IEEC: A Framework for AI Literacy in Programming
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Abstract
The integration of generative artificial intelligence (GenAI) into higher education presents both opportunities and challenges, particularly in skill-based fields like computer programming. While tools such as ChatGPT can support learning, educators need structured methods to harness their benefits while addressing concerns like over-reliance and ethical misuse. This article introduces and evaluates the IEEC (Introduce, Encourage, Evaluate, Control) framework, developed to guide the thoughtful integration of GenAI tools in programming education. The framework aims to build foundational programming competence alongside essential AI literacy – defined as the ability to use GenAI critically, ethically, and effectively. Implemented in an introductory programming course (N = 60) at Universiti Sultan Zainal Abidin, Malaysia, the study employed a mixed-methods, quasi-experimental pre/post design. Quantitative data included surveys measuring programming self-efficacy, AI attitudes, ethical awareness, and assessment performance related to evaluating GenAI outputs. Qualitative data were drawn from focus group interviews. Findings show statistically significant gains (p < 0.05) in programming self-efficacy and students’ ability to critically assess AI-generated outputs. Qualitative analysis revealed increased student engagement (Encourage) and deeper reflections on GenAI’s capabilities and ethical implications (Introduce/Control). Although students demonstrated greater ethical awareness, challenges in applying ethical principles persisted (Evaluate/Control), and concerns about AI output variability remained. Overall, the IEEC framework supported confidence, critical evaluation skills, and ethical mindfulness alongside technical competence. This study contributes a carefully evaluated pedagogical model for integrating GenAI in programming education and offers a contextually grounded approach to cultivating responsible AI use in technical learning contexts. While the findings are promising, further research across diverse institutions and disciplines is needed to determine the broader applicability of the framework.
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