Outline

Overview

New digital technologies are rapidly emerging as conduits for criminality as well as tools of policing and criminal procedure, and in the infrastructure of punishment. In this unit of study, students will learn about the impacts of technologies on crime and justice: in the commission of new crimes, in law enforcement and regulatory challenges, national security, and digital transformations in court procedure and court space, algorithmic risk assessments, corrections, and in the supervision of offenders. This unit of study draws from socio-legal literature and the emerging field of Digital Criminology to understand the profound shifts occurring in the move from terrestrial to digital environments, and from human 'reality' to augmented, virtual and artificial platforms in the criminal justice system. Refer to the Sydney Law School timetable - https://canvas.sydney.edu.au/courses/4533/pages/postgraduate-lecture-timetable

Unit details and rules

Academic unit	Law
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Carolyn McKay, carolyn.mckay@sydney.edu.au
Lecturer(s)	Carolyn McKay, carolyn.mckay@sydney.edu.au

Assessment

The census date for this unit availability is 7 November 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Reflective Journal (30%) Written task	30%	Week -01 Due date: 19 Oct 2025 at 17:00 Closing date: 26 Oct 2025	2000 words/9 days	AI allowed
Outcomes assessed:
Out-of-class quiz	Multiple Choice Quiz (10%) Online quiz	10%	Week 01 Due date: 26 Oct 2025 at 17:00	10 randomised multiple choice questions	AI allowed
Outcomes assessed:
Case studies	Essay Plan (10%) Essay plan for case study	10%	Week 03 Due date: 06 Nov 2025 at 17:00 Closing date: 16 Nov 2025	1000 words/4 weeks	AI allowed
Outcomes assessed:
Contribution	Ongoing Class Participation (10%) Seminar discussions	10%	Week 03 Due date: 08 Nov 2025 at 17:00	Ongoing	AI allowed
Outcomes assessed:
Case studies	Critical Analysis of a Case Study (40%) Critical analysis essay	40%	Week 05 Due date: 17 Nov 2025 at 08:59 Closing date: 27 Nov 2025	3500 words/6 weeks	AI allowed
Outcomes assessed:
Written work	ALTERNATIVE Capstone Essay (80%) Critical analysis essay	0%	Week 05 Due date: 17 Nov 2025 at 08:59 Closing date: 27 Nov 2025	7000 words/5 weeks	AI allowed
Outcomes assessed:

Assessment summary

SUMMARY

Reflective Journal (30%)

Overview

This assessment requires students to produce a reflective journal that captures their learning and engagement with each seminar session throughout 10-11 October 2025. The journal should demonstrate critical thinking, personal insight, and an understanding of key concepts and readings discussed in each seminar.

The reflective journal is designed to:

• Encourage deeper engagement with seminar content.

• Promote critical reflection on theoretical frameworks and readings.

• Support the development of analytical and academic writing skills.

Instructions

You must write a reflective entry for each seminar session 10-11 October 2025. Each entry should include the following components:

1. Key Learnings

o What did you learn in the session?

o How did the session contribute to your understanding of digital criminology?

2. Interesting Issues or Points

o What were the most thought-provoking or challenging ideas discussed?

o Were there any debates or discussions that stood out?

3. Reading Reflections

o Which reading was the most helpful or insightful for that session?

o How did it enhance your understanding of the topic?

4. Theoretical Frameworks

o Which theoretical framework(s) did you find most appropriate or useful for analyzing digital criminological issues discussed in the session?

o Justify your choice with reference to the session content and readings.

Formatting Guidelines

• Use clear headings for each seminar session.

• Ensure your writing is concise, coherent, and well-structured.

• All references must be properly cited.

• The total word count must not exceed 2,000 words including footnotes, references, and bibliography.

Assessment Criteria

Your journal will be assessed according to the Reflective Journal Rubric on Canvas.

The assignment will be due on 19 October 2025, at 5pm (Sydney, Australia time). It must be submitted via Canvas. Detailed instructions and marking criteria will be discussed in class and available via the unit Canvas site.

Online Multiple Choice Quiz (10%)

Students must complete the online quiz (10 randomised multiple choice questions; single attempt) by 26 October 2025, 5pm (Sydney, Australia time). Further details will be available on Canvas.

Ongoing Class Participation (10%)

Students must contribute to seminar discussions assessed according to the Class Participation Rubric on Canvas.

Essay Plan (10%)

This Essay Plan is a preparatory task for your final essay (Critical Analysis of a Case Study). It is designed to:

• Support your early engagement with the assessment topic.

• Encourage structured thinking and planning.

• Mitigate risks to assessment integrity by documenting your research and analytical process.

