Open Access Peer-reviewed Research Article

Enhancing Vocational Certification Learning through a Gamified Chatbot: Evidence from a Quasi-Experimental Study

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Submitted   Jul 2, 2025
Published   Nov 4, 2025
Issue    Vol 6 No 1 (2026)
DOI   10.25082/AMLER.2026.01.001

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Cheng-Hsiu Li corresponding author
Department of Information Management, National Taitung Junior College, Taitung 95045, Taiwan, ROC

Abstract

Certification-based vocational education often emphasizes practical training, yet repetitive subject-based learning tasks place a heavy burden on teachers and reduce student motivation. To address this challenge, this study examined the integration of a gamified chatbot into cognitive content instruction for the Level B Computer Hardware Fabrication certification. A quasi-experimental design was implemented across three academic years, involving a control group (conventional instruction), an experimental group using a non-gamified chatbot, and another using a gamified chatbot. Over an eight-week intervention, participants completed pre- and post-tests to measure learning effectiveness, while learner satisfaction was assessed through a validated questionnaire. Results from ANCOVA revealed that the gamified chatbot group achieved learning outcomes equivalent to teacher-led instruction and significantly outperformed the non-gamified chatbot group. Post hoc tests confirmed large effect sizes favoring gamification. Learners also reported greater satisfaction, particularly in reduced boredom and improved alignment with learning preferences. These findings demonstrate that a gamified chatbot can effectively function as a mobile cognitive content instructor, sustaining motivation, enhancing learning outcomes, and alleviating teacher workload in certification-oriented education. The proposed model is scalable and holds global relevance, offering adaptability to other vocational certifications, STEM training, and content-intensive learning contexts.

Keywords
chatbot, gamification, mobile learning, drill-and-practice learning, improving classroom teaching, vocational education, teacher workload

