Authors :
Zhiqiang Jiang; Phanthida Laophuangsak
Volume/Issue :
Volume 10 - 2025, Issue 5 - May
Google Scholar :
https://tinyurl.com/5n7acfba
DOI :
https://doi.org/10.38124/ijisrt/25may2040
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Purpose:
AI is reshaping career planning in the same manner as it has penetrated other sectors and domains. The study focused
on assessing the role of AI in career planning and management in the post-AI boomed era in Thailand.
Methodology:
The study is based on a qualitative structured interview. The interview questions were developed from the content
review, and face and content validity were performed before their operationalization at full scale. Moreover, the study
followed the quota sampling techniques for respondent recruitment and data collection.
Findings:
The study admits that AI is pivotal in career planning and management. Therefore, the university should focus on
training its faculty members, offering refresher courses for AI skills and embedding them in programs and curricula. At the
same time, the study also proclaims that the role of AI in career planning may lead to social inequalities, as rural graduates
may have limited access to AI-related training and programs. However, the study also admits that AI may lead to social and
economic inequality among rural and urban citizens; therefore, it should be planned for holistic growth to manage the
disparities.
Implications:
The study provides insight to educators, career planners, career counsellors, and even every individual. AI has become
a necessity for every individual and organization. Therefore, every individual and institution is supposed to embed AI in
their individual and organizational operations to improve their performance.
Recommendations:
The study recommends the integration of AI in higher education institutions (HEIs) programs to launch dedicated
programs specializing in AI tools, models, methods, and techniques need to be launched. Similarly, researchers are
recommended to explore pedagogy and andragogy-related concepts to make the teaching-learning processes more student-
centred for career planning and development.
References :
- Aliabadi, R. (2023). The Impact of an Artificial Intelligence (AI) Project-Based Learning (PBL) Course on Middle-School Students’ Interest, Knowledge, and Career Aspiration in the AI Field. Robert Morris University .
- Allal-Chérif, O., Aránega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: Using artificial intelligence to identify, select, and retain talents worldwide. Technological Forecasting and Social Change Volume 169, 1-19.
- Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., Saleh, K. b., . . . Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy Volume 19, Issue 8, 1236-1242.
- Annamalai, N., Bervell, B., Mireku, D. O., & Andoh, R. P. (2025). Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory. Computers and Education: Artificial Intelligence Volume 8, 1-19.
- Baek, C., Tate, T., & Warschauer, M. (2024). “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI. Computers and Education: Artificial Intelligence vol 7, 1-18.
- Bankins, S., Jooss, S., Restubog, S. L., Marrone, M., Ocampo, A. C., & Shoss, M. (2024). Navigating career stages in the age of artificial intelligence: A systematic interdisciplinary review and agenda for future research. Journal of Vocational Behavior Volume 153, 1-18.
- Chakamanont, S., & Thabmali, P. (2025). Guidelines For Enhancing Thai Teachers’ Artificial Intelligence Literacy In The Digital Age. Journal of Education and Innovation, Vol. 27 No. 1, 1-18.
- Chiu, T. K. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: a case of ChatGPT. Cultural and Regional Perspectives, Volume 72, 2401–2416.
- Chiu, T. K., Falloon, G., Song, Y., Wong, V. W., Zhao, L., & Ismailov, M. (2024). A self-determination theory approach to teacher digital competence development. Computers & Education Volume 214, 1-19.
- Gao, Z., Cheah, J.-H., Lim, X.-J., & Luo, X. (2024). Enhancing the academic performance of business students using generative AI: An interactive-constructive-active-passive (ICAP) self-determination perspective. The International Journal of Management Education Volume 22, Issue 2, 1-18.
- Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education Volume 18, Issue 1, 1-20.
- Guliyev, H. (2023). Artificial intelligence and unemployment in high-tech developed countries: New insights from dynamic panel data model. Research in Globalization Volume 7, 1-18.
- Guliyev, H., Huseynov, N., & Nuriyev, N. (2023). The relationship between artificial intelligence, big data, and unemployment in G7 countries: New insights from dynamic panel data model. World Development Sustainability Volume 3, 1-20.
- Hsia, L.-H., Lin, Y.-N., Lin, C.-H., & Hwang, G.-J. (2025). Effectiveness of gamified intelligent tutoring in physical education through the lens of self-determination theory. Computers & Education Volume 227, 1-18.
- Huang, X., Yang, F., Zheng, J., Feng, C., & Zhang, L. (2023). Personalized human resource management via HR analytics and artificial intelligence: Theory and implications. Asia Pacific Management Review Volume 28, Issue 4, 598-610.
