International Journal of Computational Learning & Intelligence

GIGXPERT: A User-Centered Digital Tutoring Solution Using Design Thinking and Intelligent Automation

Thasni Asharaf — 2026-01-06

Education is undergoing a rapid transformation with the rise of digital learning platforms. Yet, a significant gap remains in connecting students with verified, high-quality tutors who can cater to diverse learning needs. GIGXPERT is a digital tutoring platform developed to address these challenges by merging technology, accessibility, and personalization. Built using the Design Thinking methodology, the platform focuses on understanding the real needs of both students and tutors. GIGXPERT provides features such as AI- driven tutor matching, interactive HD video sessions, smart scheduling, and secure digital payments, creating a seamless and trustworthy learning environment. The goal is to democratize education by making learning flexible, affordable, and engaging for all learners while offering new opportunities for tutors to grow professionally.

Relaxaa: Integrating Emotional Support Tools and Calming User Experience for Psychological Well-Being

N Ganitha Aarthi — 2026-02-05

Mental health issues like stress, anxiety, and depression are rapidly increasing due to modern lifestyle pressures. Many individuals lack timely emotional support, leading to isolation and worsening mental states. Relaxaa is developed as an online platform to promote mental well-being and provide a safe digital space. It acts as a companion for users seeking comfort, motivation, and emotional guidance. The platform bridges technology and mental-health care through supportive design principles. Users can access stress-relief resources, relaxation techniques, and emotional awareness tools. Calming visuals and simple navigation create a peaceful, user-friendly environment. A soothing color palette enhances comfort and encourages regular engagement. Overall, Relaxaa supports users in achieving emotional balance and ongoing well-being.

Real-Time Syntactic, Semantic, and Logical Error Detection Using AI in Multilanguage Code Editors

Thasni Asharaf — 2026-02-05

The rapid growth of programming technology has made debugging and understanding code increasingly challenging for students and new developers. Traditional IDEs only highlight errors without context, forcing learners to search online or ask others for help, which slows learning and reduces productivity. This project aims to build an AI-powered code editor that identifies syntactic, logical, and semantic errors in real time while pinpointing the exact location of issues. It provides simple explanations, suggests fixes, predicts runtime behavior, and analyzes code quality using machine learning and NLP. Supporting multiple languages like Python, Java, C, and JavaScript, the system learns continuously from student error datasets to improve accuracy. With features like intelligent syntax highlighting, style feedback, and optimization suggestions, the AI editor enhances understanding, speeds up debugging, promotes self-directed learning, and ultimately transforms programming education into a more intuitive and efficient experience

EYEFIT: Integrating Virtual Try-On, Online Eye Testing, and Prescription Management for Smart Eyewear Services

R Tamil Selvi — 2026-02-05

The eyewear industry is essential for eye health and style, but traditional purchasing methods are inefficient. Consumers face issues like limited variety, high costs, inability to try frames easily, and limited access to eye tests, especially in rural areas. This leads to delays and dissatisfaction. To solve this, we propose EYEFIT, an all-in-one digital platform integrating online and offline services. It allows users to browse frames, use virtual try-on (VTO) with AI and AR for personalized recommendations based on face shape, preferences, and budget. Features include home eye tests by professionals, digital prescription management, secure payments, and home delivery. This innovation merges technology, healthcare, and fashion for convenience, affordability, and accessibility, empowering users to make informed decisions from home.

Online Banking System with AI-Based Fraud Detection

R M Mallika — 2026-03-11

The rapid development of online banking services has significantly reshaped the financial industry, and while it has offered greater convenience. It has also created greater risks of fraud. Conventional anti-fraud systems, which are mostly rule-based, are not very effective in keeping up with the ever-changing nature of fraud attempts. The fraud detection system in the financial sector has been facing a major challenge in dealing with the increasing rate of fraud. In order to address this problem, we have proposed a hybrid model for fraud detection in the context of online banking. In our proposed model, we have used supervised classification, anomaly detection, and sequence-based behavioral analysis for the detection of fraud in the context of online banking. We have used various machine learning algorithms like Random Forest, Isolation Forest, and Long Short-Term Memory (LSTM) for the proposed system. We have achieved an impressive 96% accuracy, with a precision, recall, and F1-score of 95%, thereby outperforming the conventional system. We have demonstrated that our proposed system can be used to improve security as well as user experience in the context of digital banking transactions. We have discussed the limitations of the conventional fraud detection system as well as the potential advancements in the proposed system.