Invited Speaker_ICIET 2026

Prof. Wernhuar Tarng, National Tsing Hua University, Hsinchu, Taiwan

Professor Wernhuar Tarng received his MS and PhD degrees in electrical and computer engineering from the State University of New York at Buffalo in 1987 and 1992, respectively. He is currently a professor in the Graduate Institute of Learning Sciences and Technologies at National Tsing Hua University, Taiwan, where he has made significant contributions to both research and teaching. His research focuses on the integration of virtual reality, augmented reality, and artificial intelligence in educational applications, particularly for teaching complex subjects such as nanotechnology, robotics, and semiconductor manufacturing processes.

Dr Fai-hang LO, The Chinese University of Hong Kong， Hong Kong

Dr FH Lo is currently a Lecturer in the School of Life Sciences at the Chinese University of Hong Kong (CUHK). Since 2009, he has been teaching undergraduate and postgraduate biochemistry courses at CUHK, as well as general education courses offered by the University and Colleges. In addition to his regular teaching duties, Dr Lo serves as an instructor for the Faculty of Science and the Faculty of Education at CUHK. He is are interested in pedagogical research and innovation. In recent years, he actively participates in public service and academic meeting both locally and internationally, and is committed to promoting gifted education and science education.

Speech Title: Artificial intelligence facilitated learning analytics study to enhance transferable skill development
and career preparedness in Biochemistry education

Abstract: Biochemistry education faces escalating challenges in developing students’ transferable skills essential for modern workplace success. This study exemplified the integration of artificial intelligence (AI) and learning analytics for systematic identification of skill deficiencies among the students that inform relevant pedagogical interventions to enhance their career development. Our longitudinal analysis of 13 years of official examination data (2009-2025) revealed critical gaps in core transferable skills: 1) scientific communication, 2) data visualization, 3) critical thinking, and 4) problem-solving abilities. Learning analytics identified that about half of students failed to construct proper graphs, ~75% demonstrated inadequate scientific writing skills, and ~60% struggled to apply knowledge to real-life scenarios. Performance declined by 16% over the past eight years, with persistent bipolar distributions indicating ineffective support for diverse learners from different academic backgrounds.
This research demonstrates AI's role in speeding up the processing of vast educational datasets to reveal patterns invisible to traditional data formats. Moreover, machine learning algorithms identified recurring misconceptions across cohorts and predicted students requiring early intervention, enabling timely support that reduced failure rates by close to 20%.
We propose integrating AI-driven learning analytics into clinical biochemistry curriculum design to develop critical transferable skills aligned with industry demands. Recommendations include implementing AI-powered personalized learning pathways, real-time competence tracking, and predictive intervention systems that foster data visualization proficiency, scientific communication skills, and critical reasoning capabilities essential for professional advancement.
This work illustrates how AI and learning analytics, when strategically applied to educational data, could transform biochemistry education into a career-ready training process. By systematically addressing skill deficiencies revealed through the analytics, institutions can better prepare graduates for evolving professional landscapes demanding advanced analytical, communication, and logical reasoning skills for real-life problem-solving.