Aug 2026· e-Jurnal Penyelidikan dan Inovasi· Vol 13, pp. 290-297· 0 citations· 17 references
TL;DR
It is concluded that AI-powered development tools provide significant advantages in enhancing students’ coding efficiency; however, their application should be supported by well-designed teaching strategies to achieve balanced learning outcomes.
Abstract
Artificial intelligence (AI) has become an integral component of modern software development using AI-powered coding assistants, automated debugging tools, and intelligent programming environments. In higher learning institutions, students increasingly adopt the tools to support programming tasks and enhance coding performance. This study presents a narrative review of existing literature on the effects of AI-powered development tools on students’ coding efficiency in higher learning institutions. The review focuses on studies published between 2022 and 2026 and retrieved from major academic databases, including Scopus, IEEEXplore, and ScienceDirect, using predefined keywords and inclusion criteria. The selected studies were analyzed using thematic analysis to systematically identify and categorize recurring patterns across the literature, focusing on coding efficiency, learning outcomes, and challenges associated with AI-assisted programming tools. The findings indicate that AI-powered development tools significantly improve coding efficiency by increasing development speed, reducing syntax and logical errors, and enhancing debugging processes. In addition, these tools support learning by providing real-time code suggestions and improving students’ problem-solving capabilities. However, certain limitations have been identified, including overdependence on AI-generated code, reduced independent thinking, issues regarding academic integrity, and limited conceptual understanding of programming fundamentals. The study concludes that AI-powered development tools provide significant advantages in enhancing students’ coding efficiency; however, their application should be supported by well-designed teaching strategies to achieve balanced learning outcomes. Future research should examine the long-term impact of AI-assisted programming on students’ technical competence, critical thinking, and professional readiness in software development.
This study examines the growing influence of artificial intelligence (AI) tools on student research in higher education, focusing on both their opportunities and challenges, and recommends comprehensive institutional guidelines, targeted training programs, and ethical frameworks to ensure responsible integration of AI.
Edelresa S Juachon, Honorata Yaneza, Arthur Cabradilla et al.· JPAIR Institutional Research· 0 citations
Artificial Intelligence (AI), particularly generative AI based on Large Language Models (LLMs), has rapidly transformed the execution of knowledge-intensive activities across multiple domains. AI-powered tools such as ChatGPT, GitHub Copilot, Microsoft Copilot, Google Gemini, Claude, and Notion AI have increasingly been adopted to automate repetitive tasks, support decision-making, accelerate software development, and improve content production. However, despite the rapid expansion of these technologies, scientific evidence regarding their effectiveness as productivity enhancers remains distributed across different research areas. This study presents a systematic literature review aimed at synthesizing current evidence on the role of AI tools in improving productivity in business, education, software engineering, and scientific research. A structured literature search was conducted across major academic databases, including Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Scopus. Studies published between 2020 and 2026 were analyzed according to predefined inclusion and exclusion criteria. The reviewed literature indicates that AI tools can improve productivity by reducing task completion time, assisting knowledge creation, supporting programming activities, and enhancing information processing. Nevertheless, significant challenges remain, including inaccurate outputs, algorithmic bias, privacy concerns, ethical risks, and excessive reliance on automated systems. The findings suggest that AI achieves the greatest productivity benefits when used as a collaborative technology that augments human capabilities rather than replacing human expertise. Future research should investigate long-term productivity impacts, organizational adaptation strategies, responsible AI governance, and the integration of advanced multimodal AI systems into professional workflows.
The development of generative artificial intelligence and large language models (LLMs) has enabled significant progress in automatic code generation and the application of AI systems in education and software engineering. This paper presents fundamental concepts of generative artificial intelligence, the importance of prompt engineering, prompting techniques and strategies for improving the quality of responses generated by large language models. Special attention is given to AI tools for code generation, their characteristics, operating principles and application in software development. The paper also analyzes studies that evaluate the performance, accuracy and quality of AI-generated code, as well as the potential applications of these tools in programming education. In addition to the advantages, challenges such as inaccurate responses, model bias, academic ethics and the need for human supervision are also discussed.
Branislava Radu, Ljubica Kazi· 16th International Symposium...· 0 citations
Artificial intelligence (AI) tools are increasingly used in English as a foreign or second language (EFL/ESL) academic writing classrooms, yet evidence on their instructional use and effects remains fragmented across tools, contexts, and study designs. This systematic literature review synthesized primary empirical studies published between 2019 and 2025 and retrieved from Scopus, Web of Science, ERIC, and Google Scholar, following the PRISMA 2020 guidelines, and appraised the methodological quality of the included studies. Three research questions were addressed, concerning the AI tools used in EFL/ESL writing instruction, their effects on writing quality and skills, and the associated challenges and ethical considerations. The findings indicate that three categories of tools, automated writing evaluation and grammar-checking tools, paraphrasing tools, and generative AI are used across all stages of the writing process, frequently in combination. Their reported effects reveal a tension between consistently documented gains at the surface level of grammar, mechanics, and vocabulary and less certain gains in higher-order aspects such as content, organization, and coherence, which appear mainly in studies with stronger designs or newer generative models. Recurring concerns include student overreliance, academic integrity, and the uneven distribution of benefits across learners of differing proficiency. The review contributes an EFL-specific synthesis spanning multiple tool types and educational levels, and argues that the value of AI tools depends less on the technology itself than on how it is pedagogically mediated. Implications for teacher-guided, critically evaluated use and directions for future research are discussed.
R. Santika, Rafi Farizki· International Multidisciplin...· 0 citations
Artificial intelligence is increasingly used in higher education, yet evidence concerning its relationship with academic performance remains fragmented across adoption studies, predictive models and evaluations of learning outcomes. This study maps and synthesises Scopus-indexed research published between 2020 and 2025. A bibliometric analysis of 84 records was conducted using VOSviewer and Microsoft Excel, while 29 eligible studies were examined through a PRISMA-guided systematic review and structured content analysis. The review analysed publication patterns, influential contributors, AI application types, educational purposes, research designs, outcome measures and theoretical frameworks. The findings show rapid publication growth, strong reliance on quantitative and predictive designs, and increasing attention to generative AI, adaptive learning, and learning analytics. However, evidence of improved academic performance varies according to whether studies measured perceptions, prediction accuracy, correlations or direct learning outcomes. Important gaps remain in the limited use of pedagogical frameworks, including TPACK, and the scarcity of experimental, longitudinal, and geographically diverse research. The study contributes an outcome-centred synthesis that connects bibliometric patterns with the methodological and pedagogical strengths of the available evidence.
Mohammed Adi Al-Battashi, M. A. M. Adnan, Hassan Abuhassna et al.· International Journal of Lea...· 0 citations
This study systematically reviews the current evidence on AI in academic writing using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines and contributes a comprehensive synthesis of current research by integrating pedagogical, assessment, and human perspectives through the TPACK, assessment, and Technology Acceptance Model frameworks.
Saidatul Akmar Zainal Abidin, Noor Hanim Rahmat· International journal of res...· 0 citations
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