Article Details

Supporting Agencies
This work was supported by the Ministry of Education Teaching Practice Research Program (Taiwan) under Grant No. MOE-113-TPRED-0222-001Y1, 2024.
How to Cite
Li, C.-H. (2025). Enhancing Vocational Certification Learning through a Gamified Chatbot: Evidence from a Quasi-Experimental Study. Advances in Mobile Learning Educational Research, 6(1), 1653-1667. https://doi.org/10.25082/AMLER.2026.01.001
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Ahmad, F. B. (2025). AI-Powered Chatbots for Mobile Teacher Training: Enhancing Interactive Learning Experiences. International Journal of Interactive Mobile Technologies (IJIM), 19(17), 41–59. https://doi.org/10.3991/ijim.v19i17.54387
  2. Al-Hafdi, F. S., & Alhalafawy, W. S. (2024). Ten Years of Gamification-Based Learning: A Bibliometric Analysis and Systematic Review. International Journal of Interactive Mobile Technologies (IJIM), 18(07), 188–212. https://doi.org/10.3991/ijim.v18i07.45335
  3. Alrasheedi, M., & Capretz, L. F. (2015). Determination of critical success factors affecting mobile learning: A meta-analysis approach. Turkish Online Journal of Educational Technology, 14(2), 41–51. https://doi.org/10.48550/arXiv.1801.04288
  4. Athanassopoulos, S., Manoli, P., Gouvi, M., Lavidas, K., & Komis, V. (2023). The use of ChatGPT as a learning tool to improve foreign language writing in a multilingual and multicultural classroom. Advances in Mobile Learning Educational Research, 3(2), 818–824. https://doi.org/10.25082/amler.2023.02.009
  5. Bhargava, M., Varshney, R., & Anita, R. (2020). Emotionally intelligent chatbot for mental healthcare and suicide prevention. International Journal of Advanced Science and Technology, 29(6), 2597–2605.
  6. Burke, R. L. (1982). CAI sourcebook. Prentice-Hall.
  7. Business Next. (2023). 2018 survey on internet-browsing time by countries: The Philippines ranks first and Japan ranks last. https://www.bnext.com.tw
  8. Carrión Candel, E., & Colmenero, M. J. R. (2022). Gamification and mobile learning: innovative experiences to motivate and optimise music content within university contexts. Music Education Research, 24(3), 377–392. https://doi.org/10.1080/14613808.2022.2042500
  9. Chang, C. Y., Sheu, J. P., & Chan, T. W. (2003). Concept and design of Ad Hoc and Mobile classrooms. Journal of Computer Assisted Learning, 19(3), 336–346. Portico. https://doi.org/10.1046/j.0266-4909.00035.x
  10. Cheng-Hsiu Li. (2023). Instructional Design, Learning Satisfaction, and Learning Outcome in a Virtual Reality Learning Environment Aimed at Improving the Cognition of Computer Hardware Components. International Journal of Engineering and Technology Innovation, 13(2), 111–124. https://doi.org/10.46604/ijeti.2023.10247
  11. Cheng-Hsiu Li. (2024). Integrating Gamification Elements into a Personalized Cognitive Mobile-Learning LINE Bot. Emerging Science Innovation, 3, 27–42. https://doi.org/10.46604/emsi.2024.12980
  12. Crompton, H., & Burke, D. (2018). The use of mobile learning in higher education: A systematic review. Computers & Education, 123, 53–64. https://doi.org/10.1016/j.compedu.2018.04.007
  13. Dahri, N. A., Al-Rahmi, W. M., Alhashmi, K. A., & Bashir, F. (2025). Enhancing Mobile Learning with AI-Powered Chatbots: Investigating ChatGPT’s Impact on Student Engagement and Academic Performance. International Journal of Interactive Mobile Technologies (IJIM), 19(11), 17–38. https://doi.org/10.3991/ijim.v19i11.54643
  14. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness. Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, 9–15. https://doi.org/10.1145/2181037.2181040
  15. Diahyleva, O., Yurzhenko, A., & Kononova, O. (2024). Gamification techniques in Maritime English online courses: Motivating learners in virtual environments. Advances in Mobile Learning Educational Research, 4(1), 965–972. https://doi.org/10.25082/amler.2024.01.008
  16. Dondlinger, M. J. (2007). Educational video game design: A review of the literature. Journal of Applied Educational Technology, 4(1), 21–31.
