I lecture and lead studio-based learning across Human-Computer Interaction, Design Thinking, UX/UI Design, and Data Structures & Algorithms at NTU. My teaching is informed directly by my research in HCI in Education, with a focus on game-based learning, productive failure, and AI-augmented pedagogy.
Courses where I serve as lecturer — designing the curriculum, delivering lectures, and leading tutorials and laboratory sessions.
Introduces the principles and practices of designing interactive digital systems that are user-friendly, accessible, and aligned with user needs. Students learn foundational theories in HCI — including cognitive models and interaction paradigms — and apply them through hands-on projects involving task analysis, interface prototyping, and usability evaluation. The course equips students with both conceptual understanding and practical skills for creating effective and meaningful user experiences, and serves as a strong foundation for careers in UX design, interface development, or further studies in human-centred computing.
Covers (i) fundamental data structures — arrays, linked lists, stacks, queues, and trees — that are critical for efficient algorithms; (ii) algorithm analysis, including time and space complexity; and (iii) searching techniques such as sequential and binary search, hash tables with collision-resolution methods, and string search using Tries. In AY2025 the course was fully redesigned and converted from C to Python, with new OBTL-aligned lecture notes, tutorials, labs, assignments, lab tests, and practice questions, aligning the syllabus with CCDS’s strategic emphasis on Data Science and AI.
As part of the Learning with AI (LWAI) initiative, lab sessions now run on AlgoGPT — a multi-agent LLM tutoring platform grounded in Cognitive Load Theory and Self-Regulated Learning. The system pairs a Driver Agent that delivers strictly non-directive feedback in four tiers (analysis → hints → questions → suggestions) with a Navigator Agent that injects plausibly flawed code calibrated to each student’s reasoning level, requiring them to evaluate and accept or reject the AI’s output. The design transforms AI from an answer generator into a Socratic coach that promotes productive struggle and metacognitive reflection.
Deployment at scale (Jan–Apr 2026): 1,000 active students, 10,333 sessions, 35,433 code submissions across 14 lab questions over 7 lab weeks. Mode-level evidence shows distinct, statistically significant learner behaviour (Kruskal–Wallis H = 886.01, p < 10−192): Navigator Mode produces the most efficient solution paths (1.80 attempts to first accept) while Driver Mode produces the highest-quality submissions (5.13/10 tests passed). An accompanying SRL survey (N = 155) returned an overall mean of 3.39/5 with Reflective Self-Evaluation the strongest dimension, all 12 items above neutral midpoint at p < 0.05. AlgoGPT’s findings are reported in Beyond the Perfect Assistant: Provoking Learning with Flawed AI Partners (ACM DIS 2026) and at NIE RPIC 2026.
Cultivates a human-centred mindset and practical problem-solving skills through the iterative design-thinking process. Students learn to empathise with users, define meaningful problems, ideate creative solutions, prototype effectively, and test iteratively. By integrating insights from real-world contexts and interdisciplinary perspectives, students develop the ability to design innovative, desirable, and feasible solutions for complex challenges. The course prepares students to apply design thinking across technology, product development, service design, and social innovation, with an emphasis on usability, empathy, and impact.
An integrative, project-based course in which students apply the full arc of skills developed across the B.Tech programme — problem framing, design thinking, user research, software development, and evaluation — to deliver a substantial team project. Students work under close supervision to scope a real-world challenge, prototype iteratively, and present a polished outcome with documented impact.
Provides an introduction to HCI, with the overarching goal of inculcating a user-centric perspective on usability when designing, evaluating, and innovating user interfaces. Students learn to: (a) appreciate the significance of usability in interface development, including user requirements, measurements, and various usability tests; (b) acquire vocabulary to frame and articulate HCI considerations across computing applications; (c) apply first principles of UI design to current and future interface modalities; (d) align HCI with human thought processes and physical abilities; and (e) appreciate how HCI design is applied across sectors of the computing industry.
Provides a comprehensive understanding of user experience and user interface design principles, methodologies, and tools. Students learn to conduct user research, develop personas, create information architectures, wireframes, and prototypes, and apply visual design principles such as colour theory, typography, and layout systems. The course emphasises usability testing to evaluate and improve design quality through iterative feedback, and covers responsive design and cross-platform consistency. Graduates leave able to design functional, aesthetically appealing, and user-centred interfaces that effectively meet user needs and expectations.
An intensive month-long delivery of the SC3061 HCI curriculum, taught annually since 2020 as part of the GEM Trailblazer Summer Programme (3 AUs, June–July). The full course — 40 hours of teaching spanning lectures, tutorials, labs, and a final examination — is compressed into a single month. The cohort is highly international: visiting undergraduates from partner universities across North America, Europe, and Oceania study alongside NTU students, building lo-fi and hi-fi UI prototypes, conducting usability evaluations, and developing fluency in user-centred design vocabulary and methods. Delivery is physical on the NTU campus; a computer-science background is preferred but not required.
Courses where I lead tutorials and laboratory sessions, supporting the lecturer of record. These contributions help large core courses scale while maintaining small-group, hands-on instruction.
A core part of my work at NTU is supervising students and research staff — from doctoral candidates and Master’s researchers through to FYP and URECA undergraduates. Many former students and staff have moved on to research and engineering leadership roles internationally.