Instructions

Your Essay Plan must include the following components:

1. Title and Topic Selection

• Clearly state the case study and theoretical framework you have selected from the lists provided on Canvas.

• Briefly explain why you chose this combination and its relevance to the Unit of Study.

2. Preliminary Summary of the Case Study

• Provide a concise summary of the case study, including key facts, context, and its connection to digital criminology in Australia.

3. Theoretical Framework Overview

• Outline the key concepts of your chosen criminological theory.

• Explain how this theory will help you interpret and analyse the case study.

4. Proposed Argument or Thesis Statement

• Present a working thesis or central argument for your essay.

• Indicate the main points you intend to explore.

5. Essay Structure

• Provide a brief outline of your planned essay structure (e.g., Introduction, Analysis, Discussion, Conclusion).

• Include bullet points or short descriptions of what each section will cover.

6. Preliminary References

• List at least three academic sources you plan to use.

• Include full citations in your chosen referencing style.

The total word count must not exceed 1,000 words including footnotes, references, and bibliography.

The assignment will be due on 6 November 2025, at 5pm (Sydney, Australia time). It will be released on Canvas on 9 October and must be submitted via Canvas. Detailed instructions and marking criteria will be discussed in class and available via the unit Canvas site.

Critical Analysis of a Case Study (40%)

This assignment requires you to critically analyse a case study related to digital criminal behaviour; OR digital law enforcement or regulation; OR digital criminal court procedure; OR digital punishment. You must apply one criminological theory to interpret and evaluate the case study.

You will be provided with:

• A list of five case studies

• A list of five criminological theoretical frameworks

Both lists will be available on Canvas. You must select ONE case study and ONE theoretical framework to complete your analysis.

This assignment is designed to:

1. Assess your understanding of criminological theories in the context of digitalised criminal justice.

2. Evaluate your ability to apply theory to real-world issues.

3. Develop your academic writing, essay structuring, and argumentation skills.

4. Enhance your independent research and referencing capabilities.

5. Foster critical thinking and analytical reasoning.

Instructions

Part 1: Case study Selection

• Select one case study from the list provided.

• Provide a concise summary of the case study.

• Explain its relevance to the themes and issues explored in this Unit of Study.

Part 2: Theoretical Framework

• Select one criminological theory from the list provided.

• Clearly explain the key concepts and assumptions of the theory.

• Justify why this theory is appropriate for analysing your chosen case study.

Part 3: Research and Analysis

• Apply the selected theoretical framework to critically analyse the case study.

• Demonstrate a deep understanding of both the theory and the real-world context.

• Use scholarly sources to support your analysis and interpretation.

• Consider broader implications for digital criminology and criminal justice in Australia.

Part 4: Discussion and Conclusion

• Summarise your key findings and arguments.

• Reflect on the strengths and limitations of your theoretical approach.

• Offer concluding thoughts on the significance of your analysis.

Formatting and Referencing

• Use a formal academic tone and structure.

• Include a title, introduction, body (with clear headings if appropriate), and conclusion.

• All sources must be properly cited.

The complete essay, with a maximum word count of 3,500 words (including footnotes, references, and bibliography), is due by 17 November 2025, 8:59am (Sydney, Australia time) and must be submitted via Canvas. It will be released on Canvas on 9 October. Detailed instructions and marking criteria will be discussed in class and available via the unit Canvas site.

7,000-word Capstone Essay (80%)

The due date for the Alternative Capstone Essay is 17 November 2025, 8:59am (Sydney, Australia Time). The capstone essay will be based on the student’s choice from the provided 5 case studies and provided 5 criminological theoretical frameworks.

Students will be provided with:

• A list of five case studies

• A list of five criminological theoretical frameworks

Both lists will be available on Canvas. You must select ONE case study and ONE theoretical framework to complete your analysis.

The essay topic must be relevant to the material covered in the unit; should build on work already completed in the degree; may not recycle work already submitted for assessment in the degree, except and only with specific permission and appropriate referencing.

The capstone essay (80%) replaces the essay plan (10%) and the critical analysis of a case study (40%), as well as the reflective journal (30%). Students will still need to complete the ongoing class participation (10%) and the multiple choice quiz (10%).

Students who wish to apply for permission to undertake the Capstone essay must submit the completed form, obtained on application, to the convenor seeking approval for their topic by Monday 13th October, at 5pm.

Word limit penalty: A piece of assessment which exceeds the prescribed word limit will attract a penalty of 10% of the total marks available for the piece of assessment for every 100 words, or part thereof. The total word count for essay and other written assessments will include all footnotes and any bibliography (if required), and exclude acknowledgements of assistance from editors, automated writing tools or generative AI (where use is permitted).