  17. El Azhari, K., Hilal, I., Daoudi, N., & Ajhoun, R. (2023). SMART Chatbots in the E-learning Domain: A Systematic Literature Review. International Journal of Interactive Mobile Technologies (IJIM), 17(15), 4–37. https://doi.org/10.3991/ijim.v17i15.40315
  18. Gee, J. P. (2003). What video games have to teach us about learning and literacy. Computers in Entertainment, 1(1), 20–20. https://doi.org/10.1145/950566.950595
  19. Guo, S. Y. (1997). Psychological and educational testing (11th ed.). Tung Hua Book Co., Ltd.
  20. Hakiki, M., Fadli, R., Samala, A. D., Fricticarani, A., Dayurni, P., Rahmadani, K., Astiti, A. D., & Sabir, A. (2023). Exploring the impact of using Chat-GPT on student learning outcomes in technology learning: The comprehensive experiment. Advances in Mobile Learning Educational Research, 3(2), 859–872. https://doi.org/10.25082/amler.2023.02.013
  21. Hamad, T. A. B., & Baniabdelrahman, A. (2023). The Effect of Self-Questioning Strategy on EFL Tenth-Grade Students’ Reading Comprehension. Journal of International Education and Practice, 6(2), 1. https://doi.org/10.30564/jiep.v6i2.5525
  22. Hamari, J., & Koivisto, J. (2015). “Working out for likes”: An empirical study on social influence in exercise gamification. Computers in Human Behavior, 50, 333–347. https://doi.org/10.1016/j.chb.2015.04.018
  23. Hobert, S., & Berens, F. (2019, December 15–18). Fostering students’ motivation to learn daily on a voluntary basis: A gamified mobile learning approach for formal learning settings. Fortieth International Conference on Information Systems, Munich, Germany.
  24. Huang, S. S. (2023). Leveraging Line Bot chatbot for enhancing novice Python learning effectiveness (Master’s thesis, National Kaohsiung Normal University, Taiwan).
  25. Huang, W., Liu, J., Wang, X., Li, J., Zhang, R., & Liu, Y. (2016). Application of Mobile Learning and Big Data on Improving Flipped Classroom and MOOCs. DEStech Transactions on Computer Science and Engineering, icte. https://doi.org/10.12783/dtcse/icte2016/4765
  26. Kozub, H., Sipii, V., Kozub, Y., Bratytsya, G., & Bondarenko, L. (2025). Effectiveness of Gamification in Mobile and Interactive Learning: Analysis of Approaches and Outcome. International Journal of Interactive Mobile Technologies (IJIM), 19(08), 27–41. https://doi.org/10.3991/ijim.v19i08.50917
  27. Lampropoulos, G. (2023). Educational benefits of digital game-based learning: K-12 teachers’ perspectives and attitudes. Advances in Mobile Learning Educational Research, 3(2), 805–817. https://doi.org/10.25082/amler.2023.02.008
  28. Landers, R. N., Bauer, K. N., Callan, R. C., & Armstrong, M. B. (2014). Psychological Theory and the Gamification of Learning. Gamification in Education and Business, 165–186. https://doi.org/10.1007/978-3-319-10208-5_9
  29. Li, C. H. (2019). Gamification of an asynchronous HTML5-related competency-based guided learning system. IOP Conference Series: Materials Science and Engineering, 658(1), 012004. https://doi.org/10.1088/1757-899x/658/1/012004
  30. Li, L. S., & Lai, C. C. (2007). Current status and future development of vocational and technical education. National Elite Quarterly, 3(1). (In Chinese)
  31. Li, L. S., Li, H T., & Chen, S. C. (2010). A review and prospects of the certification system in vocational and technical education. Educational Resources and Research Bimonthly, (93), 31–52. (In Chinese)
  32. Lin, Y. T., & Ye, J. H. (2023). Development of an educational chatbot system for enhancing students’ biology learning performance. Journal of Internet Technology, 24(2), 275-281. https://doi.org/10.53106/160792642023032402006
  33. Malone, T. W., & Lepper, M. R. (1987). Making learning fun: A taxonomy of intrinsic motivations for learning. In R. E. Snow & M. J. Farr (Eds.), Aptitude, learning, and instruction: Conative and affective process analyses (Vol. 3, pp. 223–253). Erlbaum.
  34. Nasa-Ngium, P., Nuankaew, W. S., & Nuankaew, P. (2023). Analyzing and Tracking Student Educational Program Interests on Social Media with Chatbots Platform and Text Analytics. International Journal of Interactive Mobile Technologies (IJIM), 17(05), 4–21. https://doi.org/10.3991/ijim.v17i05.31593
  35. Ozdamli, F., & Cavus, N. (2011). Basic elements and characteristics of mobile learning. Procedia - Social and Behavioral Sciences, 28, 937–942. https://doi.org/10.1016/j.sbspro.2011.11.173
