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Item Embargo From AI Assistance to Critical Thinking: Exploring Cognitive Offloading and Metacognition as Mechanisms within Personalized Learning Environments(Institute of Electrical and Electronics Engineers Inc., 2026) Gunathilake, N; Gamage, A; Rajapakshe, D; Jayasooriya, M; Wisenthige, K; Yapa, C.GThe rapid adoption of AI-assisted learning tools in higher education has completely transformed the undergraduate study system, but empirical evidence on their impact on deep cognition and learning processes is limited. This study investigates the effects of AI-assisted learning tool usage on metacognition, cognitive offloading, personalized learning, and critical thinking among Sri Lankan undergraduates. Using positivism philosophy and a deductive quantitative approach, data were collected from 379 students in computing, management, and engineering subjects through a 35-items, five-point Likert scale questionnaire, and analyzed using PLS-SEM. The findings reveal significant direct, indirect, and moderating relationships among key variables, suggesting that the use of AI-assisted learning tools influences students’ learning regulation, reliance on external support, and development of higher-order thinking. This study provides empirical evidence for the cognitive and psychological effects of AI-assisted learning tools, and helps address an important research gap, and offers practical insights for educators, curriculum developers, and policymakers to use balanced and effective AI integration in higher education.Item Embargo A Comprehensive Approach to Secure, Accessible, and Engaging Voting Systems(Springer Science and Business Media Deutschland GmbH, 2026) Jayasinghe J.A.M.P; Bandara S.Y.T.D; Shabry S.M; Wickramasinghe W.A.R.M.; Rajapakse, K; Silva, NThis research presents a secure and accessible e-voting system for polling booths in Sri Lankan context, to overcome issues with the traditional voting system. It incorporates block-chain for fair vote storage, and homomorphic encryption for privacy preserving computation of results. The identity of voters is confirmed by face recognition, which includes measures to deterring any voting by impostors. Special identification model with multiple digits is beneficial for disabled voters. Public opinion is effectively gauged through sentiment analysis from social media and it puts concerns to rest, thus a whole lot of enhancement in the whole of customer engagement. Ease of use is also assured thanks to a very user-friendly interface which eliminates mistakes a lot with only a little effort generally. Experimental results demonstrate that security is greatly strengthened, transparency and usability are significantly increased traditional procedural integrity is still maintained efficiently.Item Embargo Interactive Sinhala Letter Learning Module for School Children (Grade 1 to 5)(Springer Science and Business Media Deutschland GmbH, 2026) Weerasooriya, K; Udana, I; Jayasinghe, L; Kasiwaththa, J; Rajapaksha, S; Kumari, SSinhala is the native language of most people in Sri Lanka. However, most of the children find it difficult to write Sinhala letters fast and accurately, this may undermine their confidence and affect grades. The primary issue is that the parents usually lack their time in order to assist their children in their studying at home. Few interesting tools also exist to teach children how to write in Sinhala in an interesting and effective manner. To address these issues we have developed the ”Interactive Application of the Sinhala Language to School children (Grade 1 to 5) which is a web based application, to allow children studying in primary schools to enhance their knowledge of the Sinhala language. This app provides children an entertaining and effective method of learning how to write Sinhala letters. The system combines instructions in animation, touch tracing finger tools, hand writing recognition and immediate feedback such that kids can learn Sinhala writing, and the non touch screen users can post their written letters on paper to be analyzed individually as to feedback analysis. The system uses handwriting recognition to provide real-time feedback on accuracy and speed. The system also monitors progress and generates comprehensive reports to help children and parents in identifying areas requiring improvement. The application uses a combination of engaging letter tracing and intensive deep learning which are not present in other learning tools. Additionally, the system will aid parents to mentor their children in education even when they are in charged schedules and also enable children improve their skills in Sinhala writing. We offer to make the learning of Sinhala to school students in Sri Lanka easier, more relevant and interesting.Item Embargo Post-Quantum Cryptography for Web Authentication Protocols: A Systematic Review of OAuth 2.0, OpenID Connect, and SAML Migration(Institute of Electrical and Electronics Engineers Inc., 2026-03-19) Dissanayake, R; Wijesinghe, H; Vindinu, J; Jayasinghe, K; Abeywardena, K; Senarathne, AOAuth 2.0, OpenID Connect (OIDC), and SAML rely on classical public-key primitives such as RSA and ECDSA, which are vulnerable to quantum attacks via Shor's algorithm. This systematic review examines migration of these protocols to Post-Quantum Cryptography (PQC) following the 2024 NIST standardization of ML-DSA and ML-KEM. We map cryptographic dependencies across all three protocols, evaluate NIST-standardized algorithms for authentication use cases, and analyze practical migration challenges. Token size explosion, with ML-DSA-65 signatures approximately 52 times larger than ECDSA P-256, represents the dominant implementation barrier, compounded by incomplete JOSE standardization and limited ecosystem maturity. Missing formal security proofs and federation migration frameworks are identified as critical priorities before production deployment.Item Embargo CodeHarbor: A Code Analysis Tool(Springer Science and Business Media Deutschland GmbH, 2026) Dewmin T.Y; Kodithuwakku Y.S.; Dayananda I.H.M.B.L; Fernando K.R.A.W; De Silva D.I; Rathnayake S.As software systems grow more complex, developers face increasing challenges in maintaining and evolving codebases, often resulting in higher costs and longer development cycles. To address these issues, this study presents CodeHarbor, an intelligent tool that integrates machine learning with code analysis to simplify complex code segments. CodeHarbor calculates complexity metrics and offers personalized, context-aware suggestions for improving