Outline

Overview

ENGG2112 provides an introduction to the context of engineering practice and how engineers engage with other professions in concept development, analysis, and planning. Students are introduced to basic concepts in data science used by engineers to understand problems, support decision making, and run systems. Students will then work within teams to address components of a complex multi-disciplinary project relevant to their chosen engineering stream. In the process, students will consider the influence of contextual factors such as regulatory frameworks, economics, and societal expectations. In doing so, student teams will draw from various fields such as economics, law, business, and the social sciences as they complete the project.

Unit details and rules

Academic unit	Engineering
Credit points	6
Prerequisites ?	(INFO1110 or INFO1910 or ENGG1810) and (MATH1005 or MATH1905 or MATH1062 or MATH1962 or MATH1972 or BUSS1020) and (AERO1560 or BMET1960 or CHNG1108 or CIVL1900 or ELEC1004 or ELEC1005 or ENVE1001 or MECH1560 or MTRX1701)
Corequisites ?	None
Prohibitions ?	ENGG1111 or ENGF1112
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Teng Joon Lim, tj.lim@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Exam to test achievement of unit learning outcomes, with a hurdle section that students must achieve a certain score on to pass the unit of study.	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	EFT: AI to Accelerate Learning Submit the AI conversation transcript and a brief explanatory video for an interaction to learn about a topic covered in lectures to date.	5%	Week 03	No limit.	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Presenting technical topic to a small group Each student is tasked to find out about a relevant technical topic and explain it to their team. AI is allowed for the research, but the in-class component is a presentation and AI cannot be used.	5%	Week 05	5 minutes	AI prohibited
Outcomes assessed:
Practical skill	AI for Learning Part 2 Submit an AI conversation transcript for an interaction to learn about a topic relevant to the group project. Students will have to explain that topic to the rest of the team verbally in class.	5%	Week 05	No limit	AI allowed
Outcomes assessed:
Experimental design	Project Proposal Description of project justification, workplan, objectives and deliverables. Individualised marks will be moderated by SPARKPLUS peer evaluation.	10%	Week 06	Lesser of 3 pages or 1000 words	AI allowed
Outcomes assessed:
Written work	Project Final Report Final project report. Requirements will be discussed in class. Individual marks will be derived from the group mark and moderated by SPARKPLUS assessment.	15%	Week 12	Lesser of 10 pages or 5000 words.	AI allowed
Outcomes assessed:
Q&A following presentation, submission or placement	Q&A on Project Individual oral Q&A	20%	Week 13	5 minutes	AI prohibited
Outcomes assessed:
Attendance - accreditation or faculty requirement	Tutorial attendance Students must attend at least 80% of all tutorials. Attendance is only recorded if a student stays for at least three-quarters of the duration of the class.	0%	Weekly	NA	Not applicable
Outcomes assessed:
= hurdle task ? = group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

AI transcripts: An early feedback task in Week 3 and a homework assignment in Week 5 will ask students to engage constructively with generative AI in learning socio-technical aspects of AI in engineering. Guidance will be provided on how to prompt AI productively and students' AI conversations will be marked.

Oral presentations and defences: In Week 5, students will be asked to teach their project team members about a topic relevant to their project that they learned using AI. In Week 13, individual Q&As will test students' ability to discuss what they did in the project.

Group Written Reports: A project proposal is due in Week 6 and a final report in Week 12. This will be marked on organization, clarity and general ability to write a professional technical report.

Final Exam: This will test the achievement of key learning outcomes and will have a hurdle section.

Attendance: At least 80% attendance at tutorials is required, with attendance defined as physical presence in class for more than 3/4 of the class.

Assessment criteria

Result Name	Mark Range	Description
High Distinction	85 鈥撎�100	When you have surpassed the required learning outcomes and shown initiative/effort well beyond what was expected
Distinction	75 鈥撎�84	When you show that you have achieved the learning outcomes to a very high level
Credit	65 鈥撎�74	When you demonstrate a more than adequate achievement of the learning outcomes
Pass	50 鈥撎�64	When you barely demonstrate achievement of the learning outcomes
Fail	0 鈥撎�49	When you don鈥檛 meet the learning outcomes of the unit to a satisfactory standard

听

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the听Academic Integrity Policy.

