Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Exploring the Usage of AI Tools in Education: Insights from Gen Z Undergraduates in Sri Lanka(University of Nigeria Department of Mass Communication, 2025-06-02) Nishshanka, N; Karunarathna, N; Dayapathirana, N; Karunarathna, R. V; Hewage, H. K; Anthony, PBackground: This study investigates the patterns of use and adoption of AI tools in Sri Lanka, with a special emphasis on Generation Z undergraduates who will enter the industry next. As AI is an emerging technology, how this generation interacts with and enriches knowledge through AI tools becomes a vital area of concern. Objective: To identify key subjective factors influencing the adoption and usage of AI tools in education among Gen Z undergraduates in Sri Lanka. Methodology: This study employs qualitative research methods, specifically semi-structured interviews, to gather insights from 18 university students across various disciplines. Thematic analysis was used to identify recurring themes related to undergraduates' subjective experiences, benefits received, and attitudes, for which MAXQDA is used as the analytical software. Results: The findings demonstrate four key subjective factors that influence adoption and usage, such as academic work, awareness and adoption, challenges and risk, and helpful and supportive factors. The frequently used AI tool in Sri Lanka was noted as ChatGPT, which showed a high usage pattern in the analysis. Conclusion: Understanding the usage patterns and adoption factors helps the community use AI tools effectively, as it makes them aware of the risks and helpful factors. Also, the facilities that aid in adopting these AI tools could elevate the efficiency of their usage by making many students, future undergraduates, AI developers, and educational institutions aware of its benefits. Unique Contribution: This research provides insights for future research by helping to understand the usage of emerging AI tools among Gen Z undergraduates in a developing country like Sri Lanka. The findings can be applied to understanding different generations and emerging generations, such as Generation Alpha.Publication Open Access Breaking the cycle: long-term socio economic determinants of child labour in SAARC countries(BioMed Central Ltd, 2025-11-19) Magammana, T; Muthugala, H; Bandara, A; Perera, A; Jayathilaka, RBackground: Child labour remains a critical issue in SAARC countries, driven by various socio-economic factors. While previous studies have explored individual determinants, limited research has been conducted on their collective long-term impact. Understanding how structural and economic conditions shape child labour trends is essential for designing effective policy interventions. Methods: This study engages panel cointegration techniques to examine the long-term relationship between child labour and key socio-economic drivers in SAARC countries. It assesses the impact of education, access to healthcare, economic conditions, labour market dynamics, foreign investment, and urbanisation on the prevalence of child labour. Results: The findings confirm a stable, long-term relationship between child labour and these determinants in each SAARC country. Improvements in education and health significantly reduce child labour. However, economic growth and urbanisation have complex, country-specific effects. Higher unemployment and increased FDI may also influence child labour, emphasising the need for targeted policy responses. Conclusions: The study highlights the significance of ongoing investments in education and healthcare. Labour market reforms are crucial to mitigate the impact of unemployment, while inclusive economic policies ensure that growth benefits vulnerable populations. Targeted strategies for FDI and urbanisation are necessary to prevent unintended consequences on child labour. Combating child labour in SAARC countries requires a multi-sectoral approach. Regional collaboration is crucial for sharing best practices, developing unified strategies, and enhancing cross-border initiatives. Holistic policies integrating education, health, and economic planning are key to reducing child labour.Publication Open Access Factors influencing migration intention of undergraduates in Sri Lanka: ‘About more than employment(Elsevier Ltd, 2026-01-26) Marawila, R; Weerarathna, R; Rathnayake, N; Guruge, R; Wehella, B; Udugahapattuwa, T; Weligodapola, MThe objective of this study is to examine the factors influencing Sri Lankan undergraduates' intention to migrate. Persistent economic, social, and political challenges have driven many youngsters and professionals to leave their Country of Origin (COO). The economic collapse triggered by COVID-19 further intensified this trend, leading to a sharp increase in outward migration. Recently, a growing number of Sri Lankan undergraduates and