- Jiang, Q., Qian, J., & Zang, Y. (2024). To acknowledge or conceal: An exploratory study on designers' self-determination factors and attitudes toward artificial intelligence participation in their works. Kybernetes, 1-11.
- Kang, H., Turi, J. A., Bashir, S., Alam, M. N., & Shah, S. A. (2021). The moderating role of information system and mobile technology with learning and forgetting factors on organizational learning effectiveness. Learning and Motivation Volume 76, 1-14.
- Labrague, L. J., Aguilar-Rosales, R., Yboa, B. C., Sabio, J. B., & Santos, J. A. (2023). Student nurses' attitudes, perceived utilization, and intention to adopt artificial intelligence (AI) technology in nursing practice: A cross-sectional study. Nurse Education in Practice Volume 73, 1-18.
- Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence Volume 3, 20-40.
- Li, L., Yu, l., & Zhang, E. (2024). A systematic review of learning task design for K-12 AI education: Trends, challenges, and opportunities. Computers and Education: Artificial Intelligence Volume 6, 20-37.
- Li, T., Zhan, Z., Ji, Y., & Li, T. (2025). Exploring human and AI collaboration in inclusive STEM teacher training: A synergistic approach based on self-determination theory. The Internet and Higher Education Volume 65, 1-16.
- Li, Y., Zhou, X., & Chiu, T. K. (2024). Systematics review on artificial intelligence chatbots and ChatGPT for language learning and research from self-determination theory (SDT): What are the roles of teachers? Interactive Learning Environments, 1-24.
- Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., . . . Dragan Gašević e, G. S. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI? Computers and Education: Artificial Intelligence Volume 3, 1-19.
- Martínez-Moreno, J., & Petko, D. (2024). What motivates future teachers? The influence of Artificial Intelligence on student teachers' career choice. Computers and Education: Artificial Intelligence Volume 7, 1-23.
- Nazaria, N., Shabbirb, M. S., & Setiawan, R. (2021). Application of Artificial Intelligence powered Digital Writing Assistant in Higher Education: Randomized Controlled Trial. Heliyon, Volume 7, Issue 5, 1-17.
- Sanusi, I. T., Agbo, F. J., Dada, O. A., Yunusa, A. A., Aruleba, K. D., Obaido, G., . . . Oyelere, S. S. (2024). Stakeholders’ insights on artificial intelligence education: Perspectives of teachers, students, and policymakers. Computers and Education Open Volume 7, 1-18.
- Shahrasbi, N., Rohani, M., Purmehdi, M., & Ghatari, A. R. (2024). Self-determination theory and customer revenge behaviour: explaining how customers regulate their anger and revenge behaviour. Journal of Consumer Marketing, Vol. 41 No. 2, 129-147.
- Thangam, K. S., Anju, S., Madhavan, S. P., & Gupta, D. (n.d.). Innovativeness or Competence: A Self-Determination Theory Model of How Students Use Generative AI in Higher Education. ICT Systems and Sustainability, (pp. 1-18).
- True Blue. (20204). The Impact of AI and ML on Thailand's Economy and Workforce. Bankok: True Blue.
- Wutiwiwatchai, C. (2024). Thailand's AI strategy to boost economic and social well-being. National Electronics and Computer Technology Center.
Purpose:
AI is reshaping career planning in the same manner as it has penetrated other sectors and domains. The study focused
on assessing the role of AI in career planning and management in the post-AI boomed era in Thailand.
Methodology:
The study is based on a qualitative structured interview. The interview questions were developed from the content
review, and face and content validity were performed before their operationalization at full scale. Moreover, the study
followed the quota sampling techniques for respondent recruitment and data collection.
Findings:
The study admits that AI is pivotal in career planning and management. Therefore, the university should focus on
training its faculty members, offering refresher courses for AI skills and embedding them in programs and curricula. At the
same time, the study also proclaims that the role of AI in career planning may lead to social inequalities, as rural graduates
may have limited access to AI-related training and programs. However, the study also admits that AI may lead to social and
economic inequality among rural and urban citizens; therefore, it should be planned for holistic growth to manage the
disparities.
Implications:
The study provides insight to educators, career planners, career counsellors, and even every individual. AI has become
a necessity for every individual and organization. Therefore, every individual and institution is supposed to embed AI in
their individual and organizational operations to improve their performance.
Recommendations:
The study recommends the integration of AI in higher education institutions (HEIs) programs to launch dedicated
programs specializing in AI tools, models, methods, and techniques need to be launched. Similarly, researchers are
recommended to explore pedagogy and andragogy-related concepts to make the teaching-learning processes more student-
centred for career planning and development.