Teaching Statement

I teach software engineering as a practice of building, explaining and improving software for people. Students need to connect requirements with design decisions, test whether their solutions work, and communicate the limitations of what they have built. My role is to make that process learnable: break complex work into meaningful stages, give feedback that students can act on, and ask them to justify decisions with evidence rather than rely on a polished final demonstration.

Download my two-page teaching portfolio (PDF). This statement and the portfolio draw on the same teaching records; the page provides additional context and links. Updated October 2026.

Teaching experience and responsibilities

Between 2021 and 2024, I taught tutorials or supervised projects in five undergraduate and postgraduate subjects at two Australian universities:

• The University of Melbourne: COMP90082 Software Project, SWEN90009 Software Requirements Analysis, and SWEN90014 Masters Software Engineering Project, as a project supervisor, with tutoring and workshops in SWEN90014; and ISYS90043 Enterprise Applications & Architectures, as a tutor.
• Deakin University: SIT217 Introduction to Software Engineering, as a tutor supporting tutorials and practical work.

These course-team roles included requirements elicitation, design and prototype review, implementation planning, usability evaluation, testing, project management, consultation, marking and feedback, according to the subject.

More recently, I have contributed to learning design in two courses at Universitas Palangka Raya: Human-Computer Interaction (1DCP353030, 2025) and Software Engineering (1DCP362030, 2026). Both are led by Dr Devi Karolita; I collaborate with her and Dr Jialong Li of Waseda University. At Monash, I contributed an invited recorded interview for AHC5002 Technology in Human Biomechanical Analysis in 2026. Together, these provide eight teaching and learning-design contexts across four institutions, with different responsibilities in each.

Example 1: Making accessibility part of the design process

In the Palangka Raya Human-Computer Interaction course, I helped design a group project around Mobile JKN, Indonesia’s national mobile health application. Rather than add accessibility as a final checklist, we embedded it in four assignments: Discover, Design, Prototype and Evaluate. A cross-phase persona activity connected user evidence with requirements, while a guideline-reflection activity asked students to explain how accessibility guidance affected their design choices.

My contribution included co-designing assignments and teaching materials and supporting evaluation of students’ confidence and learning. This gives students opportunities to revisit assumptions across the development cycle, rather than treat requirements as fixed at the first submission. The educational design is documented in our two-page ICSE 2026 Posters-track paper in the ICSE-Companion proceedings. The pre-course survey of 45 students describes an initial learning need; it is not evidence of improved outcomes after teaching.

Course responsibilities and materials context · Project and teaching artefacts · Publication

Example 2: Teaching critical use of generative AI

In the 2026 Software Engineering course, I help design activities in which students use interview evidence with CRAFTER, our structured human-AI collaboration framework, and general-purpose generative AI. Students create, review and refine software-engineering artefacts, including personas and requirements. The teaching aim is not simply to produce more text: it is to keep generated claims connected to evidence and make students’ reasoning visible.

I contributed group-project tasks, supporting materials, pre- and post-course surveys, comparison activities and reflective questions. Analysis of feedback and interaction histories examines prompting, refinement, evidence use and cognitive load. This is an ongoing evaluation, not a claim that one tool has already produced better learning. It informs my approach to responsible AI in teaching: assess how students check and revise an output, not only whether the final output looks plausible.

AI-integrated Software Engineering course and contribution

Example 3: Connecting student work with professional practice

As a Monash Engineering industry-experience host supervisor, I supervised Khang Tieu and Yi Lai, who each completed a 115-hour placement between November 2025 and February 2026. Their work produced an integrated accessible wayfinding prototype, followed by a demonstration, a README, a demo video and a handover that documented limitations. These deliverables made technical progress and professional communication visible at completion.

Two further placements, Coeun Ham and Tony Tian-Yi Liang, run from July to October 2026. Their projects connect evidence with virtual-reality filming scripts and compare manual and AI-assisted requirements work using de-identified consent interviews. In this setting, professional learning includes explaining what evidence supports a design, recognising uncertainty and handing work over so another person can continue it.

My partner-engaged research with Wallara Australia and Waverley Social Enterprises offers a basis for future, permission-cleared teaching briefs about accessible technology and workplace needs. Research partnerships are not automatic placement agreements: any student access, data use or partner participation must be agreed separately.

Completed placement and outputs · Coeun’s placement · Tony’s placement

Inclusive participation and reflective improvement

My research and co-design work with people with intellectual disability and neurodivergent people informs how I think about communication, participation and choice. In university teaching, I would apply these lessons through plain-language task instructions, staged milestones, worked examples, accessible materials and different ways to seek feedback, while working with the university’s accessibility services on individual adjustments.

My existing practice uses project discussions, student consultation and feedback to identify where students need support. A priority for further development is to document the changes that follow: which instruction was unclear, what I revised, and whether later student work shows the intended understanding. I would combine student feedback with peer observation and assessment artefacts, keeping satisfaction, participation and demonstrated learning distinct.

Future software-engineering teaching

I can contribute most directly to requirements engineering, software projects, human-computer interaction, software quality and responsible AI-supported development. My next teaching-development priority is to connect these areas through authentic tasks: a defensible requirement, an accessible prototype, a justified test and a clear handover. In security-related curricula, my contribution is the human-centred software-engineering interface, including consent, risk communication and user understanding.