Shaping Education Through Innovation: Voices from Singapore

In the face of complex global challenges and the rapid evolution of technologies, education systems worldwide are being called to adapt, evolve, and reimagine their purposes and practices (OECD, 2022). In Singapore, these calls have been met with bold experimentation, thoughtful redesigns, and grounded innovations that reflect both global trends and local imperatives (Chan, 2025). Anchored in the Singaporean context, this symposium brings together a constellation of practitioner-led and research-informed initiatives that illuminate how educators and researchers are collaboratively shaping the future of learning. The first paper examines a work-study pedagogical model that positions learning at the boundary (Akkerman and Bakkar, 2011) between academic study and professional practice. Drawing on cultural-historical activity theory (Engestrom, 2014), this initiative reimagines how educators develop professional competencies by engaging with authentic workplace challenges while receiving dual guidance from academic supervisors and workplace mentors. Rather than viewing work and learning as separate domains, this innovation proposes a hybrid space where contradictions between theory and practice fuel expansive learning. It also sheds light on how educators themselves evolve as pedagogical designers in such boundary-crossing contexts. Two cases will be presented to showcase how innovation in pedagogy can be both contextually responsive and deeply transformative for the educators. Automated formative assessment offers potential time and cost savings for educators, yet fully automated systems often struggle with reliability, trustworthiness, and alignment with human evaluators (Chamieh et al., 2024; Riordan et al., 2024). Large Language Model (LLM)-assisted grading provides a balanced alternative by incorporating human oversight while leveraging the efficiency of machine capabilities (Bexte et al., 2024; Brown et al., 2020). To achieve human/educator-centered design in automatic grading, it is essential to consider both the machine’s reliability as well as the information exchange between the human-machine interface. In this study, we explore: 1) whether LLMs, guided by predefined rubrics, can effectively evaluate student work, 2) which prompt-based strategies can improve LLM reliability, and 3) how to design a human-in-the-loop system that reduces educators’ grading burden without sacrificing their ability to gain a rich, comprehensive view of students’ strengths and weaknesses. Our findings may offer insight into the practical potential of LLM-assisted grading and provide guidance on implementing collaborative grading tools effectively. The third paper explores how higher education is evolving with the rise of AI and digital technologies, highlighting the opportunities and challenges that come with their increasing adoption by institutions. In Singapore, an educational innovation program termed Cyber-Physical Learning (CPL) has been conducted. The aim of CPL is to address the future of education and develop forward-looking educational models, technology tools, and pedagogies for a new form of learning, where the concept is that both remote cyber students and face-to-face physical students can learn and interact effectively, seamlessly, and synchronously. CPL involves innovations of various streams of technologies such as immersive technologies, telepresence robotics, and personalized learning, and is powered by Learning Science and Educational Technology. CPL is relevant to both STEM and the future of STEAM education. This paper shares experiences, results, strategies, and challenges in innovating cyber-physical learning to shape the future of education. The fourth paper highlights the need for teachers to be adaptable and embrace lifelong learning through ongoing reflection and pedagogical refinement in today’s evolving educational landscape. It illustrates the journey undergone by NIE to create, test and embed an AI-powered chatbot into its ubiquitous Digital Portfolio; itself already a core component of NIE's initial teacher preparation programme. This AI-powered chatbot provides student teachers with immediate, detailed feedback on their written reflections, in accordance with established educational frameworks. This paper will cover how such AI-powered chatbots have the potential to assist our student-teachers in developing an active habit of reflection, increase their motivation, and improve their metacognitive awareness as they plan and execute their lessons. This final paper turns to secondary school students, highlighting one example of how technology is being repurposed to support well-being and mental health. In an education system known for its academic rigour, this pedagogical and technological innovation based on Martin Seligman’s (2011) PERMA model, introduces an intervention that promotes emotional literacy, mindfulness, peer connections, meaning, purpose, and the celebration of small wins. The paper explores how in positive education programmes, educators can use technology not to add more pressure, but to relieve it. This paper illustrates how innovation can foster not only smarter but flourishing and thriving learning environments. Research efforts to evaluate the impact of the innovation, via mixed methods approaches show some promising findings. References: Akkerman, S. F., & Bakker, A. (2011). Boundary crossing and boundary objects. Review of Educational Research, 81(2), 132–169. Bexte, M., Horbach, A., Schützler, L., Christ, O., & Zesch, T. (2024). Scoring with confidence? – Exploring high-confidence scoring for saving manual grading effort. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024) (pp. 119–124). Association for Computational Linguistics. Brown, T., Mann, B., Ryder, N., et. al. (2020). Language models are few-shot learners. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in neural information processing systems (Vol. 33, pp. 1877–1901). Curran Associates, Inc. Chamieh, I., Zesch, T., & Giebermann, K. (2024). LLMs in short answer scoring: Limitations and promise of zero-shot and few-shot approaches. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024) (pp. 309–315). Association for Computational Linguistics. Chan, C. S. (2025, February 11). 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