skilled professionals have expressed a strong desire to relocate abroad, often immediately after completing secondary education. For this study, a sample of 385 undergraduates from state and non-state universities across Sri Lanka was analysed. Given the national concerns of brain drain and shortages of trained and skilled workers, the study specifically focused on understanding undergraduates' aspirations to migrate. Structural Equation Modeling (SEM) was applied to identify and test the variables influencing migration intentions within the Sri Lankan context. The findings provide a holistic picture of the drivers of undergraduate migration. These carry important implications not only for students but also for policymakers and Higher Education Institutions (HEIs), by informing policies and strategies that could encourage young people to realise their potential within Sri Lanka rather than abroad.Publication Embargo Revolutionalize Your Learning Experience with EQU ACCESS(IEEE, 2024-07-25) Raveenthiran, G; Sivarajah, K; Kugathasan, V; Chandrasiri, S; Mohamed Riyal, A. A; Rajendran, KThis paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners.Publication Embargo A Comprehensive Mobile Platform for Fostering Communication, Literacy, Numeracy, and Emotion Understanding in Children with ASD(IEEE, 2024-07-25) Bandara, T.W.M.I.P.S; Deshan, M.A.D.; Prasanth, P.; Nadeera, M.S.; Krishara, JThis study presents SIPNENA, a novel mobile application designed to aid the learning and communication development of Sinhala-speaking autistic children aged six, particularly in rural areas of Sri Lanka. It offers a unique approach to teaching challenging subjects like English and Mathematics, tailored to the specific needs of children with Autism Spectrum Disorder (ASD). The application integrates interactive methodologies and gamification elements to facilitate better communication, understanding, and engagement. Additionally, it incorporates real-time emotion recognition features to monitor and respond to children's emotional states during learning activities. This research evaluates SIPNENA's effectiveness in improving communication abilities, academic skills, and emotion understanding among autistic children. The findings indicate promising results in catering to the unique educational needs of this target population, particularly in under-resourced rural regions, where specialized interventions are often scarce.Publication Embargo E-tutor: Comprehensive Student Productivity Management System for Education(IEEE, 2022-12-09) Silva, K; Induwara, R; Wimukthi, M; Poornika, S; Samaratunge Arachchillage, U.S.S; Jayalath, TWith the advancement of technology, e-learning has emerged as predominant in the education sector. As students, parents, and educators acknowledged, adopting e-learning can offer several benefits over traditional learning techniques. Since more individuals are becoming acclimated to online learning platforms, these online platforms can provide a simple, instructive, and efficient mode of delivery. This novel approach could be improved with the aid of Artificial Intelligence (AI) to comprehend consumers more thoroughly and provide valuable and better-suited services. Most sectors in education, including universities, swiftly adapted to new educational methodologies because of their flexibility and productivity. Nevertheless, there are some downsides that young demography experiences, such as less instructiveness, distraction due to the absence of teachers, and poor IT literacy. Consequently, these drawbacks would recede the capability of students to assimilate content during the lecture. Therefore, the main objective of this research is to implement an E-learning platform with AI learning analytics to enhance students’ performance regularly while reducing the significant drawbacks of the E-learning platforms. This research consists of students’ focus detection, essay-based answer evaluation, note summarization, mind map generation, and personalized guidance facilities.Publication Embargo IELTF: An ICT-based Framework to Leverage English Language Education in Sri Lanka(IEEE, 2018-08-08) Weerakoon, U; Manage, R; Wijekoon, JThis paper aims to develop an ICT based English Language Teaching Framework (IELTF) for the students of Sri Lanka to overcome the complications of learning English language in secondary education. The English language is an international language and proper English education is essential for the betterment of the students lives. According to a survey (using both students and English teachers) and several research findings, we observed that there are several issues in the English education in Sri Lanka for the students who are following their secondary education. Such phenomenon make the most students struggle learning and understanding the English language, and hence, the teachers struggle teaching