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Developing a chatbot becomes a challenging task when it is built from scratch and independent of any Software as a Service (SaaS). Inspired by the idea of freeing lecturers from the burden of answering the same questions repetitively during the pre-registration process, this research has succeeded in building a textbased chatbot system. Further, this research has proved that the combination of keyword spotting technique for the Language Understanding component, Finite-State Transducer (FST) for the Dialogue Management, rulebased keyword matching for language generation, and the system-in-the-loop paradigm for system validation can produce an efficient chatbot. The chatbot efficiency is high enough as its score on Concept Efficiency (CE) reaches 0.946. It shows that users do not need to repeat their utterances several times to be understood. The chatbot performance on recognizing new concepts introduced by users is also more than satisfactory which is presented by its Query Density (QD) score of 0.80.

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This study tried to examine how cell phone users who undergo a technology leap acquire their procedural knowledge of operating a cell phone and to find out which factors can improve their device competency. Using interviews, usability tests, and a questionnaire, this study found out that many respondents use unstructured means such as asking other cell phone users or rote learning in gaining their procedural knowledge. Some factors influencing users’ device competencies that were found in this study are classified into three categories: user interface design, culture, and the users themselves. In order to improve users’ device competency, elements in those three categories must be integrated. One realization of such integration is the attempt of localizing user interface through the user’s culture, not the culture where the cell phone is designed and manufactured.