Keynotes, Workshops & Invited Talks

DAY 1

Morning

Keynote Abstract

Professor Joseph Sung will inaugurate the conference with a visionary keynote, calling on stakeholders to lead with ethics, educate with purpose and partner with AI to shape a human-centric future. Drawing on his leadership in medicine and academia, Prof Sung will examine AI’s transformative impact on society and healthcare - highlighting both its extraordinary potential and inherent dual-use risks. Addressing bias, transparency, privacy and accountability, Prof Sung will challenge educators and institutional leaders to frame inclusive conversations about AI adoption. He will underscore the importance of balancing technological progress with human values and wisdom in care delivery and education. Emphasising the imperative to prepare professionals across disciplines, he calls for multidisciplinary collaboration and agentic learning that empowers educators and students as responsible, creative partners in human‑AI innovation, echoing the theme “Learning About, With, and Beyond AI.

Biography

Professor Joseph Sung is a globally recognised leader and researcher in gastroenterology, hepatology and health professions education. Currently serving as Dean of the Lee Kong Chian School of Medicine, Nanyang Technological University, and Senior Vice President (Health and Life Sciences) at NTU, he guides the university’s vision in healthcare innovation, research and education. Internationally, Professor Sung is renowned for advancing colorectal cancer screening across Asia, championing innovations in digestive health, microbiome science and the use of AI in medicine. With over 1,000 publications in leading journals and recognition as a highly cited researcher, his significant contributions continue to shape clinical practice, preventive healthcare and medical education worldwide.

Keynote Abstract

In an age where artificial intelligence can provide instant answers and solutions, the traditional approach to learning is being fundamentally challenged. This talk explores the evolving role of education and examines what critical skills students must develop to thrive in a world dominated by AI-powered knowledge access. Rather than focusing solely on information acquisition, students need to cultivate skills in critical thinking, creativity, ethical reasoning, and adaptability. These capabilities enable learners to interrogate AI outputs, innovate beyond automated responses, and apply knowledge in complex, real-world contexts. The talk argues that learning is not obsolete; instead, it must transform to emphasize judgment, problem-solving, collaboration, and lifelong learning in partnership with intelligent technologies.

Biography

Professor Simon See is currently the Solution Architecture and Engineering Director, Chief Solution Architect and Global Head for NVIDIA AI Technology Center, NVIDIA Corporation. He is also an Adjunct Professor at Shanghai Jiao Tong University, Conventry University, and Universitas Indonesia (UI) and Newcastle University in Singapore. He is a distinguished fellow in Fudan University. Previously, Professor See also served as the Chief Scientific Computing Advisor for BGI (China) and held positions at Nanyang Technological University (Singapore) and King Mongkut's University of Technology (Thailand). Professor See is currently involved in several international computational and mathematical science projects, as well as national AI initiatives. Recently, he has been appointed as the Executive Director of the ASEAN Applied Research Centre (AARC). His research interests are in the area of High-Performance Computing, Big Data, Artificial Intelligence, Machine Learning, Computational Science, Applied Mathematics and Simulation Methodology. Professor See is also leading some of the AI initiatives in Asia Pacific. He is a Steering Committee member of NSCC’s flagship High Performance Computing Conference Supercomputing Asia (SCA) since March 2018. He has published over 200 papers in these areas and has won various awards in the field

From Intelligent Agents to Intelligent Learners: Co-Creating Education through Judgment, Taste, and Task-Aware AI
(short sharing by Prof Song Jie)

Abstract

As AI agents increasingly perform complex tasks, education must shift from knowledge delivery toward cultivating learners’ ability to guide and evaluate intelligent systems. This talk proposes a new paradigm focused on judgment, taste, and goal-aware collaboration with AI.

We outline how AI transforms assessment, why critical judgment and conceptual refinement matter more than ever, and how agent-based platforms like DataAgent support co-creative, reflective learning. By embedding AI into authentic tasks, we empower students not just to use AI, but to think with and beyond it—redefining what it means to learn in an intelligent future.

Panellist

Biography

Professor Song Jie is a leading scholar at the School of Advanced Manufacturing and Robotics, Peking University, holding a PhD in Management Science and Engineering from Tsinghua University. Her research expertise spans data security, digital transformation, intelligent algorithms, data valuation, and energy system optimization. She has pioneered a data-driven AI modeling and quantitative decision-making framework that integrates "data analytics, model construction, algorithm design, and simulation optimization" to address complex challenges in economic and safety coordination. Her contributions include proposing a shared reserve scheme for food security across 31 provinces, which has been adopted in China’s National 14th Five-Year Plan for Food Security, and leading the development of a data-value-driven multi-tier power dispatching system deployed in provincial and regional grids to strengthen energy security. With over 100 high-impact publications in top journals such as sub-journals of Nature and Cell, IISE Transactions, and IEEE Transactions on Automatic Control, her work has been recognized by the IISE Transactions Best Paper Award, IEEE TASE Best Paper Award, IEEE Distinguished Speaker Award, and a Special Gold Medal at the Geneva International Exhibition of Inventions.

Professor Mutlu Cukurova is Professor of Learning and AI in Education at University College London (UCL). He investigates human–AI complementarity in education, focusing on how to prepare learners for a future with AI systems that require more than routine cognitive skills. His research examines how people learn to learn and solve complex problems collaboratively, using transdisciplinary approaches that draw on the Learning Sciences, Computer Science, and Human–Computer Interaction. At UCL Knowledge Lab, he leads large-scale research projects, supervises postgraduate and postdoctoral researchers, and teaches the Design and Use of AI in Education module in the Education and Technology programme. Beyond research and teaching, he advises on AI in Education policy as an external expert for organisations such as UNESCO, OECD, and the European Commission. He is also a member of UCL’s Grand Challenges on Transformative Technologies working group, an Editor of the British Journal of Educational Technology, and an Associate Editor of the International Journal of Child–Computer Interaction.

Dr. Ho Shen Yong is the Executive Director of the Institute for Pedagogical Innovation, Research, and Excellence (InsPIRE) at Nanyang Technological University (NTU). After graduating from Imperial College, he taught at Hwa Chong Junior College (1998–2003). Dr. Ho then completed his PhD at the University of Toronto in 2009 and conducted postdoctoral research at the University of Illinois at Urbana-Champaign and at the National University of Singapore (NUS). He joined NTU in 2011 as a Physics lecturer. Passionate about education, he has developed activities to enhance students’ thinking skills and to bridge theories with real-world applications in the Freshman Engineering Physics course. In 2014, he helped launch the “Making and Tinkering” course, where students design and build prototypes of their choice. Dr. Ho has received many teaching awards, including the Nanyang Education Award (Gold) in 2018. He was promoted to Principal Lecturer that year and has served as Provost’s Chair in Physics since 2019. In recent years, he has also led faculty efforts to prepare for the impact of AI on teaching and learning at NTU.

Afternoon - Parallel Talks/ Workshop 1

Abstract

The vision of Project NALA (NTU AI Learning Assistants) extends beyond simply using AI for learning (“Learning with AI”). It is equally about creating opportunities to develop AI as a form of learning itself (“Learning about and beyond AI”).

We aim to collaborate with faculty and students to position NALA as a platform for developing innovative AI solutions for teaching and learning. One example is a learning analytics hackathon challenge organised by InsPIRE from July 2025 to October 2025. The objective is to work with students to design and build solutions that mitigate cognitive offloading, support students in tracking their learning progress, and help them set meaningful learning goals.

At the same time, we provide students with opportunities to develop employable skills and showcase their capabilities. In this panel session, we will share how we collaborated with a student innovation club and Imperial College London to organise the challenge, and invite student teams to reflect on their experiences of learning about and beyond AI. We will also outline the next steps — including how we plan to extend collaboration with students for the benefit of the wider university community.

Biography

The Analytics and AI for Teaching and Learning team in InsPIRE conducts research and development on the use of analytics and AI for teaching and learning. Established in Jan 2023 and lead by Dr Lim Fun Siong, the team has developed applications such as NALA and EARLI that are used across the university. They also work with students and faculty to create opportunities for students to develop analytics and AI solutions. Finally, they have published in major conferences and journals including LAK, AIED, Journal of Learning Analytics, and Computers and Education.

Workshop Abstract

This workshop presents the first functional prototype of an Agentic AI platform designed to transform student support in higher education. Building on earlier work in predictive analytics for early academic risk detection and AI-enabled career preparation, we advance toward an agentic learning intelligence framework that unifies academic, co-curricular, and career data in a student-centered system. The current prototype integrates two foundational data hubs: (i) the Curriculum Hub, containing course offerings and graduation requirements, and (ii) the Student Academic Hub, capturing academic profiles and performance histories. Students can set their own learning objectives (e.g., personal interests, career aspirations) and preferred frequency of guidance, enabling the system to generate personalized, goal-driven recommendations. Contextual suggestions are dynamically curated by specialized AI agents, aligning academic progression, skill development, and career readiness. The workshop will demonstrate the prototype, reflect on its implications for student agency and institutional support, and outline future directions, including expansion to career and co-curricular hubs. Our goal is to share insights on how agentic AI can shift student support from a retrospective, institution-centric model toward a proactive, holistic, and student-driven learning ecosystem.

Biography

Abstract

The launch of ChatGPT in November 2022 created much excitement in diverse fields. This development also challenges our long-held assumptions about assessment, academic integrity, and the very purpose of schooling. Some folks claim that a revolution is happening and perhaps teachers might no longer be relevant. Yet more recent reports claim that ChatGPT might be causing harm to our students. We believe that as AI becomes more powerful, human skills will become more important. In this talk, we will describe how at NUS, we have developed and deployed LLM-powered role playing chatbots in diverse disciplines such as social work, law and nursing to train human skills. We will also share some learning points from our experiences and our views about the longer term impact of AI on higher education in Singapore.

