Aug 2026· International Journal on Advanced Science, Engineering and Information Technology· 0 citations· 62 references
Computer Science
TL;DR
Overall, vibe coding enhances productivity but requires critical oversight, reinforcing its role as a transformative yet transitional paradigm in software development.
Abstract
This study evaluates Vibe Coding, an emerging AI-led conversational programming paradigm that enables developers to generate software through natural-language interaction with large language models (LLMs). Using a mixed-methods design, the study assessed performance efficiency, cognitive implications, and responsible adoption in comparison with traditional and AI-assisted coding environments. Thirty participants, including professional developers and advanced computing students, completed equivalent programming tasks under three experimental conditions. Quantitative data were analyzed using descriptive statistics and repeated-measures ANOVA, while qualitative data were examined through thematic analysis. Results show that vibe coding significantly improved development efficiency, reducing task completion time by 27% compared with traditional coding and 12% compared with AI-assisted coding. However, these gains were accompanied by lower maintainability indices and higher security vulnerabilities, indicating trade-offs in software quality. Usability results yielded a good rating (SUS = 71.4), while cognitive workload remained moderate (NASA-TLX = 55.5), reflecting reduced syntactic effort but increased linguistic reasoning. Thematic analysis identified trust calibration, loss of control, cognitive adaptation, and prompt-engineering strategy as key constructs. Notably, perceived loss of control was associated with increased security risks due to reduced transparency and validation of AI-generated outputs. Based on these findings, the study proposes a three-pillar framework for responsible adoption: (1) hybrid integration of human and AI capabilities, (2) human oversight and transparent accountability, and (3) context-aware deployment. Overall, vibe coding enhances productivity but requires critical oversight, reinforcing its role as a transformative yet transitional paradigm in software development.
The system attained a System Usability Scale (SUS) score of 88.5 and cut grading time by 87.5 percent, facilitating focused instructor review in LLM-supported programming assessments, facilitating focused instructor review in LLM-supported programming assessments.
A. Ibrahim, Runal Rezkiawan· EDUMATIC: Jurnal Pendidikan...· 0 citations
Background: Generative AI coding tools are increasingly used in university computing education, including emerging practices commonly described as vibe coding. However, evidence on their educational effects is scattered across studies that differ in interaction patterns, instructional designs, assessment practices, and...
The widespread adoption of generative AI tools has created new methodological challenges for the validity of assessment in higher education. If a neural network can write code, how can a student's actual knowledge be assessed objectively? This study proposes a multifactor AI-assisted assessment model for the fair e...
Z. Nurbekova, M. Kemelbek, A. Sagimbayeva et al.· Frontiers in Education· 0 citations
Generative artificial intelligence is reshaping programming education, yet its effects on skill development depend partly on how learners interact with artificial intelligence-supported systems. This study introduces the Artificial Intelligence-Scaffolding Interaction Framework, which conceptualizes constrained, questi...
M. Yenidogan, Zeynep Cömert, Duygu Çakır· IEEE Access· 0 citations
Initial evidence of the existence and variation of Perspective Switching is provided and its importance in the context of AI-assisted software development, where the human role shifts from code production to critical evaluation and validation, is discussed.
Muhammad Asri bin Muhamad Khidzi, Muhammad Faiz bin Supian, Abdul Azim bin Abdul Rashid et al.· International journal of res...· 0 citations
The findings support scaffolded integration of AI tutors and compulsory modules on verifying AI-generated code within AI literacy provision and support scaffolded integration of AI tutors and compulsory modules on verifying AI-generated code within AI literacy provision.
Nurkasym Arkabaev, R. Oichueva, Aizada Ajibekova· Jurnal Ilmiah Ilmu Terapan U...· 0 citations