Special consideration: Successful grants of Special Consideration may involve alternative tasks, as appropriate.

Assessment requirements to pass a unit of study: A student must make a genuine attempt at all assessment tasks set out in this Unit of Study in order to obtain a Pass mark and grade (or above); otherwise an Absent Fail grade will be recorded as the student’s result for this Unit of Study.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Work receiving a high distinction grade will generally exhibit the following characteristics: • Completely answers the question. • Contains striking originality of approach or analysis. • Demonstrates exhaustive or innovative research (where independent research required). • Exceptionally well written, structured and expressed. • Is otherwise exceptional in some way.
Distinction	75 - 84	Work receiving a distinction grade will generally exhibit the following characteristics: • Completely answers the question. • Achieves a critical and evaluative approach to the issues. • Content and structure is well organised in support of the argument. • Demonstrates extensive research and analysis to support a well-documented argument. • Generally well expressed and free from errors. • Has a clear structure and is well articulated.
Credit	65 - 74	Work receiving a credit grade will generally exhibit the following characteristics: • Covers main issues fairly well in answering the question. • Contains no significant errors • Demonstrates an attempted critical approach to the issues. • Demonstrates reasonably sound research and analysis in addressing the key issues. • Has a clear structure and reasonably clear expression.
Pass	50 - 64	Work receiving a pass grade will generally exhibit the following characteristics: • Identifies the key issues, but does not follow through with a reasoned argument. • Contains some significant errors. • Displays satisfactory engagement with the key issues. • Offers descriptive summary of material relevant to the question. • Superficial use of material, and may display a tendency to paraphrase. • Demonstrates little evidence of in-depth research or analysis. • Adequate expression. • Overall, demonstrates the minimum level of competence in the assessment and satisfies the requirements to proceed to higher-level studies in the degree or subject area.
Fail	0 - 49	Work may fail for any or all of the following reasons: • Does not answer the question. • Contains significant or numerous errors. • Few or no identifiable arguments. • Content that is inappropriate or irrelevant. • Lack of research or analysis. • Difficult or impossible to understand through poor grammar, expression or structure. • Overall, does not demonstrate the minimum level of competence in the assessment.

For more information see sydney.edu.au/students/guide-to-grades

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 Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including 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:

The late submission of a piece of assessment, without an approved extension, will attract a penalty of 10% of the total marks available for the piece of assessment per 24 hours or part thereof, after the due time on the due date. For example, a submission after the due time specified in the assessment summary but before the same time the following day will attract a 10% penalty.

Academic integrity

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

Our website 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 Academic Honesty Education Module (AHEM) 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 Current Students website for more information on AI in assessments, including 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 simple extension.  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’t 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 special consideration or special arrangements.

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

Using AI responsibly

Co-created with students, AI in Education 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 reflects the University’s 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
Week -02	Introduction: Digital Criminology; Understanding Digital Criminology	Seminar (3 hr)
	Theorising Digital Criminology; Researching Digital Criminology	Seminar (3.5 hr)
	Criminal Behaviour in the Digital Age	Seminar (4.5 hr)
	Victimisation in the Digital Age	Seminar (2 hr)
Week 03	Digitalised Policing; Regulation	Seminar (4.5 hr)
	Peer Review Session; social justice in digital society	Workshop (2 hr)
	Digitalised Justice	Seminar (3 hr)
	Digitalising punishment and supervision	Seminar (1.5 hr)
	Workshopping students' individual case studies / Conclusion	Workshop (2 hr)

Attendance and class requirements

Attendance: All students are required to attend ALL classes to satisfy the pass requirements for this unit. Failure to meet this requirement will result in an Absent Fail grade being recorded as the student’s result for this unit (see The University of Sydney Law School Resolutions: Coursework Awards cl 11).

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.

Required readings

Reading materials will be provided on Canvas.

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.

Outcomes
Graduate qualities

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

LO1. Analyse the evolving landscape of digital crimes and victimisation, identifying emerging trends and patterns.
LO2. Evaluate the effectiveness of current policing, legal and policy responses to digital crime, considering their impact on stakeholders.
LO3. Apply relevant criminological theories to complex scenarios involving digitalised criminal justice, justifying conclusions with sound, logically structured arguments.
LO4. Propose evidence-based recommendations for regulatory or legislative reforms addressing challenges posed by digitalised criminal justice, considering ethical implications.
LO5. Apply relevant research methodologies to investigate a topic within digital criminology and communicate findings effectively.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney 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’s 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

Responding to student feedback

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

Assessments have been re-designed to encourage greater in-person class participation and to minimise academic integrity issues.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

LAWS6358: Digital Criminology: Tech and Crime

Intensive October - November, 2025 [Block mode] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links