Biography

Ben Leong is currently an Associate Professor of Computer Science at the National University of Singapore (NUS). Over the years, he has been recognized for excellent teaching with a number of teaching awards including the NUS Outstanding Educator Award in 2015. Dr Leong is currently Director of the AI Centre for Educational Technologies (AICET), where his team applies AI to build software platforms for education. In 2020, Dr Leong was appointed Chief Data Officer of AI.SG, the national AI Programme and from Jan 2021 to Dec 2023, he also served as Director of the Centre for Computing for Social Good & Philanthropy (CCSGP) at School of Computing (SoC).

Abstract

This case study highlights how SUSS, an autonomous university with a large population of adult learners, is harnessing AI to reimagine lifelong education. Adult learners often juggle work, family, and study, creating demand for flexible and personalised support. To address this, SUSS developed iSmartGuide, an AI-powered platform built on faculty-developed course content and trusted open resources. Using generative AI and retrieval-augmented generation (RAG), iSmartGuide serves as an on-demand companion with features such as an AI Tutor, AI adaptive quizzes, multilingual support with translation and summarisation, accessibility tools, and gamification through leaderboards and badges. Learners can access all iSmartGuides across the undergraduate programme and continue using them after graduation, supporting lifelong learning. Since launch, iSmartGuide has reached 20,000 users, supported over 27,000 AI Tutor enquiries, and delivered more than 11,000 AI Quiz questions. It also equips educators with analytics for personalised teaching, showing how ethical AI can transform higher education.

Biography

Associate Professor Lee Wee Leong is Vice President (Learning Services) at the Singapore University of Social Sciences (SUSS), where he leads the university’s educational technology, learning innovation, and support services. He also chaired SUSS’s Generative AI Committee, steering its institutional strategies for applying generative AI in teaching and learning. Previously, he served as Director of Educational Technology & Production and Director of Online Learning at SUSS. Before that, he was Associate Professor (Education) at Singapore Management University (SMU), where he also served as Director of the Master of IT in Business (Analytics) programme.

Abstract

Over the past four decades, Knowledge Building has distinguished itself from other educational approaches by transforming education from a model of knowledge transmission to one that engages students directly in the means by which knowledge advances in the world. As artificial intelligence continues to advance, fundamental questions arise about students' epistemic agency and the implications of AI for Knowledge Building. This study examined the intersection of AI and Knowledge Building through a collaborative dialogue with 23 contributors from diverse disciplinary backgrounds and career stages. Using a dialogical knowledge-making framework inspired by postdigital dialogue methodology, authors engaged in a sustained dialogue on Knowledge Forum to examine theoretical, pedagogical, design, ethical implications and tensions of AI in Knowledge Building. Findings show that Knowledge Building becomes more critical in the AI era by foregrounding epistemic agency and authentic knowledge creation. Three distinct AI conceptions emerged: AI as external tool, as extension of self, and as epistemic infrastructure. A typological framework also emerged, identifying six AI role configurations—tutors/experts, companions/partners, group members/simulators, formative assessment, interactive analytics, and conceptual playgrounds—mapped along interface type and epistemic authority levels. The study highlights the need to navigate productive tensions between AI assistance and human agency, psychological safety and generative social friction, and efficiency versus transformation. We propose three design principles that leverage AI to augment human epistemic agency in knowledge creating communities: making AI's epistemic position visible, enabling learner-driven navigation across AI roles, and creating opportunities for metadiscourse.

Biography

Dr. Lydia Cao is an Assistant Professor at the Ontario Institute for Studies in Education (OISE), University of Toronto. She is an interdisciplinary scholar whose research and teaching explore artificial intelligence in education, immersive technologies (XR), knowledge building, and sustainability education. Dr. Cao received her PhD in Education from the University of Cambridge and has held postdoctoral fellowships at both the Harvard Graduate School of Education and OISE. She currently teaches Introduction to AI in Education at the University of Toronto.

Abstract

In the era of AI reshaping all industries, education must proactively adapt to the future. Responding to this need, "TeachPod" stands as an AI-powered teaching platform deeply integrated with cutting-edge technologies. Its core philosophy is to empower, not replace, educators, collaborating with them to forge a new educational paradigm. It systematically addresses core teaching challenges—such as outdated content, inadequate classroom interaction, and a dearth of personalized guidance—by serving as a "super assistant" to teachers, fundamentally reengineering the teaching workflow.

The platform converts cutting-edge industry developments into structured lecture notes and case studies, significantly enhancing preparation efficiency. By creating interactive scenarios with multiple AI agents, it fosters classroom dialogue and critical thinking, reorienting education from knowledge delivery to skill development. Simultaneously, through systematic analysis of learning data, it provides educators with precise insights into student progress, enabling truly personalized instruction and efficacious formative assessment. The future of education resides in deep collaboration between educators and technology. TeachPod’s mission is to liberate teachers from repetitive tasks, enabling them to focus on their irreplaceable core role: inspiring intellect through mentorship and shaping the pillars of tomorrow’s world via innovative teaching.

Biography

Ying Zhang is a Professor of Marketing and Behavioral Sciences and Vice Dean at the Guanghua School of Management, Peking University. He is also the academic director of Peking University Center in Chicago and Deputy Secretary-General of DI-IDEA.

Ying Zhang studies consumer psychology and decision making, and is an expert on Chinese consumer market, particularly digital-driven market growth. He holds a Ph.D. in Behavioral Science and Marketing from the University of Chicago and BA from Nanjing University. His research has been published extensively in top international marketing and psychology journals and is a MSI young scholar. He is now editor-in-chief of Journal of Marketing Science.

Abstract

Automation in education has historically emphasised administrative applications, distinct from pedagogical judgements such as feedback or content generation. However, recent developments in AI have accelerated the automation of pedagogical judgements, especially using Large Language Models (LLM). When organisational inertia causes 'lock-in' of software platforms, the consequences now go beyond administrative efficiency, and amount to 'pedagogy lock-in'. This phenomenon represents two serious risks in education: stifling innovation, and raising ethical questions of agency in education. These issues are at the eco-system level and require reconsideration of the software architecture that we use in education. To facilitate rapid and sustainable innovation, high quality services, and agency with teachers and educational institutions - as opposed to software vendors - we propose a microservice architecture for pedagogical judgements. We define this architecture in the context of systems and software engineering literature applied to education, and provide a blueprint for a 'high-n' multi-platform, multi-microservice ecosystem. We present an exemplar microservice architecture for automated feedback, where students have received automated, formative feedback from microservices external to the main learning management system (LMS). We show the feasibility of the architecture, delivering feedback 1 million times per year and with microservices across a range of specialist disciplines and managed by a diversity of different organisations. We discuss the benefits and risks of this approach, and identify next steps for us as a community to develop a microservice ecosystem for sustainable innovation with AI in education.

Biography

Peter B. Johnson is a Principal Lecturer at Imperial College London. Peter began his career as a researcher in fluid mechanics, earning a PhD at UCL applied to ocean renewable energy, and working for Schlumberger as a field engineer and then a senior scientist. Peter changed his career to become a professional educator in 2018 when he joined Imperial College London. At Imperial Peter delivers undergraduate teaching in Fluid Mechanics and Professional Engineering Skills, has earned an M.Ed. degree, and leads in education innovation and management. Peter is the Director of Lambda Feedback, an initiative founded at Imperial. Lambda Feedback is a web-based study platform that provides automated formative feedback using a unique microservice architecture for the generation of feedback. Lambda Feedback is deployed widely across Imperial College London, and increasingly in other institutions, generating formative feedback millions of times per year.

Abstract

Generic AI feedback often fails to capture the immediacy, phrasing, and personal tone that students recognise from class. This talk presents an OCR–LLM personal feedback system designed for large-enrolment Computational Thinking courses. Student quiz scripts are scanned, digitised, and matched against a databank of instructor-crafted comments grounded in classroom experience. Draft feedback is generated in the instructor’s own style, then reviewed and finalised through a human-in-the-loop process. The system delivers timely, personalised, and context-rich feedback within days instead of weeks, helping students act on guidance while their reasoning is still fresh. Results from pilot runs show that students perceive the AI-generated personal feedback as authentic and useful, bridging the gap between scalable automation and the relational qualities of classroom teaching.

Biography

Dr Mohamed Arif Mohamed is a Lecturer at the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. He teaches courses in Data Science, Artificial Intelligence, Aerodynamics, and Computational Thinking, and has supervised a wide range of student projects in computational fluid dynamics, machine learning, and educational technology. His teaching and research interests lie at the intersection of aerodynamics, turbulence and learning innovation.

Abstract

On December 6, 2022, Stephen Marche published an op-ed in The Atlantic titled “The College Essay is Dead”, arguing that essay-writing, long regarded as the bedrock of humanistic pedagogy, faces a seismic paradigm shift with the emergence of Generative Artificial Intelligence (GenAI). As universities grapple with fragmented policies, unreliable detection tools, and divided opinions over what constitutes academic dishonesty beyond plagiarism, the crux of the problem is clearly not just technological, but pedagogical. This problem is especially critical for humanities and language departments, where essay-writing remains the predominant mode of assessment for language acquisition.