the English Language effectively. Recently Information and Communication Technology (ICT) is gaining momentum upgrading societies to the smart societies. To this end, this paper proposes a novel notion of using ICT for effective English education and thereby improve the teaching and learning quality of the English language in Sri Lankan secondary education.Publication Embargo Improved robot attitudes and emotions at a retirement home after meeting a robot(IEEE, 2010-09-13) Stafford, R. Q; Broadbent, E; Jayawardena, C; Unger, U; Kuo, I. H; Igic, A; Wong, R; Kerse, N; Watson, C; MacDonald, B. AThis study investigated whether attitudes and emotions towards robots predicted acceptance of a healthcare robot in a retirement village population. Residents (n = 32) and staff (n = 21) at a retirement village interacted with a robot for approximately 30 minutes. Prior to meeting the robot, participants had their heart rate and blood pressure measured. The robot greeted the participants, assisted them in taking their vital signs, performed a hydration reminder, told a joke, played a music video, and asked some questions about falls and medication management. Participants were given two questionnaires; one before and one after interacting with the robot. Measures included in both questionnaires were the Robot Attitude Scale (RAS) and the Positive and Negative Affect Schedule (PANAS). After using the robot, participants rated the overall quality of the robot interaction. Both residents and staff reported more favourable attitudes (p <; .05) and decreases in negative affect (p <; .05) towards the robot after meeting it, compared with before meeting it. Pre-interaction emotions and robot attitudes, combined with post-interaction changes in emotions and robot attitudes, were highly predictive of participants' robot evaluations (R = .88, p <; .05). The results suggest both pre-interaction emotions and attitudes towards robots, as well as experience with the robot, are important areas to monitor and address in influencing acceptance of healthcare robots in retirement village residents and staff. The results support an active cognition model that incorporates a feedback loop based on re-evaluation after experience.Publication Embargo Teaching a tele-robot using natural language commands(IEEE, 2005-11-07) Jayawardena, C; Watanabe, K; Izumi, KFor Internet-based teleoperation systems, user-friendly natural interfaces are advantageous because those systems are intended to be used by non-experts. In developing user friendly interfaces, natural language communication is mandatory. This work presents a system in which a sub-set of natural language is used to command a tele-robot manipulator doing an object sorting task. The paper discusses about referring to objects with natural language commands such as "pick the small red cube". This is achieved by learning individual lexical symbols that refer to colors, shapes, and sizes independently, and then inferring the meaning of a combination of them.Publication Embargo Improved robot attitudes and emotions at a retirement home after meeting a robot(IEEE, 2010-09-13) Stafford, R. Q; Broadbent, E; Jayawardena, C; Unger, U; Kuo, I. H; Igic, A; Wong, R; Kerse, N; Watson, C; MacDonald, B. AThis study investigated whether attitudes and emotions towards robots predicted acceptance of a healthcare robot in a retirement village population. Residents (n = 32) and staff (n = 21) at a retirement village interacted with a robot for approximately 30 minutes. Prior to meeting the robot, participants had their heart rate and blood pressure measured. The robot greeted the participants, assisted them in taking their vital signs, performed a hydration reminder, told a joke, played a music video, and asked some questions about falls and medication management. Participants were given two questionnaires; one before and one after interacting with the robot. Measures included in both questionnaires were the Robot Attitude Scale (RAS) and the Positive and Negative Affect Schedule (PANAS). After using the robot, participants rated the overall quality of the robot interaction. Both residents and staff reported more favourable attitudes (p <; .05) and decreases in negative affect (p <; .05) towards the robot after meeting it, compared with before meeting it. Pre-interaction emotions and robot attitudes, combined with post-interaction changes in emotions and robot attitudes, were highly predictive of participants' robot evaluations (R = .88, p <; .05). The results suggest both pre-interaction emotions and attitudes towards robots, as well as experience with the robot, are important areas to monitor and address in influencing acceptance of healthcare robots in retirement village residents and staff. The results support an active cognition model that incorporates a feedback loop based on re-evaluation after experience.