  36. Papadakis, S., Zourmpakis, A. I., Kasotaki, S., & Kalogiannakis, M. (2024). Teachers’ perspectives on integrating adaptive gamification applications into science teaching. Journal of Electrical Systems, 20(11s), 2593–2600. https://doi.org/10.52783/jes.7917
  37. Simões, J., Redondo, R. D., & Vilas, A. F. (2013). A social gamification framework for a K-6 learning platform. Computers in Human Behavior, 29(2), 345–353. https://doi.org/10.1016/j.chb.2012.06.007
  38. Skill Evaluation Center, Workforce Development Agency, Ministry of Labor. (2021). Regulations for technician skills certification and issuance of certificates. https://www.wdasec.gov.tw
  39. Skill Evaluation Center, Workforce Development Agency, Ministry of Labor. (2023). Test reference materials. https://techbank.wdasec.gov.tw
  40. Skinner, B. F. (1968). The technology of teaching. Appleton-Century-Crofts.
  41. Taiwan Network Information Center. (2020). 2020 Taiwan internet report. https://www.twnic.tw
  42. The News Lens. (2023). 2022 Taiwan internet report. https://www.thenewslens.com
  43. Vankúš, P. (2024). Generative Artificial Intelligence on Mobile Devices in the University Preparation of Future Teachers of Mathematics. International Journal of Interactive Mobile Technologies (IJIM), 18(18), 19–33. https://doi.org/10.3991/ijim.v18i18.51221
  44. von Ahn, L., & Dabbish, L. (2008). Designing games with a purpose. Communications of the ACM, 51(8), 58–67. https://doi.org/10.1145/1378704.1378719
  45. Wu, M. L. (2000). The practical of SPSS statistics and application. Kings Information Inc.
  46. Xezonaki, A. (2022). Gamification in preschool science education. Advances in Mobile Learning Educational Research, 2(2), 308–320. https://doi.org/10.25082/amler.2022.02.001
  47. Xu, H., Song, D., & Ju, P. (2017). Engaged Cohorts: Can Gamification Engage All College Students in Class? EURASIA Journal of Mathematics, Science and Technology Education, 13(7). https://doi.org/10.12973/eurasia.2017.00755a
  48. Yang, S. Y. (2015). A research of multimedia courseware in high school English learning performance, motivation and perceptions based on situated learning (Master’s thesis, National Taiwan University of Science and Technology, Taiwan).
  49. Yuen, M. (2023). Chatbot market in 2022: Stats, trends, and companies in the growing AI chatbot industry. Insider Intelligence. https://www.insiderintelligence.com/insights/chatbot-market-stats-trends/
  50. Zaky, Y. A. M. (2023). Chatbot Positive Design to Facilitate Referencing Skills and Improve Digital Well-Being. International Journal of Interactive Mobile Technologies (IJIM), 17(09), 106–126. https://doi.org/10.3991/ijim.v17i09.38395
  51. Zhang, X., & Wareewanich, T. (2024). A Study of the Factors Influencing Teachers’ Willingness to Use Generative Artificial Intelligence Based on the UTAUT Model. International Journal of Interactive Mobile Technologies (IJIM), 18(06), 126–142. https://doi.org/10.3991/ijim.v18i06.47991
  52. Zhi Lin Liu. (2025). Generative AI and Mobile Learning in Higher Education: Comparing Student and Faculty Perspectives on Employability Impact. International Journal of Interactive Mobile Technologies (IJIM), 19(01), 34–45. https://doi.org/10.3991/ijim.v19i01.51325
  53. Zourmpakis, A.-I., Kalogiannakis, M., & Papadakis, S. (2023). A Review of the Literature for Designing and Developing a Framework for Adaptive Gamification in Physics Education. The International Handbook of Physics Education Research: Teaching Physics, 5-1-5–26. https://doi.org/10.1063/9780735425712_005
  54. Zourmpakis, A.-I., Kalogiannakis, M., & Papadakis, S. (2023). Adaptive Gamification in Science Education: An Analysis of the Impact of Implementation and Adapted Game Elements on Students’ Motivation. Computers, 12(7), 143. https://doi.org/10.3390/computers12070143
  55. Zourmpakis, A.-I., Kalogiannakis, M., & Papadakis, S. (2024). The Effects of Adaptive Gamification in Science Learning: A Comparison Between Traditional Inquiry-Based Learning and Gender Differences. Computers, 13(12), 324. https://doi.org/10.3390/computers13120324