Amidst the impasse between innovation and regulation, language instructors are forced to confront a difficult but necessary question: Is essay-writing still useful or relevant as a skill in the university classroom? Scholarship has long relied on writing as a central avenue to critical thinking, serving as both assessment and learning. With the growing influence of GenAI tools, a reconfiguration of these traditional beliefs and assumptions seems inevitable, as measures to integrate these tools without compromising learning continue to develop. This paper seeks to examine these concerns from the perspective of a language instructor who still currently teaches essay-writing at the university level, reflecting on the pedagogical challenges of teaching essay-writing in this new era.

Biography

Dr. Ho Jia Xuan is a lecturer at the Language and Communication Centre (LCC), NTU. He obtained his PhD in English from NTU in 2021, where he read the works of Dermot Healy, J. M. Coetzee, and Julian Barnes through the lens of time and narrative. His current research interests have expanded to the realm of education technology, specifically in the areas of digital pedagogy and the role of GenAI tools in learning. Building on his long-standing fascination with temporality, he is investigating the role of time-based edits in writing behaviour, using a keystroke logging tool he developed while working as an EdTech consultant. As a side quest, he is also exploring interdisciplinary algorithms in an attempt to uncover potential new insights in the field of writing cognition.

Afternoon - Parallel Talks/ Workshop 2

Abstract

Generative Artificial Intelligence (GenAI) is rapidly reshaping graduate education. While the Technology Acceptance Model (TAM) has long emphasized usefulness and ease of use as key drivers of technology adoption, much less is understood about how social influence and disciplinary culture guide students’ engagement with GenAI,especially in its early diffusion stage. This research extends TAM by introducing social and contextual dimensions to explain how peer norms and academic environments shape adoption among master’s and doctoral students across research-intensive universities in China. Findings reveal that social influence acts as a critical accelerator, legitimizing the use of GenAI and amplifying perceptions of usefulness within academic communities. Disciplinary variations further highlight that adoption is mediated by distinct epistemic traditions and professional norms. By moving beyond usability, this research shows that GenAI adoption in higher education is not only a cognitive decision but also a socially embedded process, shaped by interaction, legitimacy, and the evolving culture of learning in the age of intelligent technology.

Biography

Jie Lin is a Ph.D. in Higher Education from Shanghai Jiao Tong University. Her research focuses on AI adoption, higher education policy, and disciplinary evaluation. She has led multiple funded research projects and published in both Chinese and international journals. As a Visiting Scholar at the University of Pennsylvania, she collaborated with global experts on educational governance and digital transformation. Lin’s current work examines how graduate students engage with emerging AI technologies, contributing to the global understanding of equitable and human-centered AI integration in higher education.

Abstract

As Generative AI's capabilities grow, its power to solve complex problems undermines the traditional role of our curriculum and assignments in developing student thinking. This is compounded by widespread AI adoption among university students, presenting an immediate challenge: how to protect and cultivate students’ independent critical thinking. Therefore, we developed "PKU Quest," Peking University's AI-assisted learning platform. We began by using RAG to ensure factual accuracy, and are evolving our AI applications toward heuristic, cognitively forced interactions. This shifts the evaluation of AI+Education tools: from optimizing for problem-solving ability to optimizing for tutoring ability. Our "Math Tutor" tool exemplifies AI applications built through human-computer collaboration, where human experts provide the ground-truth solutions, and the AI acts as a tutor. It references this correct answer to provide heuristic guidance, using a Socratic method to force students to solve the problem independently, step-by-step. We argue that future applications must be judged by their embedded pedagogical strategies, not just solution accuracy, to offer a scalable model for Socratic education.

Biography

Leheng Chen is a Ph.D. student at the Beijing International Center for Mathematical Research (BICMR), Peking University, advised by Professor Bin Dong. He has broad interests in the application of artificial intelligence. Previously, he explored research directions in AI for Science, such as thermodynamic modeling and foundation models for partial differential equations, with his work published in Physical Review E and at an ICLR Workshop. He has since shifted his research focus to the practical application of AI in Education, where he designed and developed "PKU Quest," an AI-assisted teaching and learning platform for Peking University.

Abstract

With recent advances in generative AI and machine learning, it has become increasingly important to articulate core principles of how humans learn. Educational principles have been shaped by philosophers, psychologists, and neuroscientists over centuries. This talk explores deriving a teaching approach/pedagogy based on these established learning principles and examines how generative AI can best support learning when grounded in sound pedagogical frameworks

Biography

Passionate about education, he has developed activities to enhance students’ thinking skills and to bridge theories with real-world applications in the Freshman Engineering Physics course. In 2014, he helped launch the “Making and Tinkering” course, where students design and build prototypes of their choice. Dr. Ho has received many teaching awards, including the Nanyang Education Award (Gold) in 2018. He was promoted to Principal Lecturer that year and has served as Provost’s Chair in Physics since 2019. In recent years, he has also led faculty efforts to prepare for the impact of AI on teaching and learning at NTU.

Workshop Abstract

Learn how to put NVIDIA Inference Microservices (NIMs) to work in real-world scenarios. We’ll start with the basics, what a NIM is and when to use one, before walking through build.nvidia.com to explore and deploy them. You’ll call a NIM via its REST API, run the same model locally on your own GPU for low-latency inference, and explore fine-tuning with LoRA adapters.

Pre-requisite knowledge:

Basic familiarity with Python programming
Basic understanding of Docker containers
Basic familiarity with using AI models via API calls

Equipment to bring:

Laptop and charger if needed

Abstract

The rise of generative AI and large language models such as ChatGPT invites a reimagination of learning—not only learning about AI, but learning with and beyond it. This talk explores the promise of teachable AI agents—pedagogical partners that support learning through the act of teaching. Rooted in Jean-Pol Martin’s “learning by teaching” approach, these agents can act as novice students, peer collaborators, or motivational companions, fostering self-directed and reflective learning. I present findings from a mixed reality fire safety study where university students taught and protected a virtual child agent. Results show that combining structured instruction with a socially meaningful teaching task enhances learning reflection, motivation, and response efficacy. These insights suggest that AI-powered agents can enrich not only content delivery but also the emotional and social dimensions of learning—empowering students to think critically, act responsibly, and teach collaboratively in increasingly AI-augmented educational environments.

Biography

Dr. Younbo Jung is an Associate Professor at the Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore. He holds a B.A. and M.A. in Telecommunication from Michigan State University and a Ph.D. from the Annenberg School for Communication and Journalism at the University of Southern California. His research focuses on the social and psychological implications of human communication, particularly in areas such as generative AI, serious games, virtual reality, human–computer interaction, and computer-mediated communication. A passionate educator, Dr. Jung has received five teaching-related awards at NTU, including the Nanyang Excellence in Teaching Award (2010), Nanyang Education Award (College, 2013), Nanyang Education Award (University – Bronze, 2014), Koh Boon Hwee Scholar’s Award (2014), and Graduate Mentorship Award (2024). He has served as an inaugural Fellow of the Teaching Excellence Academy since 2014. His work bridges emerging technologies and communication science through interdisciplinary research and engaged pedagogy.

Abstract

Sitting at the intersection of STEMB higher education and creative technology, Imperial College London’s Digital Media Lab explores innovative approaches to simulation, visualisation, and technology-enhanced learning.

Through shared examples, reflections, and lessons learned, this session will trace the team’s collaborative journey—rethinking XR and exploring ways to further extend reality, offering students and staff a broader transmedia toolbox.

In a domain often driven by modality or solution-first thinking, the case will be made for spending more time on the questions themselves, and for deeper engagement with the real opportunities and barriers. As Gen-AI continues to reshape the field of interactive learning media, the session will highlight the value of ‘being less bot’ in our praxis—resisting knee-jerk adoption of generic offerings for students, whether or not bots are part of the workflow.

Biography

Daniel Mitelpunkt is a Founder and Director of Imperial College London’s Digital Media Lab (DML), established to develop interactive and time-based solutions and media for learning and research.

The lab’s work ranges from spatial computing in traditional lecture theatres to VR digital twins of teaching labs, positioning DML as the university’s interdisciplinary hub for applied media.
DML is recognised for its close alignment with evolving needs and opportunities, and for its collaborative approach—working with leading academic and professional staff across the institution in both education and research. The team brings together expertise from gaming, broadcast, and XR, creating a dynamic environment for innovation. An inspirational external advisory board further enriches the lab’s work.
Beyond Imperial, Daniel is an active advocate for Creative Technology within Higher Education, championing stronger ties between academia and the creative industries and giving this emerging field a stronger voice.

Abstract

This keynote will present how artificial intelligence (AI) agents can transform case-based learning (CBL), a widely used pedagogy for developing higher-order thinking and professional skills. Drawing on a mixed-methods study with graduate students, the research compares traditional CBL with an AI-enhanced approach powered by a large language model. Findings show that the AI agent enriched case scenarios, assumed diverse discussion roles, and provided adaptive feedback, leading to stronger performance, engagement, and interaction. The presentation will discuss both the opportunities and challenges of integrating AI into CBL, and its implications for advancing innovative pedagogy in higher education.

Biography

Heng Luo received his Ph.D. in Instructional Design, Development, and Evaluation from Syracuse University in 2015 and later worked as a Research Associate at the John A. Dutton e-Education Institute, Pennsylvania State University. He is currently Professor and Vice Dean of the Faculty of Artificial Intelligence in Education at Central China Normal University. His research focuses on integrating learning sciences, instructional design, and AI technologies to enhance educational quality and equity across diverse contexts. He has published more than 60 articles in SCI/SSCI-indexed journals, and his scholarly contributions have been recognized with awards including the TICL Outstanding International Research Collaboration Award (AERA 2022) and the Outstanding Practice Award (AECT 2019).

