AI IN LAO EDUCATION: BALANCING WORKLOAD MANAGEMENT AND SKILL DEVELOPMENT IN HIGHER EDUCATION
DOI:
https://doi.org/10.14456/aelr.2026.5Keywords:
Artificial Intelligence, Higher Education, Academic Workload Management, Skill Development, Lao PDR.Abstract
This study explores the integration of Artificial Intelligence (AI) into higher education in Vientiane, Lao PDR, examining its impact on academic workload management and the practical skill development of students and postgraduates in engineering, economics, and social sciences. Employing a mixed-methods approach, the research combines quantitative survey data on AI usage with qualitative insights from educators. Key findings reveal that while AI tools enhance efficiency and support personalized learning, they also pose challenges, such as the potential for skill atrophy and widening digital equity gaps. The study highlights a “performance paradox,” where AI-assisted gains can overshadow the development of foundational skills. Systemic barriers, including uneven access to training and limited awareness of specialized AI applications, further complicate integration. The research emphasizes the need for comprehensive AI literacy programs, curriculum redesign to augment learning, and policies to address ethical concerns and promote equitable opportunities. The findings provide empirical evidence to inform policymakers and educators on strategies for harnessing AI’s benefits while mitigating its risks in resource-constrained settings.
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Amar, J., Rahimi, S., von Bismarck, N., & Wunnava, A. (2022). Smart Scheduling: How to Solve Workforce Planning Challenges with AI. Retrieved from www.mckinsey.com/capabilities/operations/our-insights/smart-scheduling-how-to-solve-workforce-planning-challenges-with-ai.
Babashahi, L., Barbosa, C., Lima, Y., Lyra, A., Salazar, H., Argôlo, M., … & Souza, J. 2024). AI in the Workplace: A Systematic Review of Skill Transformation in the Industry. Administrative Sciences, 14(6), 127.
Baker, T., & Smith, L. (2019). Educ-AI-tion Rebooted? Exploring the Future of Artificial Intelligence in Schools and Colleges. London: Nesta.
Brasse, J., & Broder, H., Förster, M., Klier, M., & Sigler, I. (2023). Explainable Artificial Intelligence in Information Systems: A Review of the Status Quo and Future Research Directions. Electronic Markets, 33, 26.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Bryman, A. (2016). Social Research Methods (5th ed.). London: Oxford University Press.
Creswell, J., & Plano Clark, V. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). California: SAGE Publications.
Davenport, T, &. Miller, S. (2022). Working with AI: Real Stories of Human-Machine Collaboration. Massachusetts: MIT Press.
Davis, F. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340.
Denzin, N. (2012). Triangulation 2.0. Journal of Mixed Methods Research, 6, 80-88.
Dwivedi, Y., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. (2021). Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy. International Journal of Information Management, 57, 101994.
Fetters, M., Curry, L., & Creswell, J. (2013). Achieving Integration in Mixed Methods Designs—Principles and Practices. Health Services Research, 48(6), 2134-2156.
Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). London: SAGE Publications.
García-Martínez, I., Fernández-Batanero, J., Fernández-Cerero, J., & León, S. (2023). Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis. Journal of New Approaches in Educational Research, 12(1), 171-197.
Guetterman, T., Fetters, M., & Creswell, J. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. Annals of Family Medicine, 13(6), 554-561.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Massachusetts: Center for Curriculum Redesign.
Kim, N., & Paek, S. (2021). Analysis of Worldwide Research Trends on the Impact of Artificial Intelligence in Education. Sustainability, 13(14), 7941.
Lao Ministry of Education and Sports. (2022). National report on educational technology integration in Laos. Vientiane: Government Printing Office.
Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S., Ogata, H., ... & Tsai, C. (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 11, 580820.
Nguyen, Q., Lai, N., & Nguyen, Q. (2024). Artificial Intelligence (AI) in Education: A Case Study on ChatGPT’s Influence on Student Learning Behaviors. Educational Process: International Journal, 13(2), 132-147.
Nowell, L., Norris, J., White, D., & Moules, N. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods, 16(1). https://doi.org/10.1177/1609406917733847.
Pintrich, P., & de Groot, E. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40.
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582-599.
Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). California: SAGE Publications.
Selwyn, N. (2022). Education and technology: Key issues and debates (3rd ed.). New York: Bloomsbury Academic.
Sharples, M. (2023). Towards Social Generative AI for Education: Theory, Practices and Ethics. Learning: Research and Practice, 9(2), 159-167.
Shen, Y., Song, K., Tan, X., Zhang, W., Ren, K., Yuan, S., … & Zhang, Y. (2024). TaskBench: Benchmarking Large Language Models for Task Automation. ArXiv. https://doi.org/10.48550/arXiv.2311.18760.
Smith, J., & Doe, R. (2024). Personalized Learning Pathways: Integrating AI in Modern Education. Educational Technology & Society, 27, 15-28.
St-Hilaire, F., Vu, D., Frau, A., Burns, N., Faraji, F., Potochny, J., … & Kochmar, E. (2022). A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions. ArXiv. https://doi.org/10.48550/arXiv.2203.03724.
Taherdoost, H. (2016). Sampling methods in research methodology: How to choose a sampling technique for research. International Journal of Academic Research in Management, 5, 18-27.
Tashakkori, A., & Teddlie, C. (2010). SAGE Handbook of Mixed Methods in Social & Behavioral Research (2nd ed.). New York: SAGE Publications.
United Nations Educational, Scientific and Cultural Organization. (2021). AI and education: Guidance for policy-makers. Paris: UNESCO Publishing.
Wei, X., Zhang, Z., Yue, Z., Chen, H. (2021). Context-AI Tunes: Context-Aware AI-Generated Music for Stress Reduction. ArXiv. https://doi.org/10.48550/arXiv.2505.09872.
World Economic Forum. (2020). Schools of the future: Defining new models of education for the fourth industrial revolution. Retrieved from www3.weforum.org/docs/WEF_Schools_of_the_Future_Report_2019.pdf.
Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators?. International Journal of Educational Technology in Higher Education, 16, 39.
Zheng, L., Fan, Y., Chen, B., Huang, Z., Gao, L., & Long, M. (2023). An AI-enabled Feedback-Feedforward Approach to Promoting Online Collaborative Learning. Education and Information Technologies, 29, 11385-11406.
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Copyright (c) 2025 Chaymaly PHAKASOUM, Voutixay SIHALATH, Souvanh SISAAT, Doulakhom THEPPARSOUK
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