Design and technical evaluation of InterViu: A generative AI mobile mock interview trainer for internship preparation
DOI:
https://doi.org/10.55573/JISED.118752Keywords:
Artificial intelligence, generative AI, internship preparation, mobile learning application, mock interview, personalized feedback, technical evaluationAbstract
University students may have limited opportunities to participate in structured and personalized interview practice before attending internship interviews. This study presents the design and technical evaluation of INTERVIU, a generative artificial intelligence mobile mock interview trainer developed to support internship preparation. The Android-based prototype was developed using Flutter and Dart, with Firebase Authentication for user access, Cloud Firestore for data storage, speech-to-text processing for voice responses, and the Gemini API for generating interview questions and evaluating answers. INTERVIU enables students to select an internship role and competency level, complete mock interview sessions using text or voice input, receive AI-generated scores and structured feedback, and review their performance history. The study adopted a design science research orientation and an iterative Agile development approach. Technical evaluation was informed by selected quality characteristics of ISO/IEC 25010:2023 and included 31 unit tests, 20 functional tests covering student and administrator modules, three end-to-end system scenarios, nine AI answer-evaluation scenarios, 12 mobile performance operations, ten authentication-related security cases, and six database performance operations. All predefined technical test cases produced their expected outcomes. The nine AI answer scenarios also matched their predefined performance categories, with weak answers scoring between 29 and 38, average answers between 64 and 69, and strong answers between 86 and 91. These results establish the technical feasibility and internal functional consistency of the prototype under controlled conditions. However, the AI findings represent scenario-based category agreement rather than expert-validated assessment accuracy. As no real students or interview experts participated, the findings do not establish usability, learning effectiveness, confidence improvement, or internship readiness. Further empirical validation is required before INTERVIU can be considered an educationally validated interview-training platform.
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