DAY 2

Morning

Biography

Dr. Ng Aik Beng is the senior regional manager of NVIDIA AI Technology Centre (NVAITC). Additionally, he serves as an Associate Professor (Adjunct) at Singapore Institute of Technology (SIT), Co-Director of SIT-NVIDIA AI Centre and a member on the Singapore National AI Technical Committee. With over 20 years experience, prior to NVIDIA, he has worked across SMEs, MNCs and public agencies, in roles spanning technology innovation, IT management, and industry development. Together with his research team and collaborators, he remains committed to drive the ecosystem on AI-powered transformations and promote AI within the ecosystem through strategic collaborations

Abstract

This talk explores how Agentic AI including curious, persuasive, and affective agents can catalyze active, experiential learning. By weaving together autonomy, emotion, and reflection, these intelligent agents cultivate adaptive and empathetic learning ecosystems that co-evolve with learners, transforming learning into a shared journey of discovery and growth.

Biography

Dr Miao Chunyan is a President's Chair Professor in Computer Science and Associate Vice President (Capability Building) at Nanyang Technological University. She is a Fellow of Singapore Computer Society (SCS), the Institution of Engineers Singapore (IES), and Academy of Engineering Singapore (SAEng). A leading expert in Humanized AI (HAI), Dr Miao has published extensively and received numerous awards (over 40 research best paper and top AI awards) for her impactful research. She directs multiple research centers that focus on AI for ageing (LILY), Fintech with WeBank, and e-sustainability with Alibaba (ANGEL). Her work combines human and artificial intelligence for real-world applications in health, education, and industry. Recent honors include the 2023 President's National Day Award - Public Administration Medal (Silver) and recognition by the Singapore Computer Society as "Professional of the Year 2020."

Keynote Abstract

This talk examines the evolving landscape of teacher-AI teaming in education through the lenses of replacement, amplification, and augmentation of human competence. Drawing on recent empirical and conceptual work, it argues that while AI promises efficiency gains, its indiscriminate use risks competence atrophy, dehumanisation, and erosion of teacher agency. By contrast, carefully designed AI systems can amplify teacher capacities through complementarity by supporting perception, interpretation, and decision-making while safeguarding professional judgment. The most ambitious but challenging frontier is augmentation, where synergistic human–AI interaction yields emergent competence beyond the best of what either can achieve alone. The talk proposes a five-step teacher-AI interaction categorisation model to make sense of the current complex land scape of teachers’ AI use in education. By highlighting ethical, pedagogical, and socio-technical implications, the talk is concluded by calling for evidence-informed strategies that resist automation-first logics and instead prioritise human-centred, agency-preserving designs of AI in Education.

Biography

Panel Discussion 2

This interdisciplinary symposium will share varied theoretical and empirical lenses describing how NIE researchers are studying emotions in children and adolescents, and the implications of these lenses for technology design, in particular, AI technology design at all educational levels. For instance, what particular kinds of teacher-child conversations affect socio-emotional development? How do children’s emotional understanding relate to their academic achievement? What are the learning mechanisms by which negative emotions like frustration impact executive functioning in children? What epistemic emotions do children frequently show and how do these relate to their knowledge building efforts and learning activities within informal learning environments? Can we deliberately design to improve emotional resilience in children as preparation for future learning? How may we synergise our technology design efforts to meaningfully and responsibly integrate research on emotional influences in learning?

Afternoon - Parallel Talks/Workshop 3

Workshop Abstract

SynthDa is a framework designed to make synthetic data generation for human actions more usable and accessible. It focuses on pose-level augmentation, generating synthetic training videos by interpolating between real and AI-generated poses. This helps improve minority-class coverage, mitigating data scarcity for rare and safety-critical actions.

In this workshop, participants will get hands-on experience running SynthDa pipelines, learn how to generate synthetic datasets for action recognition, and explore how synthetic augmentation can strengthen downstream performance in AI applications. Attendees will leave with practical skills for integrating synthetic data into their own research or projects.

Prerequisite Knowledge

Basic Python programming (writing and running scripts, working with packages)
Foundations of deep learning/machine learning (concepts like training datasets, augmentation, neural networks)
Familiarity with action recognition/pose estimation concepts is useful but not required.

Equipment to Bring

Laptop with WiFi (Ubuntu or Windows recommended)
(Optional) Ability to set up a hotspot in case venue internet bandwidth is limited

Abstract

This study evaluates the use of AI-mediated extended reality (AI-XR) role-play simulations as a tool to strengthen parent–teacher communicative competence among pre-service teachers. Parent–teacher conferences often involve complex emotional challenges, yet teacher education programmes provide few opportunities for authentic rehearsal. Traditional peer-to-peer or scripted virtual simulations can build confidence but are constrained by limited scenarios and human resource demands. AI-XR technologies address these limitations by generating adaptive “parent” avatars capable of expressing diverse affective states—such as defensiveness, anxiety, or disengagement—and by delivering real-time, personalised feedback. In this study, pre-service teachers engaged in a classroom-based AI-XR role-play session, after which their feedback was collected to assess both usability and perceived pedagogical value. The findings provide insights into how students interact with AI-driven avatars, the kinds of communicative strategies they practice, and the potential of AI-XR to enrich teacher preparation in line with Singapore’s goals for family engagement and Smart Nation innovation.

Biography

Dewi Ayu Kencana Ungu is a Research Fellow at Nanyang Technological University, Singapore. Her research examines the educational affordances of immersive technologies—VR, AR, and AI-XR—across STEM and SHAPE disciplines, with a focus on how they enhance learning, communication, and engagement compared to traditional media. She earned her PhD in Education from Curtin University, Australia, where she specialised in immersive learning environments. Prior to joining NTU, she spent six years as Simulation Director at Labster, Bali and Copenhagen, leading the design and implementation of virtual laboratories for global adoption in higher education.

Abstract

How do we perceive and judge artificially created faces? We first tested participants’ judgment of robot/avatar faces. We found the uncanny valley effect (Mori 1970, feeling of discomfort generated by near-human appearances of artificial entities) occurred when faces presented as brief as 50 ms, suggesting perceptual level processing (Yam et al., 2024). We then tested participants on real and deepfake faces generated by various deepfake algorithms, varying display durations (17-1000 ms), with eye movement recordings. We found that Stable-Diffusion (SD)-generated and real faces were accurately identified (accuracy > 84%) even at the shortest duration, while Style Generative Adversarial Network (StyleGAN) generated and face-swapped images were most difficult to detect (accuracy < 31%). Eye movement analysis showed that face-swapped images elicited fewer-but-prolonged fixations with nose-focused patterns—suggesting more holistic processing strategy used. Our findings provide insights into human perception, attention, and judgment of artificially created faces in the era of AI.

Biography

Associate Professor Xu Hong is a faculty member in Psychology, School of Social Sciences at NTU. Before joining NTU, she was a postdoctoral fellow at the Department of Neuroscience at Columbia University. She obtained her Ph.D. in Psychology and Master’s in Statistics from University of Chicago. Her research focuses on neural mechanisms of Visual Perception and its application to Human-Computer Interaction (HCI). She studies face perception and heading (self-motion) perception, signage design, wayfinding, and environmental psychology by field study and virtual reality simulations. Her recent work on face perception is on human faces, robotic and avatar faces, and deepfake faces, extending to fake news detection with LLM and deep learning algorithms. Her research on HCI investigates our trust toward mobile banking, autonomous vehicles, AI Medicine, and sustainable systems. She founded the Visual Cognitive Neuroscience (VCN) Lab at NTU. Further information about her lab can be accessed from VCN LAB (vcn-lab.github.io).

Abstract

This talk will present cases of AI-powered second language learning in Singapore classrooms, with a particular focus on young learners. It will underscore the importance of multimodal feedback enabled by AI and explore its pedagogical implications. It will further examine the theoretical foundations and practical considerations behind a shift from AI-driven feedback toward Learner-AI Shared Agency within immersive learning environments. The talk invites a rethinking of AI’s potential in second language education.

Biography

Wen Yun is an Assistant Professor in the Learning Sciences and Assessment Department at the National Institute of Education (NIE). Her research investigates how people learn through interaction and dialogue in multimodal environments, and how emerging technologies, such as Augmented Reality (AR) and Artificial Intelligence (AI), can foster high-quality interactions to promote teaching and learning. She pioneered the integration of computer-supported collaborative learning into Chinese as a Second Language learning, driving pedagogical innovations in the field. A key contribution of her work is the sustainable application of AR into subject teaching for young learners. The learning systems designed and developed by her team are consistently used by over 60 teachers in their classrooms, engaging approximately 2,000 students across 14 primary schools in Singapore

Workshop Abstract

With autonomous vehicles reducing the need to drive, a similar question arises in education: will learners one day study without human educators as AI becomes more pervasive? Artificial intelligence has already transformed learning by enabling personalization and real-time feedback, yet it also raises critical concerns about the educator–learner relationship, ethical implications, and the relevance of human roles in teaching.

This workshop will draw on scenarios from Health Professions Education to explore how educators can adapt to remain vital in AI-enabled learning environments. Using evidence-based frameworks, it emphasizes the augmentation rather than replacement of human teachers. In this stimulating workshop and discussion, participants will be able to identify issues surrounding AI in higher education, understand guiding principles, define evolving educator roles, and develop actionable, student-centered strategies.

