology: Utilizing an Outcome-Based Education (OBE) model, we mapped hands-on sketching activities utilizing AI-generated visual scaffolding to Program Outcomes (POs). Attainment levels were evaluated using a weighted combination of direct academic performance (80%) and indirect student surveys (20%). Qualitative thematic analysis was applied to student reflections. Results: Quantitative evaluation showed high Program Outcome attainment, specifically in engineering knowledge and modern tool usage (Level 3, >80%). Students reported significant reductions in anxiety (“blank page syndrome”) and enhanced learning autonomy. Contribution: This study provides empirical evidence of the value of GenAI as a structured educational scaffolding rather than a copy tool, establishing a roadmap for outcome-driven AI integration in higher education.