This paper presents a second-cycle evaluation of a foundation-year Introduction to Programming module that integrated Khan Academy, generative AI tools, interactive classroom activities, and game-based learning within a holistic, student-centred teaching framework. Building upon the findings and limitations identified during the first delivery cycle, the module was refined through iterative changes to lesson pacing, Blackboard structure, practical scaffolding, quizzes, commented coding exercises, and responsible AI guidance. The module was delivered to mixed-ability cohorts across computing-related disciplines and combined approximately 60 hours of face-to-face teaching with self-paced digital learning and practical programming activities. Students engaged with Python programming through game-inspired exercises, pair programming, incremental development tasks, and a final coursework project involving the creation of a functional game prototype. Compared to the previous delivery cycle, observational findings and module performance data suggest improved student confidence, stronger engagement, and more technically ambitious project outcomes. Average marks increased from 59.4% to 63.9%, while the percentage of students passing submitted work increased from 90.0% to 94.9%. Students also demonstrated greater willingness to experiment beyond tutorial examples, with some learners independently publishing projects on platforms such as itch.io as part of their growing professional portfolios. The paper discusses the evolving role of generative AI within introductory programming education, particularly the importance of guided and reflective usage to support conceptual understanding rather than shortcut-driven learning. Findings suggest that iterative refinement of digital pedagogies, combined with structured support and practical game-based learning, can positively contribute to engagement, inclusivity, and confidence within foundation-year computing education.
Mario Santos (2026) studied this question.
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