Biography

A/Prof Chow Minyang is a Consultant at Tan Tock Seng Hospital, Assistant Professor at the Lee Kong Chian School of Medicine, and Consultant at the Ministry of Health’s Professional Training and Assessment Standards Division. A clinical educator and internist, he integrates artificial intelligence, machine learning, and learning analytics to improve medical education and healthcare outcomes. He serves as Associate Program Director of the Singapore Chief Residency Programme at the Healthcare Leadership College and the NHG Internal Medicine Residency Programme, as well as working closely with TTSH’s Pre-Professional Education Office and NHG Residency to advance technology-enabled training. Dr. Chow has received multiple awards for teaching and mentorship, including the NUS Medicine Special Recognition Award and the Dean’s Award for Teaching Excellence. He holds a Master of Science in Health Professions Education from the MGH Institute, where he is currently pursuing a Ph.D.

A/Prof Wong Teck Yee is a Family Physician and Senior Consultant at Tan Tock Seng Hospital, and Associate Professor and Lead for Family Medicine at the Lee Kong Chian School of Medicine, NTU. His career spans leadership in clinical training, undergraduate and postgraduate education, and national-level assessment development. At LKCMedicine, he has directed key modules and served as Assistant Dean (Year 4). Within the National Healthcare Group, he was Education Director of the Pre-Professional Education Office and Academic Director of NHG College, advancing faculty development and training. Nationally, Dr. Wong co-chairs the Medical Review Committee and contributes to the Joint Committee on Family Medicine Singapore. His research and teaching in Family Medicine and Health Professions Education have earned recognition, reflecting his impact on medical education and healthcare training.

Ms. Theophilia Yong is a leading innovator in technology-enabled health professions education and currently serves as Assistant Director of the Digital Learning Team at the Lee Kong Chian School of Medicine. She spearheads initiatives that harness AI, gamification and immersive technologies to enhance medical and healthcare training. With an established background in instructional design and learning simulation, she has advanced projects that bridge pedagogy and technology, equipping future health professionals with impactful, practice-ready learning experiences. Her leadership has helped position LKCMedicine as a testbed for scalable, next-generation digital education models.

Abstract

Generative AI and VR are opening new possibilities for how we teach, train, and prepare people for the future of work. This talk focuses on how these technologies can be applied to education and industrial training, from virtual prototyping and immersive tours to understand a training effetiveness.

By combining immersive experiences with physiological and behavioral data, like heart rate variability, gaze tracking, and behaviour patterns, we can better understand how learners and trainees engage, how much cognitive effort they are spending, and how they react to unexpected situations. This evidence-based approach not only makes training and education more effective, but also connects to sustainable practices, aligned with SDG 12 which stands for Responsible Consumption and Production.

Biography

Dr. Ingrid Winkler's research explores the potential of immersive technologies to drive a more sustainable and human-centric Industry 5.0. She holds a DT-1D Research Productivity Fellowship, a grant-based distinction awarded by Brazil’s National Council for Scientific and Technological Development (CNPq). She is also the Vice-Chair of the Special Committee on VR at the Brazilian Computing Society (SBC). At SENAI CIMATEC University, Dr. Winkler is an Associate Professor in the Computational Modeling and Industrial Technology Management Department. She has held international research positions as a Visiting Researcher at Nanyang Technological University (Singapore) and is affiliated with Lusíada University (Portugal) and the EMBRAPII Excellence Center in Immersive Technologies (AKCIT/UFG). As the Head of the Institute for Science, Innovation, and Technology in Industry 4.0 (INCITE INDUSTRIA 4.0), Dr. Winkler leads a scientific network dedicated to advancing research and innovation in Industry 4.0 and immersive technologies. Dr. Winkler has coordinated 44 R&D&I projects in collaboration with industry partners, including EMBRAER, FORD, Great Wall Motors, NVIDIA, Petrobras, SHELL, HP, Fiocruz, and Continental Mabor Portugal.

Abstract

True understanding begins where teaching reaches its limit. Beyond explanation lies experience, the space where knowledge is tested, shaped, and made one’s own. When learners engage with realistic scenarios, face uncertainty, and reflect on outcomes, learning transforms from reception to construction. Simulations, teamwork, and industry-based case studies open this territory of exploration, where practice gives depth to theory. Artificial intelligence strengthens this approach by amplifying, refining, and accelerating learning methods. It helps create adaptive contexts that both guide and challenge learners, generating environments where feedback is immediate and experimentation continuous. In such settings, education moves again, it becomes a living process where curiosity, initiative, and mastery evolve together, preparing professionals to act with confidence and discernment.

Biography

Etienne Sellan is a cybersecurity expert with a strong background in digital and e-commerce environments, combining hands-on field experience with a deep interest in learning through concrete practice. He is an active contributor to the cybersecurity community through technical events and knowledge-sharing initiatives. Passionate about education, Etienne develops immersive and practice-oriented learning approaches that bridge conceptual understanding with real-world application. His work centres on designing learning experiences where experimentation, collaboration, and reflection transform knowledge into expertise—helping learners build both confidence and sound judgement when navigating complex challenges.

Abstract

The rapid convergence of AI, digital twin, and mixed reality (MR) technologies presents new opportunities to reimagine learning and training for complex operational domains. This talk explores how tangible digital twins—combining real-time simulation, shared visualization, and intuitive gesture-based interaction—can enhance collaborative decision-making, situational awareness, and cognitive engagement. Drawing on applied research in air traffic management, we demonstrate how AI-augmented MR interfaces using holographic overlays on 3D printed models enable experiential and team-based learning, bridging physical and digital worlds.

Beyond aviation, this paradigm offers transformative potential for education and training: from immersive STEM learning environments to human–AI hybrid workspaces for developing critical thinking, problem solving, and collaboration skills. The talk will highlight implications for AI-enabled learning design, and pathways for cross-sector adoption in future smart learning ecosystems.

Biography

Sameer Alam is the Director of the Air Traffic Management Research Institute (ATMRI) and an Associate Professor at the School of Mechanical & Aerospace Engineering, Nanyang Technological University (NTU), Singapore. His research and teaching span AI-enabled air traffic management, digital towers, air traffic flow management, and human-AI hybrid systems. His research portfolio is supported by SESAR Joint Undertaking, Civil Aviation Authority of Singapore, Office of Space Technology and Industry, Saab AB, and other international partners. He holds a PhD in Artificial Intelligence from the University of New South Wales, Australia. Sameer’s research has been recognised through multiple best paper awards at leading conferences, the Australian Tall Poppy Science Awards, and the Australian National University Science Medal. He has authored over 200 peer-reviewed publications with more than 3,500 citations and serves on editorial boards of top journals, including IEEE Transactions on Artificial Intelligence, Transportation Research Part C, and the AIAA Journal of Air Transport. His research has been operationalised in surveillance, trajectory optimisation, and safety assessment systems worldwide.

Afternoon - Parallel Talks/Workshop 4

Abstract

The disruptive emergence of generative AI (GenAI) tools—including ChatGPT, Microsoft Copilot, and Google Gemini—has elicited both optimism and apprehension regarding pedagogical innovation within mathematics classrooms. In this presentation, I report on an exploratory study investigating whether, and in what ways, GenAI tools can generate personalized learning pathways for students and support teachers in Dutch mathematics classrooms. I also address persistent challenges in mathematics education—particularly the need for instruction that fosters student engagement—and consider how AI might assist and positively affect students’ performance and attitudes.

Biography

Dr S. M. Sharon Calor is a senior lecturer and researcher at the Teacher Academy and Research Institute of Child Development and Education (University of Amsterdam). She is also a senior lecturer and researcher at the Amsterdam University of Applied Sciences (HvA) and assistant professor at Vrije Universiteit Amsterdam (VU).

Her research interests are in the areas of small-group scaffolding, students’ learning, mathematical reasoning, mathematical level-raising, digital literacy, teachers’ professional development, and generative AI in education.

Abstract

Generative AI enables students to produce their assignments with less effort. Yet it also allows students to skipping the planning, research, discerning and writing stage. This presentation assumes that limiting students' use of Gen AI is futile. Instead, it will be more effective to sever the link between what students do in preparation, and what the actual assessment entails. This presentation will showcase team presentations as a performative assessment with additional conditions, such as limiting the number of words per slide, restricting the use of scripts, while allowing for images that serve as mental triggers. Students were required to internalize their presentation while images reduced the risk of forgetting their points. A total of 79 presentations were assessed. Students candidly shared that while these additional conditions were a source of anxiety and more effort was needed, the presentations were also less challenging than expected.

Biography

Dr. Ricky Chua is a Lecturer at Nanyang Technological University (NTU) of Singapore. He concurrently teaches classes with the Interdisciplinary Core Curriculum Office. He received his PhD in Materials Science from NTU and has been a career educator in classrooms and lectures for 15 years. He is passionate about prioritizing engagement in the classroom and is constantly experimenting with novel and creative tactics to inject drama and suspense, all with the intention of heightening engagement for the purposes of improving attendance, attention and retention of content.

Abstract

The adoption of generative AI across higher education is being presented to us as an inevitable, even beneficial, pedagogical development. However, this presentation argues that in art and design education, the costs may actually outweigh the gains.

Creative practice is not about shortcuts; rather, it is the very friction of struggling to find the right creative solution in which the value of art and design lies. Making is grounded in tacit knowledge and iterative failure, fostering an embodied understanding. Generative AI appears to threaten this by destabilising notions of authorship and providing a more convenient solution to creative problems. But does convenience make for great art and design? Concerns extend beyond authorship and convenience. Training and operating image generation models burns through vast amounts of energy, yet the environmental impact is rarely acknowledged. These models are also trained through the mass scraping of artists’ work, often without their consent, recognition, or compensation. These image models have also been shown to have significant racial and gender biases, and whilst they are purported to democratize creativity, most models offer only a limited amount of free credits, and full access lies behind a paywall. The result of all this is often a flood of AI slop: uncanny, homogenised images that serve only to flatten our visual culture.

