Dr. Liyana Rosely is a Senior Lecturer at Taylor’s University, Malaysia, with extensive experience in computer science education, software engineering, and artificial intelligence. She holds a Ph.D. in Computer Science from Universiti Teknologi Malaysia (UTM), where her research focused on hybrid fish swarm algorithms for feature selection and classification of microarray data. Her research interests include artificial intelligence, machine learning, data mining, blockchain, Internet of Things (IoT), software engineering, software quality, and intelligent computing systems. With over five years of academic and research experience, Dr. Rosely has taught a wide range of undergraduate and postgraduate courses, including Data Mining, Big Data Technologies, Software Engineering, Software Testing, Agile Development, Enterprise Architecture, and IT Project Management. She has successfully led and contributed to several funded research projects and has published numerous articles in internationally recognized journals and conferences covering AI, blockchain, cybersecurity, quantum technologies, and data analytics. Dr. Rosely actively contributes to the global research community as a reviewer, editorial board member, session chair, track chair, keynote speaker, and conference organizer. Her commitment to advancing research and innovation, combined with her passion for teaching and interdisciplinary collaboration, has established her as a respected academic dedicated to developing impactful technological solutions for society.

Balancing Artificial Intelligence and Human Decision-Making: Reinforcing Computer Science and Software Engineering Foundations for the Next Generation of Intelligent Computing Systems :The convergence of Artificial Intelligence (AI), quantum computing, and intelligent computing systems is redefining the future of digital transformation by enabling unprecedented computational capabilities, autonomous reasoning, and data-driven decision-making across critical sectors, including healthcare, finance, manufacturing, cybersecurity, education, and smart infrastructure. While these technological advances promise significant improvements in efficiency, scalability, and innovation, they have also intensified the misconception that increasingly intelligent systems will eventually replace human expertise. This paper argues that the future of AI should not be characterized by human replacement but by human augmentation, where intelligent systems enhance cognitive capabilities while preserving human responsibility, ethical judgment, contextual reasoning, and strategic decision-making. Such a paradigm can only be realized through strong foundations in Computer Science and Software Engineering, which remain the cornerstone for developing trustworthy, transparent, secure, and sustainable intelligent systems. Fundamental competencies in computational thinking, algorithms, data structures, software architecture, distributed systems, cybersecurity, machine learning, software quality assurance, and systems engineering enable professionals to understand, validate, govern, and continuously improve AI-driven applications rather than merely consume AI-generated outputs. As intelligent systems become increasingly autonomous and quantum computing accelerates computational performance and optimization, the demand for professionals capable of integrating AI into robust software ecosystems while ensuring explainability, resilience, fairness, privacy, and regulatory compliance will continue to grow. This highlights the urgent need for higher education institutions to redefine computing curricula by embedding AI literacy, decision intelligence, ethical AI, and interdisciplinary problem-solving within a rigorous Computer Science and Software Engineering framework. The next generation of intelligent computing systems should therefore be designed according to a human-in-the-loop paradigm, where AI provides predictive intelligence, adaptive learning, and computational efficiency, while humans retain authority over high-impact decisions requiring ethical accountability, creativity, and domain expertise. Rather than measuring AI advancement by the degree of human substitution, the success of future intelligent systems should be evaluated by their ability to amplify human intelligence, improve collaborative decision-making, and create resilient socio-technical ecosystems that combine computational excellence with human values. As AI and quantum computing continue to reshape the technological landscape, reinforcing the fundamental principles of Computer Science and Software Engineering will be essential for ensuring that intelligent systems remain reliable, explainable, secure, and ultimately serve as instruments of sustainable innovation rather than replacements for human potential.