Teaching

Teaching and course design

Making advanced AI accessible to students from diverse technical backgrounds.

By the numbers

Teaching at a glance

Courses taught: 17 across two universities
Largest cohort: 391 students
Lecturer evaluations: 4.9 to 5.6 / 6 across the courses I designed and lead
Certification pass rates: 96% and 98% across two industry programs

Courses at UNSW Canberra

Undergraduate

ZEIT4150 Fundamentals of Artificial Intelligence

A tour of the breadth of artificial intelligence: search, knowledge representation, reasoning, planning, and natural language processing. Students learn to formulate real-life problems as well-defined AI problems, design and validate AI solutions, and communicate AI challenges, solutions, and ethics to non-technical audiences.

The course is hands-on throughout, from problem-solving with state-space search to designing and implementing an adversarial game-playing agent and an individual AI project. By the end, students hold the foundations of the field, ready for advanced study or a fourth-year thesis in AI.

My role: Designed and developed the course; convenor and lecturer
Deliveries: Five, S1 2022 to S1 2026
myExperience lecturer score: 5.0 to 5.4 / 6 in every evaluated delivery

Undergraduate

ZEIT4151 Machine Learning

Hands-on machine learning for engineering, computer science, and cyber security students: mapping real-life problems to mathematical models machines can optimise and learn from, interpreting how data shapes what a model learns, and judging which algorithm suits which problem and why.

Students build and validate working systems through applied projects that progress from model validation and feature extraction to machine learning with images, delivered as code with explanation so the reasoning matters as much as the result.

My role: Co-designed and co-developed; lecturer
Deliveries: Four: S2 2022 to S2 2025
myExperience lecturer score: 4.9 to 5.6 / 6 in every evaluated delivery

Postgraduate · online

ZEIT8601 Applied Machine Learning

Built for postgraduates across cyber security, decision analytics, space, project management, and systems engineering: when and how to apply machine learning to real problems. Students master learning from data, feature selection, regression, classification, and clustering, and the differences between supervised and unsupervised learning, including bias in machine learning.

The course works across modalities, from tabular data to text and images, and closes the loop that matters in practice: evaluating models statistically and communicating findings in a form decision makers can act on.

My role: Designed and developed the course; convenor and lecturer
Deliveries: Two: S2 2024, S2 2025
myExperience lecturer score: 5.2 to 5.3 / 6 in every evaluated delivery

Undergraduate

ZEIT3101 IT Project 2

Project-based course in which student teams carry an IT build through requirements, development, and delivery, practising the collaboration and project discipline the industry expects.

My role: Co-taught
Deliveries: One: S2 2025

Undergraduate · first year

ZPEM1307 Computational Problem Solving

First-year computational thinking and programming at scale. I contributed to the delivery of a 201-student cohort as lab demonstrator.

My role: Lab demonstrator
Deliveries: One: S1 2024

Courses at Zagazig University

As a Lecturer from 2014 to 2017, I delivered nine undergraduate and three postgraduate courses, including core first-year courses to cohorts of up to about 390 students.

Undergraduate
Introduction to Computers (CS120), Operating Systems (CS260), Advanced Operating Systems (CS400), Computational Intelligence (CS415), Human-Computer Interaction Design (CS465), Data Analytics for Big Data (CS475), Big Data Developer (CS420), and Selected Topics in IT and IS (IT475, IS475).
Postgraduate
Machine Learning (CS617), Natural Language Processing (CS606), and Data Analytics for Big Data (CS607).

Student voices

What students say

“Step-by-step explanations, timely actionable feedback, and generous availability that linked theory to practice.”Student, ZEIT8601 Applied Machine Learning, 2025

“Her approachable nature and willingness to help students made a significant positive impact on the learning experience.”Student, ZEIT4150 Fundamentals of AI, 2024

“Good engagement with class, excellent explanations of complex concepts.”Student, ZEIT4151 Machine Learning, 2024

Quotes are anonymous student responses from UNSW myExperience surveys.

Awards

Teaching awards

Dell EMC Award of Distinction · 2017For teaching data analytics for big data and preparing students for the EMC Data Scientist Associate certification: 317 of 323 enrolled students certified, a 98% completion rate.
IBM Big Data Developer Instructor Award for Educators · 2017For teaching the Big Data Developer course and preparing students for IBM certification: 92 of 96 enrolled students certified, a 96% completion rate.

Leadership

Building AI education, not just delivering it

As AI Discipline Coordinator (2021 to 2023) I oversaw the design, resourcing, and first deliveries of the new AI course stream in the engineering program, and I later joined the faculty AI Education Strategy Workgroup that shaped the new postgraduate AI courses introduced in 2024, leading the development of the first of them, Applied Machine Learning. I have also served on the assessment panels for fourth-year Artificial Intelligence engineering projects.

Approach

Accessible by design

I design layered, multi-level learning resources so students at different starting points can all engage with advanced material, structuring each course to build from foundations toward authentic, applied problem-solving. This approach has earned consistently strong student satisfaction.

Credentials

Grounded in teaching practice

My teaching is backed by formal development: the UNSW Course Design Institute program, the Foundations of University Learning and Teaching program accredited by Advance HE, and the UNSW Graduate Teacher Training Program.