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STEM Education

2026, Volume 6, Issue 6: 1019-1035. doi: 10.3934/steme.2026041
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Research article

Generative AI in vocational ICT education: Implications for teaching, learning, and assessment

  • 1.
    Faculty of Computing, University of Sri Jayewardenepura, Nugegoda, Sri Lanka; wijendrapg@sjp.ac.lk
  • 2.
    Faculty of Graduate Studies, General Sir John Kotelawala Defence University, Colombo, Sri Lanka; sulochana.liyanage@gmail.com

  • Academic Editor: William Guo
  • Published: 08 October 2026

  • Generative AI learning outcomes and skill acquisition technologies in vocational ICT education are explicitly incorporated in the teaching practice and assessment design. This study used a mixed-methods approach, combining questionnaires with in-depth interviews (n = 20), to investigate pedagogical issues in generative AI and its potential learning and evaluation capabilities. 323 ICT undergraduates from a university of vocational technology in Sri Lanka participated in the research. The outcomes of the experiment showed that generative AI is not a leveler but a competence amplifier; thus, very competent students in ICT and those with a high level of awareness of generative AI are much more likely to succeed, whereas undergraduate use of generative AI denies individuals the opportunity to acquire basic skills. The mediator of learning value is student engagement, which implies that students' use of generative AI is more important than the generative AI itself. These findings, when applied to vocational teachers, imply systematic curriculum development: an initial foundation in basic programming, followed by the introduction of generative AI, open pedagogy that builds metacognition, and process-based testing that assesses actual skills rather than generative AI-related tasks. The research provides empirical guidance for educators who need to determine how to structure their instruction, ensure test integrity, and use generative AI in professional training.

    Citation: Wijendra Gunathilake, Sulochana Liyanage. Generative AI in vocational ICT education: Implications for teaching, learning, and assessment[J]. STEM Education, 2026, 6(6): 1019-1035. doi: 10.3934/steme.2026041

    Related Papers:

  • Generative AI learning outcomes and skill acquisition technologies in vocational ICT education are explicitly incorporated in the teaching practice and assessment design. This study used a mixed-methods approach, combining questionnaires with in-depth interviews (n = 20), to investigate pedagogical issues in generative AI and its potential learning and evaluation capabilities. 323 ICT undergraduates from a university of vocational technology in Sri Lanka participated in the research. The outcomes of the experiment showed that generative AI is not a leveler but a competence amplifier; thus, very competent students in ICT and those with a high level of awareness of generative AI are much more likely to succeed, whereas undergraduate use of generative AI denies individuals the opportunity to acquire basic skills. The mediator of learning value is student engagement, which implies that students' use of generative AI is more important than the generative AI itself. These findings, when applied to vocational teachers, imply systematic curriculum development: an initial foundation in basic programming, followed by the introduction of generative AI, open pedagogy that builds metacognition, and process-based testing that assesses actual skills rather than generative AI-related tasks. The research provides empirical guidance for educators who need to determine how to structure their instruction, ensure test integrity, and use generative AI in professional training.



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  • Authors' biographies Dr Wijendra Gunathilake is a Senior Lecturer in the Faculty of Computing at the University of Sri Jayewardenepura, Sri Lanka. She has over 25 years of experience in higher education, including more than a decade of engagement with the Technical and Vocational Education and Training (TVET) sector in Sri Lanka, Australia and the United Kingdom. She holds a PhD in Computer Science from Keele University, UK, and a Master's degree in Information Technology from the University of Colombo School of Computing. Her professional training includes Quality Assurance in TVET at TAFE NSW, Australia and Post Graduate Certificate in Teaching in Higher and Professional Education (PGCHPE), Staffordshire University, UK. Her research interests include Generative AI in vocational education, assessment integrity, empirical software engineering, digital transformation, and ethics in AI; Sulochana Liyanage holds an MBA in E-Governance from the Faculty of Graduate Studies, General Sir John Kotelawala Defence University, Sri Lanka. She also holds a Bachelor's degree in Information and Communication Technology from the University of Vocational Technology. She works as an assessor for in-plant training programmes in the Technical and Vocational Education and Training (TVET) sector, supporting the evaluation of National Vocational Qualification (NVQ) trainees. Her research interests include Artificial Intelligence applications in the TVET sector, learning analytics, and the responsible use of AI in education. Her research also focuses on assessment practices in vocational education and improving the effectiveness of in-plant training for NVQ learners
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  • © 2026 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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