Bachelor of Science in Computer Science and Engineering

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A comprehensive safety and support platform for domestic abuse victims
(BRAC University, 2025-06) Mahin, Rifah Tasnim; Ainun, Atika Hossain; Islam, Lamiya; Chowdhury, Farida; Abedin, Jawaril Munshad
Domestic violence remains a critical issue, especially in surveillance-heavy environments like Bangladesh where abusers often monitor their victims’ mobile activity. This research presents the iterative design and conceptual development of a discreet safety and support application for domestic abuse victims. Unlike traditional solutions, this mobile application is disguised as a benign utility app (e.g., a grocery list), ensuring discretion even under close monitoring. The app architecture follows a layered Four P’s Model: Preparation, Protection, Provision, and Prevention, aligning each feature with user safety goals. While designing, a user-centered approach was adopted, involving expert interviews, focus group discussions, feature assessment surveys, and victim testing across three design phases: hand-drawn paper prototypes, low-fidelity digital versions, and a fully navigable high-fidelity prototype. Each phase incorporated active feedback from survivors and professionals to ensure clarity, minimal cognitive load, and cultural relevance. Key functionalities include dummy interface switching, real/dummy login system, encrypted evidence logging, a Quick Exit button, and Bangla localization. Additionally, the app proposes two machine learning extensions: a voice-based distress and trigger word detection model using emotion recognition, and a conceptual risk prediction framework based on user-logged incidents. While not implemented due to time and development limitations, the models were architected using open-source datasets and preprocessing pipelines, ensuring future feasibility. By embedding iterative victim feedback and Human-Computer Interaction (HCI) principles throughout, this study demonstrates a survivor-informed, context-sensitive approach to mobile safety design. The final prototype serves as both a practical intervention model and a contribution to ongoing research in HCI, trauma-aware design, and machine learning for social good.
AI-generated academic assesment portal with performance tracking
(BRAC University, 2025-06) Dipu, MD. Saiful Islam; Islam, Aminul; Hasan, Rakibul; Shahriar, MD. Asif; Abedin, Jawaril Munshad
Artificial intelligence revolutionized numerous digital education operations yet the assessment of academic performance continues to prove especially difficult to overcome. Unfortunately, static question banks and rigid answer matching systems, currently used in making the assessment, can’t provide personalized learning experiences. These systems have difficulty acknowledging proper semantic responses and frequently misidentify them. This paper describes an academic assessment portal developed by AI technology which combines performance tracking features to solve existing evaluation problems. The system uses the multilingual mT5 model to automate the production of questions that match different domains and contextual requirements. The Bangla Transformer system dedicated to evaluation answers detects properly paraphrased responses that improve testing precision. Student performance directs the platform to automatically adjust questions until each student experiences a suitable learning challenge for their current level. The AI system evaluates student responses by analyzing context which enables it to improve both accuracy and fairness of the assessment process. Students obtain performance-related data about their areas of expertise through performance tracking while automated question generation frees educators to teach without additional paperwork. The platform delivers both robustness and user-friendly interface through the use of Flask, React.js. Initial test results indicate that self-assessment performance tracking platforms outperform other methods, showing an accuracy improvement of 40%-50% due to personalized tracking, adaptive learning, and data-driven feedback mechanisms.