Visit the听Current Students website听for more information on AI in assessments, including听details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In line with University policy, submissions after the closing date will not be accepted. Any late submission must have approved Special Consideration. For quizzes, this may allow an alternative in-class sitting to be arranged with the tutor. The final presentation cannot be delayed or rescheduled. SPARKplus peer evaluation must be completed by the submission deadline; late or missing SPARKplus submissions will not be counted. If you expect any difficulty meeting a deadline, you should contact your tutor or the unit coordinator before the due date. Unforeseen issues that affect submission must be supported by approved Special Consideration.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website听provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the听Academic Honesty Education Module (AHEM)听which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the听Current Students website听for more information on AI in assessments, including听details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a听simple extension.鈥� The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can鈥檛 complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for听special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students,听听includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy听reflects the University鈥檚 commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Ongoing	Group project defined by students, guided by unit instructors. Implementation of ML in Python.	Self-directed learning (80 hr)
Week 01	Introduction and Motivation	Lecture (2 hr)
Week 01	Introducing generative AI as a powerful learning tool and helpful guide.	Tutorial (2 hr)
Week 02	Responsible Use of AI	Lecture (2 hr)
Week 02	Academic integrity, reflect on personal use of AI, plagiarism versus referencing. Team formation.	Tutorial (2 hr)
Week 03	Models and Training	Lecture (2 hr)
Week 03	AI for coding ML models, training, etc.	Tutorial (2 hr)
Week 04	Classification and Regression	Lecture (2 hr)
Week 04	Classification and regression key concepts. Group discussion and group work.	Tutorial (2 hr)
Week 05	AI and Academic Integrity	Lecture (2 hr)
Week 05	Verify AI outputs against sources, think critically to generate further questions, understand limitations and power of AI. Project development.	Tutorial (2 hr)
Week 06	Neural Networks	Lecture (2 hr)
Week 06	Neural networks key concepts and coding of simple models. Project ideation.	Tutorial (2 hr)
Week 07	Generative AI Part 1	Lecture (2 hr)
Week 07	Basics of generative AI. Project proposal discussion.	Tutorial (2 hr)
Week 08	Generative AI Part 2	Lecture (2 hr)
Week 08	Reflect on technical aspects of AI learned to date, importance of critical thinking, potential to use AI as a collaborator in the project.	Tutorial (2 hr)
Week 09	Deep Fakes and Misinformation	Lecture (2 hr)
Week 09	Social implications of misinformation and deep fakes, engineering use cases, relevance to project. Further project development.	Tutorial (2 hr)
Week 10	Human-AI Interaction and Collaboration	Lecture (2 hr)
Week 10	Examples of each level of human-AI interaction, ethical issues at each level, value of the human input. Further project development.	Tutorial (2 hr)
Week 11	Security and Privacy	Lecture (2 hr)
Week 11	Risks of AI use, safeguards needed. Discuss last major pieces of work needed in project.	Tutorial (2 hr)
Week 12	Ethics, Authorship and Ownership	Lecture (2 hr)
Week 12	Evaluate ethics and ownership of various AI-native scenarios. Finalize project.	Tutorial (2 hr)
Week 13	Wrap Up	Lecture (2 hr)
Week 13	Final project presentation	Tutorial (2 hr)

Attendance and class requirements

Attendance at 80% or more of tutorials is a hurdle requirement.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

LO1. Articulate reasoning and justify creative solutions to an engineering problem solvable using machine learning.
LO2. Find and interpret information autonomously and demonstrate capacity for independent learning.
LO3. Apply basic project management techniques to manage self and others in a team, and to plan an engineering solution.
LO4. Analyze and manipulate medium-scale datasets to extract meaningful machine learning models.
LO5. Under guidance, identify and apply appropriate machine learning concepts and methods to develop an engineering solution.
LO6. Understand how data is stored, interpreted and processed for engineering applications.
LO7. Appreciate the context of data-driven engineering solutions, including applicable regulatory frameworks, standards, community expectations and commercialization opportunities.

Graduate qualities

The graduate qualities are the qualities and skills that all 爆料王 graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one鈥檚 personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.2. Information literacy and the ability to manage information and documentation. 4.5. An ability to undertake problem solving, design and project work within a broad contextual framework accommodating social, cultural, ethical, legal, political, economic and environmental responsibilities as well as within the principles of sustainable development and health and safety imperatives. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 3.2. Information literacy and the ability to manage information and documentation. 3.7. A capacity for lifelong learning and professional development and appropriate professional attitudes.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.6. An ability to function as an individual and as a team leader and member in multi-disciplinary and multi-cultural teams. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements. 4.5. An ability to undertake problem solving, design and project work within a broad contextual framework accommodating social, cultural, ethical, legal, political, economic and environmental responsibilities as well as within the principles of sustainable development and health and safety imperatives. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
	Engineers Australia Curriculum Performance Indicators - 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 3.2. Information literacy and the ability to manage information and documentation. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 3.4. An understanding of and commitment to ethical and professional responsibilities.

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

Major changes in this semester in response to student feedback and the rapid growth in AI use. Less time will be spent in class on weekly check-ins, freeing up time for tutors to teach and guide students. Closer connections between lectures and tutorials are made. AI usage by instructors will be clearly indicated and justified. Attendance in at least 80% of tutorials will be required. A final exam will be used to better gauge students' individual efforts throughout the semester.

Disclaimer

Important: the 爆料王 regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an .

爆料王

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

爆料王

ENGG2112: Multi-disciplinary Engineering

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links