This presentation argues that if we allow these algorithms to ‘connect the dots’ for our students, then this is not creative learning. It is creative surrender, and if we unquestioningly embrace Gen AI in art and design education, we risk producing passive consumers rather than innovative critical thinkers. Drawing on my MOE Tier 2 research project under the Design Ethics and Visual Integrity Lab (DEVIL) and my forthcoming Routledge book A Guide to Addressing Visual Plagiarism: Ethical Insights for Artists and Designers in an AI World, this presentation situates generative AI within a longer trajectory of debates on authorship, appropriation, and visual integrity. Rather than presenting Gen AI as a neutral tool, this research calls for art and design education to confront the ethical, professional, and pedagogical stakes of its use, emphasising visual integrity and critical authorship as non-negotiable values. Finally, this presentation proposes that to educate responsible designers, we must retain human intention and visual integrity at the core of our pedagogy and be prepared to say no when AI threatens these principles.

Biography

Lisa Winstanley is an Associate Professor and Associate Chair of Research at the School of Art, Design & Media at Nanyang Technological University in Singapore. With over 20 years of commercial design experience and a decade of work as a design educator and researcher, she has made significant contributions to the fields of visual literacy and visual communication. Her creative work is internationally recognised, earning over 80 international design awards, and her work is showcased across 30 countries, in venues such as the Oculus in New York, the State Tretyakov Gallery in Moscow and the Dongdaemun Design Plaza in South Korea.

Lisa is the founder of the Design Ethics and Visual Integrity Research Lab (DEVIL), where her scholarly research focuses on the intersections of ethical design practices and pedagogies, emphasising the development of visual literacy for interdisciplinary stakeholders. This research trajectory spans two main pillars: addressing visual plagiarism in art and design, for which she has been awarded an MOE AcRF Tier 2 grant and investigating interdisciplinary social responsibility through practice-led design. This dyad is united by a focus on fostering ethical practices in design, ensuring that visual integrity, ethical collaboration, and social responsibility are central to her approach to visual communication. Lisa also serves as an advisory and Editorial Board Member of the On the Image Research Network and is the current chair of the Global Undergraduate Awards Jury for the Visual Arts Category.

Abstract

The rise of Artificial Intelligence (AI) in healthcare requires medical professionals to develop well-defined competencies, yet existing frameworks remain too broad for medical education. In this talk, Prof Fan will present a tailored AI competency framework for medical professionals, integrating UNESCO’s AI guidelines with Miller’s pyramid of clinical competence and refined through feedback from clinicians, administrators, and educators. Building on this framework, he and his team designed and piloted a year-long postgraduate program in AI in Medicine at a Singapore medical school. The curriculum, guided by Kern’s six-step approach, features core courses on foundational AI, clinical applications, and governance and ethics, delivered through problem-based and hands-on learning. A trial version of the course has been run with encouraging feedback, underscoring its potential to prepare healthcare professionals to work responsibly with AI.

Biography

Dr. Fan Xiuyi is an Assistant Professor of Digital Health, jointly appointed at the Lee Kong Chian School of Medicine and the College of Computing and Data Science, Nanyang Technological University. He previously held positions at the University of Sydney, Imperial College London, and Swansea University. His research focuses on explainable AI (XAI), digital health, and medical AI education. At NTU, he leads interdisciplinary initiatives including the MSc in AI in Medicine programme and contributes to the Centre for AI in Medicine (C-AIM). His current projects develop interpretable, uncertainty-aware AI for triage, remote monitoring, and medical imaging in partnership with Singapore’s public healthcare system. Dr. Fan has published widely at leading AI venues and has received awards such as the Welsh Crucible Leadership Award and an IEEE Best Paper Award.

Abstract

Shanghai Jiao Tong University (SJTU) has established an integrated medical AI talent education model with "one body and two wings." Based on the course "Innovation and Practice of Clinical Intelligent Diagnosis and Treatment Technology" as the "body", it aims to guide students in developing interdisciplinary perspectives and scientific thinking that bridge medical-engineering, medical-science, and medical-humanities fields. By breaking down traditional disciplinary barriers and innovating teaching methods, a scientific, systematic, and efficient "AI+Education" curriculum system has been constructed, effectively addressing the gap between interdisciplinary theory and practical skills. The course has been recognized as a Shanghai Municipal First-Class Undergraduate Course, Shanghai Key Course and one of SJTU's first Digital Intelligence Courses. Furthermore, the model expands to include social practice and innovation-entrepreneurship as its "two wings." Leveraging the SJTU Medical-Engineering Interdisciplinary Youth Talent Training Base, it provides students with extracurricular medical AI practice opportunities, enabling them to conduct research and study at cutting-edge medical AI enterprises. Besides, by incorporating innovation-entrepreneurship methodologies, it guides students in transforming medical AI technological achievements and encourages participation in international innovation-entrepreneurship competitions to foster learning through competition. Ultimately, the program cultivates high-quality, highly capable, and comprehensively knowledgeable interdisciplinary medical innovation talents to meet the demands of future medical development.

Biography

Dr. Zhou Huifang is Party Secretary of Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Chief Physician of Ophthalmology, Yangtze Distinguished Professor of the Ministry of Education, Principal Investigator of National Key R&D Program of China and Council Member of the Asia-Pacific Ophthalmic Plastic and Reconstructive Surgery Society. She has long been engaged in AI for ophthalmic research and AI for medical education, and has led 15 research projects, published over 100 SCI papers, obtained 18 patents and achieved 5 technology transfers. She has been recognized among the "Top 100 Most Influential Ophthalmologists in Asia-Pacific", and received 9 scientific awards including the National Prize Second for Scientific and Technological Progress and the First Prize for Scientific and Technological Progress of Higher Institutions from the Ministry of Education. She was also honored with the Asia-Pacific Academy of Ophthalmology Achievement Award. Professor Zhou serves as the lead instructor for the clinical intelligent diagnosis and treatment technology innovation and practice course, and plays a key role in teaching for both ophthalmology and the national-level oculoplastics training programs. She has established three interdisciplinary medical-engineering youth talent development bases, benefiting over 1,000 undergraduate students and nearly 100 graduate students.

Dr Lei Chaoyu, M.D. in Shanghai Jiao Tong University School of Medicine, specialises in ophthalmology and digital medicine. He has been selected for the United Nations University Programme on Digital Technology and Sustainability for Sustainable Development and has published 13 peer-reviewed papers as first author, and was invited to speak at the UNU-IIGH/WHO Conference on Strengthening Global Health Governance. He has delivered 8 oral presentations at international conferences including the United Nations University AI Summit and the World Ophthalmology Congress. Dr. Lei was awarded Runner-up at the U21 Health Sciences Group 2024 Doctoral Symposium and received the Bronze Poster Award at the Nature Conferences - AI for Healthcare. He serves as a reviewer for over ten SCI journals including Nature Digital Medicine, and has received more than 20 national awards. He has long participated in the development of the clinical intelligent diagnosis and treatment technology innovation and practice course, and has published a first-author pedagogical paper on talent cultivation models in interdisciplinary ophthalmology and engineering.

DAY 3

Morning

Keynote Abstract

Since ChatGPT's release in 2022, AI has evolved from conversational tools to autonomous systems that reason, act, and learn independently. Reasoning models, agentic systems, and AI browsers that predict and act on users' behalf represent a fundamental shift – from AI as classroom presence to educational infrastructure.

Yet current AI systems optimise for engagement and efficiency – goals often antithetical to learning. The industry's response has been superficial: "study modes" and "wrapper tools" that modify behaviour without understanding how learning actually works. Drawing on neuroscience research and the concept of "cognitive debt," this talk reveals why these approaches fail to support the neural pathway formation essential for genuine learning.

As context engineering replaces prompt engineering and world models expand AI into spatial domains, education faces a critical choice: passively consume AI products that erode cognitive capacity, or actively build pedagogical systems that strengthen it. Professor Pratschke will introduce new frameworks for designing AI systems with learning – not just usage – at their core, offering concrete paths forward for educators ready to shape rather than be shaped by AI's evolution.

Biography

Mairéad is an AI Strategist and Advisor at Mairead Pratschke Ltd., as well as Visiting Professor at the London School of Economics and Political Science (LSE) Data Science Institute. She serves on the advisory board the US National AI Institute for Adult Education and Online Learning (AI-ALOE), where she is also a research fellow.

Keynote Abstract

Artificial intelligence can now generate outputs that meet the requirements of high-stakes assessments across many disciplines. This has sparked concerns about students using AI inappropriately to complete tasks, misrepresenting their abilities. It also raises deeper questions about the sustainability and authenticity of current assessment practices.

This presentation examines how assessment must evolve in response to AI. It draws on the presenter’s work as one of the leaders of Assessment Reform for a Time of Artificial Intelligence, funded by the Tertiary Education Quality and Standards Agency (TEQSA). As AI becomes an ever-present part of professional and academic life, how do we design assessments that both uphold integrity and prepare students for this new reality?

Biography

Professor Phillip Dawson is Co-Director of the Centre for Research in Assessment and Digital Learning (CRADLE) at Deakin University, an internationally recognised research group in assessment scholarship. His work focuses on feedback literacy, academic integrity, cheating, and designing assessment that remains valid and meaningful in the face of emerging digital technologies, including AI. He is highly cited in higher education research and regularly contributes to public discourse on assessment policy, integrity, and innovation.

