Biography
Senior Lecturer, Faculty of Engineering Technology at Universiti Tun Hussein Onn Malaysia (UTHM) Rama Yusvana’s academic background is biotechnology with some experience (training) in Genomic Data Science and Bioinformatics. They obtained PhD from School of Engineering & Physical Science of Heriot-Watt University (Edinburgh, UK), Master of Science (MSc) from University of New South Wales, Sydney-Australia and Bachelor of Science (BSc) from University of Nottingham, UK.
Conference contributions 1
BEYOND AUTOMATION:
A human-centric framework for responsible agentic AI in digital TVET
Digital TVET is now expected to prepare learners for AI-mediated workplaces across rapidly changing professional and industrial sectors, yet many institutional responses still treat artificial intelligence as a tool for automation rather than a supervised support system for human development. This gap is visible in practice: students encounter AI tools without structured guidance, while educators manage implementation without clear ethical or pedagogical frameworks to draw on. This conceptual paper proposes a human-centric framework for responsible Agentive AI in Digital TVET, with specific attention to learning support, student readiness, teacher agency and ethical governance. The framework is informed by an ongoing research programme examining student readiness and AI integration challenges across Malaysian TVET settings. It positions Agentive AI as an LMS-integrated learning companion capable of providing guided hints, formative feedback, practice prompts, reflective questioning and learning-path recommendations within boundaries set by educators and institutions. Rather than replacing teaching judgement, the model defines AI as a pedagogical layer that strengthens self-directed learning while keeping teachers responsible for curriculum alignment, intervention decisions and assessment integrity. The framework integrates six interrelated dimensions: digital and AI literacy, equitable technology access, pedagogical alignment, student agency and well-being, explainable learning analytics and institutional safeguards covering privacy, bias, transparency and responsible use. The paper offers TVET institutions a practical pathway from ad hoc AI use toward structured, ethical and learning-centred implementation. Its broader contribution frames AI adoption in TVET as an institutional commitment to educational quality, inclusion and social responsibility, rather than a purely technical or operational upgrade.