Volume 13, Issue 8 (August 2026), Pages: 164-174

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 Review Paper

Artificial intelligence for experience-based credit bank evaluation in electrical technology programs at vocational education institutes

 Author(s): 

Sothon Chanpramun ^1, *, Sanya Phowong ², Pattarawat Kraipiyaset ³, Pasawut Cheerapakorn ⁴, Boonlue Thongkeatkaew ⁵

 Affiliation(s):

¹Department of Electrical Technology, Nakhon Nayok Technical College, Institute of Vocational Education, Central Region 3, Nakhon Nayok, Thailand
²Department of Electronics, Nakhon Nayok Technical College, Institute of Vocational Education, Central Region 3, Nakhon Nayok, Thailand
³Division of Digital Media and Mass Communication Technology, Rajamangala University of Technology Krungthep, Bangkok, Thailand
⁴Innovation and Technology for Education, Faculty of Education, Suan Sunandha Rajabhat University, Bangkok, Thailand
⁵Office of the Director, Institute of Vocational Education, Central Region 3, Nakhon Nayok, Thailand

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 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0009-0009-2884-3329

 Digital Object Identifier (DOI)

 
https://doi.org/10.21833/ijaas.2026.08.016

 Abstract

This study examines the Scopus-indexed research landscape of artificial intelligence (AI) in experience-based credit bank evaluation within vocational electrical technology education using an integrated PRISMA-based systematic review and bibliometric analysis. Peer-reviewed articles published between 2022 and 2025 were systematically screened to identify studies related to AI-supported assessment and recognition of prior learning. The PRISMA process refined the initial dataset into a focused body of literature representing recent developments in AI-enabled evaluation systems. Bibliometric mapping was used to analyze citation relationships and keyword co-occurrence patterns, revealing the intellectual structure of the research field. The findings indicate that artificial intelligence serves as a central enabling mechanism associated with machine learning, decision-making, learning systems, cybersecurity, and sustainability considerations. These relationships suggest that AI-supported credit bank evaluation can be understood as an integrated evaluation approach that connects technological analytics with competency evidence and quality assurance considerations. Two dominant thematic clusters were identified, distinguishing security- and risk-related evaluation concerns from technology-driven analytical and decision-support processes. The study provides a structured synthesis of current research trends and highlights the role of AI in supporting transparent, scalable, and competency-oriented credit recognition practices in vocational electrical technology education. Future research should focus on empirical validation and real-world implementation to improve the practical applicability of AI-supported evaluation systems.

 © 2026 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

Artificial intelligence, Credit bank system, Vocational education, Experience-based learning, AI-based evaluation

 Article history

Received 15 March 2026, Received in revised form 11 August 2026, Accepted 20 August 2026

 Acknowledgment

The authors acknowledge Nakhon Nayok Technical College, the Institute of Vocational Education Central Region 3, and King Mongkut's University of Technology North Bangkok for their academic and technical support. 

 Compliance with ethical standards

 Citation:

Chanpramun S, Phowong S, Kraipiyaset P, Cheerapakorn P, and Thongkeatkaew B (2026). Artificial intelligence for experience-based credit bank evaluation in electrical technology programs at vocational education institutes. International Journal of Advanced and Applied Sciences, 13(8): 164-174

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