code quality. By automating code reviews, detecting anomalies, and recommending optimized refactoring strategies, it enables early issue resolution and enhances maintainability. The backend leverages artificial intelligence to identify patterns, enforce coding standards, and generate actionable insights, while the intuitive frontend provides real-time feedback, visualizations, and detailed improvement summaries. CodeHarbor also highlights repetitive patterns and compliance issues, helping developers track progress and reduce manual review effort. With its seamless integration of analysis and interface, CodeHarbor streamlines development workflows and promotes sustainable, high-quality software engineering.Item Embargo Mitigating Human Bias in Candidate Evaluation Through an AI-Driven Multimodal Assessment System(Springer Science and Business Media Deutschland GmbH, 2026) Gunarathna B.M; Thennakoon I.C; Anjalie S.; Pinsara D.; De Silva D.I; Gunathilake M.PThe recruitment process is often marred by human bias and inconsistent evaluations, especially when relying on traditional interview techniques. This paper introduces a scalable AI-driven, multimodal interview system designed to deliver objective and comprehensive assessments of job candidates. The proposed framework integrates natural language processing, computer vision, and code quality analysis to evaluate both technical skills and interpersonal attributes. It features four key components: (1) automated skill and professionalism assessment through resume parsing and behavioral analysis during a mock exam, (2) voice-based confidence evaluation using speech feature ex-traction and integrates natural language processing, (3) a gamified technical interview environment with real time stress detection via facial expression analysis, and (4) code complexity and maintainability analysis employing Cyclomatic Complexity, Cognitive Function Complexity, and Weighted Code Complexity metrics. Experimental results demonstrate that the system provides accurate, bias-reduced evaluations across technical and non-technical domains. By unifying behavioral and technical metrics, this approach offers a fair, efficient, and data driven alternative to conventional hiring methods.Item Embargo Evaluating Large Language Models for Software Testing: A Systematic Review of Metrics and Practices(Springer Science and Business Media Deutschland GmbH, 2026) Perera V.I.T; De Silva D.I.The recent advancements in Large Language Models (LLMs) present substantial potential for revolutionizing software testing practices, particularly through automated test case generation. This review synthesizes contemporary research on LLM-driven software testing methods, with a specific focus on evaluation metrics. A systematic literature review was conducted using databases like IEEE Xplore, ResearchGate, and Google Scholar, targeting literature published between 2020 and 2024, specifically focusing on LLM-based test case generation. Selection criteria included relevance to automated testing and practical application insights. This review analyzes 15 key studies that span multiple test domains, and the key findings reveal significant advancements in using LLMs for diverse testing types, including unit, property-based, security, and user acceptance testing. Despite substantial benefits, issues such as test case validity, reliability, and prompt engineering complexity remain challenging. The review concludes with recommendations for developing a standardized metric-driven evaluation framework for better assessing LLM-generated tests. This comprehensive approach aims to effectively measure and optimize the practical utility and reliability of LLM-generated software tests, ultimately guiding future research directions and improving adoption within the software industry. The key contribution of this review is a comprehensive metric-focused evaluation of LLM-driven software testing techniques offering a foundation for developing standardize evaluation methodologies and practical testing frameworks.Item Embargo Predictive Models for Urban Air Quality Management Using AI(Institute of Electrical and Electronics Engineers Inc., 2026-03-19) Liyanage, D; Vithanage, N; Wijewardane, I; Fernando, N; Wijendra, D; Dassanayake, TAir pollution threatens public health in datascarce urban areas like Sri Lanka, where sparse monitoring hinders proactive management. We propose an integrated AI framework: hybrid SARIMAX-Temporal Fusion Transformer for multi-pollutant forecasting, ensemble spatial estimation for gap-filling, CEEMDAN-Seq2Seq for 24-hour AQI risk alerting, GRU for anomaly detection, and XAI for transparency. Validated on Central Environmental Authority data (20192024), the model achieves an 81.6% decrease in the value of the RMSE metric for ozone forecasting, as well as an R2 value of 0.9077 for high-risk AQI prediction, outperforming the baseline methods by 15-81%. The framework is modular in nature, thereby providing policymakers with the ability to use real-time dashboards, thus making Sri Lanka move from reactive to proactive management.Item Open Access An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka(SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, MChronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.Item Embargo Adaptive Voice Communication in Emotion-Aware Digital Companions(Institute of Electrical and Electronics Engineers Inc., 2025) Rathnayake, P; Rathnaweera, C; Jithma, U; Aththanayake, I; Rathnayake, S; Gunaratne, MThis paper presents an adaptive voice communication system for emotion-aware digital companions that dynamically responds to users' affective states through expressive speech and synchronized 3D avatar animation. The system integrates real-time voice input, emotion recognition, and context-aware dialogue generation using GPT-3.5, followed by emotional text-to-speech synthesis via neural TTS. Lip-sync data is generated using phoneme alignment and rendered in sync with the avatar's facial expressions and gestures. To enhance user trust and engagement, the avatar visually mirrors the emotional tone of the speech. A cultural adaptation layer is introduced to align voice output and speech style with Sri Lankan communication norms, including tone, pacing, and formality. Implemented using a Node.js backend and React + Three.js frontend, the system demonstrates strong potential for emotionally intelligent, culturally adaptive AI interactions. This work contributes a modular pipeline for building empathetic voice agents capable of enhancing realism and trust in human-AI communication.