Citation

Rajendran, Venushini and Ramasamy, R. Kanesaraj (2024) Artificial Intelligence Integration in Programming Education: Implications for Pedagogy and Practice. : Lecture Notes in Electrical Engineering ((LNEE,volume 1262)), 1262. pp. 197-206. ISSN 1876-1100

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Abstract

In the swiftly evolving domain of programming education, Artificial Intelligence (AI) has emerged as a transformative force, prompting a reevaluation of pedagogical strategies and educational practices. This paper critically examines the integration of AI technologies in programming education and elucidates their pedagogical implications. With the accelerating development of programming languages and frameworks, traditional educational models struggle to maintain relevance. AI-driven intelligent search engines and recommendation systems are now pivotal in curating personalized educational content, thereby enhancing learning efficiency and reducing learner frustration. Furthermore, AI-facilitated platforms have redefined the conventional classroom, introducing virtual instructors and democratizing access to programming knowledge. Significantly, Natural Language Processing (NLP) technologies have reduced barriers to entry, enabling individuals with limited technical backgrounds to interact with programming interfaces in vernacular language. This has facilitated broader participation and has scaffolderd the learning process. Additionally, AI interventions have automated aspects of code generation and provided instantaneous feedback mechanisms, essential for real-time error correction and debugging, thus fostering a supportive learning environment. The collaborative aspects of AI are manifested through peer-to-peer learning ecosystems, bolstered by forums and code-sharing platforms. The synthesis presented in this paper highlights how AI not only enriches the learning experience but also promotes inclusivity and engagement in programming education. The implications of these AI-driven pedagogical innovations are vast, suggesting a paradigm shift towards more adaptive, personalized, and collaborative learning frameworks. This paper contributes to the discourse by not only charting the current landscape but also projecting the future trajectory of AI’s role in programming education.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Feb 2025 05:18
Last Modified:	06 Feb 2025 05:18
URII:	http://shdl.mmu.edu.my/id/eprint/13363

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