Panel Discussion 3

Biography

Professor B. Mairéad Pratschke is an internationally recognised expert in digital learning innovation and a pioneer in the field of AI pedagogy. She has been working at the intersection of digital technology and education for 25 years and is currently focused on the future of learning and work, and how to meet the challenges and opportunities that this wave of technological change presents. Author of Generative AI and Education: Digital Pedagogies, Teaching Innovation and Learning Design (Springer, 2024) and New Horizons for Higher Education: Teaching and Learning with Generative AI (2025), Mairéad supports leaders, policy makers, and educators to rethink learning models and lead systems-level change. She works with governments, universities, research bodies and organisations to rethink education and align programmes and pedagogy with the future of work, learning and society. Mairéad is an AI Strategist and Advisor at Mairead Pratschke Ltd., as well as Visiting Professor at the London School of Economics and Political Science (LSE) Data Science Institute. She serves on the advisory board the US National AI Institute for Adult Education and Online Learning (AI-ALOE), where she is also a research fellow.

Professor Phillip Dawson is Co-Director of the Centre for Research in Assessment and Digital Learning (CRADLE) at Deakin University, an internationally recognised research group in assessment scholarship. His work focuses on feedback literacy, academic integrity, cheating, and designing assessment that remains valid and meaningful in the face of emerging digital technologies, including AI. He is highly cited in higher education research and regularly contributes to public discourse on assessment policy, integrity, and innovation.

Mdm Lin Yee holds a Post-Graduate Diploma in Education (PGDE) from the National Institute of Education, Singapore. Before receiving her PGDE, she studied Mathematics at the National University of Singapore, graduating with Master’s degree in Science. Currently, Lin Yee serves as the Divisional Director of the Educational Technology Division, Ministry of Education, Singapore. In her current role, she oversees the implementation of the “Transforming Education through Technology” Masterplan 2030 to ensure that EdTech, when used purposefully and in a well-thought-out manner, can allow learners to reap the benefits of technology while remaining anchored in 21st century competencies. Lin Yee had previously served as Director, Sciences Branch, Curriculum Planning and Development Division, Principal of St. Margaret’s Secondary School and various other leadership roles in schools. These experiences have allowed her to understand the use of EdTech from a curriculum design point of view and be considered when planning for the implementation of EdTech use in schools.

Mr Jason See is the Senior Director of the Education Cluster and Chief Information Officer (CIO) at Singapore’s Ministry of Education (MOE). He leads efforts to harness digital technologies and enhance digital capabilities across MOE and its agencies. Previously, Jason served as the Senior Director of the Data Science and Artificial Intelligence Division at the Government Technology Agency (GovTech), driving digital transformation in the public sector through data science and AI. With extensive experience in public sector technology leadership, Jason is passionate about driving digital innovation and harnessing AI to empower educators and enrich students' learning experiences.

Afternoon - Parallel Talks/ Showcase

Abstract

The rapid rise of artificial intelligence is reshaping the future of work, learning, and society. Higher education must respond by ensuring that graduates are not only AI-literate but also equipped with the timeless skills of critical thinking, collaboration, and problem-solving. This talk, “Empowering Students in the AI Age: Critical Thinking, Collaboration, and Curriculum Reform,” argues that the foundation for critical thinking must be deliberately built in the early years of university study. As students’ progress, they should increasingly engage in authentic, discipline-based tasks where collaboration with both humans and AI systems becomes central to their learning experience. Achieving this requires reimagining traditional 3- and 4-year programmes, including how curricula are sequenced, how assessments are designed, and how interdisciplinary learning is integrated. The session will highlight key challenges—ranging from entrenched assessment practices to faculty readiness and institutional inertia—that must be addressed if universities are to prepare students for success in an AI-driven world.

Biography

Venky Shankararaman is a Professor of Information Systems (Education) and Vice Provost (Education) at Singapore Management University (SMU). He holds a PhD in Engineering from the University of Strathclyde, Glasgow, UK. His current areas of specialization include digital transformation, agentic business processes, and higher education leadership. He has over 30 years of experience in the computing in various capacities as a researcher, academic administrator, educator, IT professional and industry consultant. Professor Venky has contributed significantly to Computing and Information Systems education nationally and internationally, earning several honours such as the Public Administration Medals (Silver and Bronze) from the Singapore Government, SAP Outstanding Academic Award for Southeast Asia, and the Association of Information Systems Award for Outstanding Contribution to IS Education. At SMU, he has received multiple teaching accolades. He also currently serves as President of Asia Pacific Association for International Education (APAIE). He has published over 100 academic papers.
Website: https://scis.smu.edu.sg/faculty/profile/416/venky-shankararaman

Abstract

This talk proposes a 2-by-2 framework that combines a simplified AI Assessment Scale with the University of Sydney’s two-lane approach. This 2-by-2 framework is intended to exhaustively cover all possible assessment approaches and will allow us to systematically develop strategies for each possibility. We will also provide examples and propose approaches to better evaluate student learning.

Biography

Abstract

The use of Generative AI (GenAI) in higher education is rapidly expanding, raising questions about whether its role in student work should be declared, detected, or both. This study explores GenAI use in a graduate-level course (69 students) where essay writing comprised more than half of the final grade. A course policy required students to declare their use of GenAI, specifying whether it was applied to generating ideas, drafting text, refining grammar, or not used at all.

Student declarations were compared with results from Turnitin’s AI detection index to examine consistency between self-reported and system-detected use. Findings reveal patterns of GenAI application, highlight the strengths and limitations of detection tools, and provide insights into student behaviour around disclosure. The paper concludes with practical recommendations for balancing the benefits of GenAI with the need to uphold academic integrity and maintain intended learning outcomes in writing-based assessments.

Biography

Dr. Lena Stephanie is a Senior Lecturer at Nanyang Technological University (NTU), Singapore, and Programme Director of the MSc Project Management programme. She brings over 25 years of experience across IT, consultancy, training, and academia, and teaches interdisciplinary courses bridging engineering and management. Her research explores student engagement and disruptive technologies in education, with a particular focus on generative AI.

Dr. Lee Zheng-wei (Alex) is a Lecturer in Medical Education at the Lee Kong Chian School of Medicine. He is passionate about shaping meaningful learning experiences, with research interests that center on innovative learning design, fostering student engagement, and advancing assessment practices.

Abstract

In this talk, we highlight teaching practices of language, mathematics and science teachers working in collaborative teams to design AI-augmented learning experiences. These practices include: i) fostering metacognitive awareness and monitoring through targeted, timely and adaptive feedback, ii) supporting different learning readiness levels and interests through adaptive learning, and iii) understanding students’ ideas/conceptions and interests to deepen inquiry and further discussion (using LLMs to analyse and summarise student discourse) through using AI features from the Singapore Student Learning Space (SLS). We will also outline the key pedagogical considerations and success criteria these teachers have used in their designs and rise above to areas for future research on teacher practices of AI integration into teaching and learning, and implications for teacher practice of teaching with and through AI.

Biography

Samuel is a Lead Specialist in the Educational Technology Division at the Ministry of Education Singapore. He develops guidelines and resources for teaching and learning with technology and supports teacher professional learning and scaling of e-Pedagogy in schools. He also contributes to the integration of technological considerations in the design of curriculum and learning resources. Furthermore, Samuel conducts translational research on the use of affective technologies and pedagogies to increase learning motivation and foster competencies for emotion and motivation regulation in learning. His research interests include:

knowledge-building pedagogy and technology,
collaborative inquiry-based learning,
epistemic emotions and motivation regulation in learning, and
use of AI and GenAI to augment creativity, critical thinking, and enhance teaching and learning

Afternoon - Parallel Talks/ Showcase (NIE)

Abstract

Recent advances in artificial intelligence (AI) technologies have led to innovations in science education. However, concerns have been raised about how AI-integrated science education can achieve our sublime educational ideas. Discourse to date has employed various and often confounding terms, some originating from the science education field (e.g., equality, equity, transparency, liberation, joy, and justice), while others from the AI field (e.g., bias and ethics), making it challenging to establish a comprehensive understanding. In this presentation, we first review the definitions of key terms from the two fields. We then propose a framework for addressing bias and ethics in AI in relation to justice in science education. The framework suggests that biased/unbiased and unethical/ethical are two different dimensions. Consequently, there can be states that are biased and unethical (e.g., reality), unbiased but unethical (e.g., equality), biased but ethical (e.g., equity), and finally, unbiased and ethical ones as justice (e.g., transparency, liberation, and joy). It is recommended to prioritize ethical states before unbiased ones. Among the unbiased and ethical states of justice, transparency is more realistic, safe, and achievable than liberation and joy. This presentation contributes to properly orienting the ongoing transformation of science education research and practice in response to evolving AI technologies.

Biography

Assistant Professor Lee Gyeonggeon is an academic in the Natural Sciences and Science Education Department, National Institute of Education, Nanyang Technological University, Singapore. His research focuses on integrating artificial intelligence (AI) into science education, encompassing automated assessment, human-AI collaboration in learning, and laboratory safety management. His previous research publications have covered a wide range of topics, including the history and philosophy of science education, curriculum studies, educational technology, and electrochemistry. Building on his diverse research experiences and the interdisciplinarity enabled by AI, he aims to establish multimodal science learning to fulfill whole-person education.

