AskEPTBot: Your Virtual EPT Chatbot Companion!

AskEPTBot is a virtual innovation Artificial Intelligence (AI) chatbot by utilising the strengths of the Telegram application system for diploma-level students and lecturers to seek information pertaining to the English Placement Test (EPT). This innovative product was designed to replace the inefficient traditional mode of communication to these stakeholders through the individual messaging platforms such as WhatsApp or Telegram channels. Due to the repeated occurrences of misinformation and repetitive explanations given to the interested parties, a one-stop chatbot centre was created to address the stated problems, thus reducing the information overload and massive workload faced by the lecturers or known as EPT Lecturers in Charge (LICs) who are assigned to relay continuous information to the students. The main aim of the innovation was to reduce these burdens of administrative work through the automation of the communication process involving frequent enquiries from 21 UiTM branches nationwide. This product can improve its responses through more interactions with the users because it was integrated with machine learning. The analyses of engagement metrics revealed that most interactions were centred around event-based enquiries such as payment methods with 1,654 interactions compared to non-event or content related enquiries with high levels of accuracy (94.61%). Meanwhile, the analysis of the user satisfaction survey indicated that 92 per cent of users were satisfied with the clarity of information and 82 per cent of them were satisfied with the efficiency of the chatbot. This AskEPT bot has a huge potential in terms of commercialisation through paid mobile apps advertisement apart from its capacity to be upgraded into a web-based system that will attract more advertisements from internal and external customers who want to promote their products. In terms of its novelty, this AskEPTbot stands out as a one-stop centre hub for information about EPT with its additional features of auto-promoted design of further related questions and adaptive ability for accuracy refinement. It is recommended that this application is scaled up further with real-time content and dashboard-like reports for administrative purposes to ensure its sustainable and profitable presence and usage for many years to come.

AskEPTBot; EPT; AI Chatbot; Machine Learning

1. Al-Shafei, M. (2025). Navigating human-chatbot interactions: Factors influencing user satisfaction and engagement. International Journal of Human–Computer Interaction, 41(1), 411–428. https://doi.org/10.1080/10447318.2023.2301252 [Google Scholar] [Crossref]

2. AppsFlyer. (2024). The state of app monetization—2024 edition. https://www.appsflyer.com/resources/reports/app-marketing-monetization/ [Google Scholar] [Crossref]

3. Baishya, D., & Maheshwari, S. (2022). WhatsApp groups in academic context: Exploring the academic uses of WhatsApp groups among students. Contemporary Educational Technology, 14(1), 1–12. https://www.academia.edu/84799466/ WhatsAppGroupsinAcademicContext (preprint) [Google Scholar] [Crossref]

4. EdTech Magazine. (2025). How three universities developed their chatbots. https://edtechmagazine.com/higher/article/2025/05/how-three-universities-developed-their-chatbots [Google Scholar] [Crossref]

5. Gökçearslan, Ş., Tosun, C., & Erdemir, Z. G. (2024). Benefits, challenges, and methods of AI chatbots in education: A systematic review. International Journal of Technology in Education, 7(1), 19–39. https://files.eric.ed.gov/fulltext/EJ1415037.pdf [Google Scholar] [Crossref]

6. Intercom. (2023). How are customer service metrics changing in the age of AI? https://www.intercom.com/blog/customer-service-metrics-ai/ [Google Scholar] [Crossref]

7. Kommunicate. (2025). Metrics and attributes used in Zendesk Chat. https://www.kommunicate.io/blog/metrics-and-attributes-used-in-zendesk-chat/ [Google Scholar] [Crossref]

8. Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20, 56. https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-023-00426-1 [Google Scholar] [Crossref]

9. Median. (2024). Mobile app monetization statistics 2024: Trends & earnings insights. https://median.co/blog/mobile-app-monetization-statistics-2024-trends-earnings-insights [Google Scholar] [Crossref]

10. Oberlo. (2024). In-app advertising revenue (2017–2028). https://www.oberlo.com/statistics/in-app-advertising-revenue [Google Scholar] [Crossref]

11. Quickchat AI. (2025). The complete guide to chatbot analytics: KPIs, dashboards, and deflection rate. https://quickchat.ai/post/chatbot-analytics [Google Scholar] [Crossref]

12. Silva, G. R. S., & Canedo, E. D. (2022). Towards User-Centric Guidelines for Chatbot Conversational Design. International Journal of Human–Computer Interaction, 40(2), 98–120. https://doi.org/10.1080/10447318.2022.2118244 [Google Scholar] [Crossref]

13. Statista (In-App Advertising—Worldwide). (2025). Market outlook. https://www.statista.com/outlook/amo/advertising/in-app-advertising/worldwide [Google Scholar] [Crossref]

14. Stockholm University—Department of Education. (2024). New research highlights opportunities and challenges of AI chatbots in higher education. https://www.su.se/department-of-education/news/new-research-highlights-opportunities-and-challenges-of-ai-chatbots-in-higher-education-1.759929 [Google Scholar] [Crossref]

15. Telegram. (n.d.). Telegram Bot API. https://core.telegram.org/bots/api [Google Scholar] [Crossref]

16. UiTM Library (PTAR). (2024). English Placement Test (EPT) event. https://library.uitm.edu.my/en/component/jem/event/325-english-placement-test-ept [Google Scholar] [Crossref]

17. Yau, J. Y.-S., et al. (2024). Accuracy of LLM chatbot responses for emergency care questions. Journal of Medical Internet Research, 26, e60291. https://www.jmir.org/2024/1/e60291 [Google Scholar] [Crossref]

18. Zendesk. (2025). Top 22 benefits of chatbots for businesses and customers. https://www.zendesk.com/blog/5-benefits-using-ai-bots-customer-service/ [Google Scholar] [Crossref]

• A Machine Learning Model for Predicting the Risk of Developing Diabetes - T2DM Using Real-World Data from Kilifi, Kenya
• AI-Powered Facial Recognition Attendance System Using Deep Learning and Computer Vision
• A Comprehensive Review on Brain Tumour Segmentation Using Deep Learning Approach
• A Scalable Retrieval-Augmented Generation Pipeline for Domain-Specific Knowledge Applications
• Predictive Maintenance in Semiconductor Manufacturing Using Machine Learning on Imbalanced Dataset