From Python to Generative AI: An Exploratory Course-Based Study Developing the INSPIRE Framework for Creativity and Professional Readiness in Computing Education

Abstract

This exploratory study examines the implementation of a GenAI-supported curriculum redesign in a project-based multimedia computing course by comparing students’ experiences in Python-based and GenAI-supported production pathways in relation to creativity, applied digital competence, and perceived professional readiness. A mixed-methods, quasi-experimental design was employed to compare two naturally occurring undergraduate cohorts at a Gulf-region university. Data was collected from 29 students through post-course surveys, open-ended reflections, project reports, student-generated artefacts, and instructional observations. Quantitative data were analyzed using nonparametric statistical tests and effect size reporting, while qualitative evidence was examined through inductive thematic analysis supported by human-in-the-loop AI-assisted coding. The findings indicate that the GenAI-supported cohort reported significantly higher project satisfaction, perceived ease of use, and job relevance, with additional positive trends in creativity and professional output. Qualitative evidence and illustrative project examples further suggest that students experienced fewer technical barriers and greater opportunities for multimodal production, particularly in community-facing projects. Based on these findings, the study presents the INSPIRE framework, a seven-stage, practice-informed conceptual model for integrating GenAI into project-based computing education. Although limited by a small sample and single-institution context, the study provides exploratory comparative evidence and a practice-informed framework that offers actionable guidance for responsible and structured GenAI integration in higher education.