Abstract

The rapid rise of generative AI has prompted educators to rethink their role in assessment. This study explores how Large Language Models (LLMs), such as ChatGPT, can act as partners in creating high-quality STEM assessment items. We systematically generated pre-university chemistry, physics, and mathematics questions using three prompting strategies: standard prompting, chain-of-thought prompting, and chain-of-thought prompting with coding. Expert evaluations showed that chain-of-thought prompting produced the strongest results, with ChatGPT with coding achieving the highest final-answer accuracy. However, intermediate reasoning remained only moderately reliable, underscoring the need for teacher review. This work provides practical guidance for teachers to leverage generative AI to design rigorous, conceptually sound STEM assessments.

Biography

Dr Joonhyeong Park is an assistant professor at the National Institute of Education, Nanyang Technological University. His research on multimodal representation in science education focuses on understanding how the coordination and integration of multimodal representations support science teaching and learning. In relation to AI literacy, he investigates approaches to fostering students’ comprehension and application of AI concepts, tools, and methodologies within the context of science education.

Abstract

AI is increasingly prevalent across educational and societal domains, and the chatbot empowers student teachers to build their conceptual maps of teaching and learning, providing scaffolded support that encourages clarity of thought and self-assessment. However, it is critical that student teachers retain agency in this process. They are not passive recipients of AI-generated insights, but active meaning-makers who engage critically with the feedback they receive.

Biography

Associate Professor Chua Bee Leng is an academic from Nanyang Technological University, Singapore, with expertise in using digital tools and AI to support teacher training and reflective learning. Her work focuses on designing educational technologies—such as chatbots and digital portfolios—that promote self-assessment and student teacher agency. She is interested in how AI feedback can scaffold clarity of thought and reflective practice in teacher education.

Abstract

This presentation explores the integration of adaptive technology to enhance the teaching and learning of affixes in Malay grammar. With the growing demand for individualised education and the rise of advanced technologies such as Artificial Intelligence (AI), personalised learning has become increasingly prominent. AI-powered adaptive systems offer the potential to transform learning experiences by delivering real-time feedback and tailored instructional pathways. The Personalised Adaptive Learning and Assessment System (PALAS) – an AI-enabled, learner-centric platform – was developed and designed to support student teachers in the Malay Morphology modules at the Asian Languages and Cultures (ALC), NIE-NTU, Singapore. PALAS aims to provide each learner with a customised learning journey aligned with their unique needs and progress. Findings from the implementation of PALAS indicate promising outcomes, including measurable learning gains, increased learner motivation, deeper understanding of grammatical concepts, and enhanced confidence in teaching the Malay language. This presentation will highlight key adaptive learning features within PALAS – such as curriculum mapping, iterative learning loops, diagnostic assessments, and mastery tracking – that contribute to these positive results.

Biography

Suryani Binte Atan is a Teaching Fellow in the Department of Asian Languages and Cultures at the National Institute of Education, Nanyang Technological University. Her work focuses on multilingual AI‐tutors and voice/chatbot technologies for young learners, especially in Singapore’s Mother Tongue Languages (MTLs). She leads projects such as MAGIC, LEARN, SingaKids Pic2Speak and PALAS aimed at enhancing oral proficiency, assessment, and learner engagement in early language education and teacher training.

Abstract

Drawing on experiences with curating new AI for education coursework across higher education contexts in Europe and Asia over the past 5 years, I will synthesize common threads and disparities in how we can deliberately design meaningful learning opportunities for our students. At the same time, I will provide a few concrete examples of pedagogical affordances that may serve as starting points to engage students in responsible co-construction with AI.

Biography

Dr Tanmay Sinha is an Assistant Professor at National Institute of Education, Nanyang Technological University in Singapore. He obtained a master’s degree in artificial intelligence from Carnegie Mellon University (USA) and completed his doctoral work in the learning sciences at ETH Zurich (Switzerland). Tanmay served as executive director for the first ETH-EPFL joint doctoral program in the learning sciences in Switzerland during its formative years (2021-2023), where he co-developed the academic program strategy, formulated and taught courses on learning sciences foundations and artificial intelligence for education. Tanmay's sustained, influential learning sciences scholarship from 2015-2024 has garnered over 2300 citations (h-index 22) and has been cited nearly four times more than the global average within education (field-weighted citation index 4.00). His work has appeared in flagship avenues such as Journal of the Learning Sciences, Journal of Educational Psychology, Review of Educational Research, Learning and Instruction, Cognitive Science, Technology Knowledge and Learning.

Abstract

While advances in educational technology and AI have transformed how we teach and learn, the emotional dimensions of learning, particularly boredom, remain critically underexplored. Unlike disengagement, which can be more overt, boredom tends to manifest itself as a quiet, underlying emotional state that significantly impacts student motivation, focus, and cognitive performance. This presentation provides a holistic overview of boredom as an emotion and its role in the learning process. During this presentation, the presenter will also provide a snapshot of his current research project, which aims to develop an automated framework to detect boredom through physiological signals and machine learning approaches. This framework has the potential to support strategies to manage boredom in the classroom and increase students' engagement, which are essential for fostering flow states during learning.

Biography

Dr Yuvaraj Rajamanickam is a Lecturer (Research Scientist) at the Science of Learning in Education Centre (SoLEC), National Institute of Education (NIE), Singapore. His research focuses on affective computing and the role of emotions such as boredom in learning, using physiological signals and machine learning to detect and support student engagement. He works at the intersection of educational psychology, human–computer interaction, and AI to design systems that enhance motivation and learning outcomes.

Afternoon - Symposium & Closing

Symposium

The emergence of Generative AI (GenAI) presents education with a pivotal moment that demands careful consideration of how we conceptualize it in relation to learning. Tan (2024) proposes a framework to conceptualize GenAI in education in three broad categories: 1) learning from GenAI through personalized tutoring, 2) learning with GenAI as a collaborative partner, and 3) learning about GenAI to develop AI literacy. While much of the research and practice effort has been focusing on ‘learning from GenAI’, there has been notably less work in the area of ‘learning with GenAI’, and even less on the intersection between knowledge building and GenAI. Knowledge Building has distinguished itself from other educational approaches by transforming education from a model of knowledge transmission to one that engages students directly in the means by which knowledge advances in the world. We argue that knowledge building requires “learning beyond AI”, developing meta-skills, where learners and educators orchestrate various AI usage that are responsive to their knowledge building trajectory and intentionally refrain from using AI when it is judged to undermine learners’ epistemic agency. This symposium presents cutting-edge research that explores how GenAI can be designed to advance authentic Knowledge Building in the emerging “learning beyond AI” paradigm.

Biography

Dr. Bodong Chen is an Associate Professor in Learning Sciences and Technologies at the University of Pennsylvania Graduate School of Education, where he directs the Penn GSE Wonder Lab and the Penn Knowledge Building Innovation Network. His scholarly inquiry integrates knowledge media design, software engineering, and data science methods to continually improve infrastructures for learning. Guided by design-based research and participatory design approaches, he aims to generate justice-oriented pedagogical designs, technological innovations, and empirical understandings of learning in authentic settings.

Dr. Chew Lee Teo is Deputy Centre Director of Centre for Research in Pedagogy and Practice at the Office of Education Research, National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore. Her main research interests are in the areas of Knowledge Building Pedagogies and Learning Analytics. She founded the Knowledge Building Community - Singapore since 2016. Each Network Learning bring together teachers and researchers to explore germane issues on 21st-century competencies and pedagogies. These network learning sessions have reached more than 1000 teachers and researchers.

Dr. Seng Chee Tan is an Associate Professor, Provost’s Chair in Education at Nanyang Technological University and Associate Dean in the National Institute of Education, specialises in leveraging emerging technologies to enhance pedagogy. His research focuses on integrating emerging technologies such as artificial intelligence into educational practices and exploring the effects of transformative approaches like knowledge building in real-world classroom settings. He also plays a key role in advancing teacher professional development, particularly in the area of emerging technologies.

Dr. Katherine Guangji YUAN is an Education Research Scientist at the Centre for Research in Pedagogy and Practice, Office of Education Research, National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore. Her research focuses on the intersection of AI in education and Knowledge Building, with particular emphasis on learning analytics and innovative research design. She has led initiatives such as the AI-Coding Hackathons for low-SES students, which aim to promote equitable AI literacy and foster epistemic agency.

Keynote Abstract

The rapid rise of generative AI has sparked debate about its role in education. Rather than positioning AI and humans in opposition, researchers advocate for intelligent augmentation—a human-AI partnership that enhances human capacity. In this model, teachers become designers, enablers, and facilitators of learning, supported by AI tools. Beyond augmentation, such partnerships can drive transformative change in education. When combined with approaches like knowledge building, AI can act as an idea co-constructing partner, accelerating the speed, depth, and quality of collective idea improvement. Embedding AI into teacher knowledge-building communities can enrich professional learning and catalyse pedagogical transformation.

Biography

Professor David Hung, Centre Director of Science of Learning in Education at NIE, NTU Singapore, and President’s Chair in Learning Sciences, explores learning through instructional technologies including AI. His research spans constructivism, cognition, and communities of practice, with a focus on sociocultural contexts in Singapore schools. Prof Hung integrates the science of learning with neuroscience, physiology, and biological indicators to deepen understanding of educational systems. His work juxtaposes systemic educational frameworks with the science of learning, positioning AI as one of the tools in advancing instructional design and understanding cognitive processes in diverse learning environments.

Associate Professor Tan Seng Chee, Provost’s Chair in Education at Nanyang Technological University and Associate Dean in the National Institute of Education, specialises in leveraging emerging technologies to enhance pedagogy. His research focuses on integrating emerging technologies such as artificial intelligence into educational practices and exploring the effects of transformative approaches like knowledge building in real-world classroom settings. He also plays a key role in advancing teacher professional development, particularly in the area of emerging technologies.