The need for specialised skills in Services Marketing has grown in recognition of the important role of services in advanced economies. This course addresses the essential nature of services and the role of service quality. Employees' role in service delivery and the emotional load for service workers form an important focus of the course. Service sector firms face increased competition and more demanding customers.  Marketers need to develop a distinct set of competencies to design, manage and evaluate the processes and performances that comprise the service offering.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. define, explain and illustrate services marketing concepts, including the services marketing mix, and their application to profit oriented and non-profit service delivery
2. synthesise and relate theory from a range of academic sources to services marketing conceptual frameworks
3. consider the key elements underpinning the design, planning and implementation of services marketing plans and strategies
4. critically evaluate case studies, identifying and analysing problems and then making recommendations for practical implementation
5. communicate effectively, individually and in teams, in oral presentation and written forms using the concepts and terminology of the marketing discipline.

Research-Led Teaching

MKTG2023 is taught as a living conversation with the global services research community. Every week, students engage directly with cutting-edge scholarship from the Journal of Service Research, Journal of Services Marketing, Journal of Service Management, and Journal of Service Theory and Practice, translating peer-reviewed evidence into practical service design, AI-augmented audits, and consulting-style recommendations. Foundational frameworks such as the Gaps Model, Servuction System, Service-Dominant Logic, Service Blueprinting, and Servicescape, are not delivered as settled doctrine but interrogated, replicated, and stress-tested against real customer data and contemporary service phenomena. Through structured reading critiques, open-data analytics, and a simulated consulting capstone, students learn to think like service researchers: forming evidence-based judgements, questioning AI-generated insights, and generating new knowledge about how value is created in an increasingly technology-mediated economy.

Field Trips

There are no field trips in this course.

Additional Course Costs

There are no additional costs expected in this course.

Required Resources

Services Marketing: People, Technology, Strategy (2022) (9 ed.) by Jochen Wirtz & Crhistopher Lovelock , New Jersey: World Scientific

Students can get access to an Online copy of the book through the library of the Australian National University.

Chapters and journal articles prescribed for reading will be made available on Canvas through Readings.

A PDF copy is accessible through the author's ResearchGate account at:

Services_Marketing_People_Technology_Strategy_9th_edition

Recommended Resources

Whether you are on campus or studying online, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

The lectures include insights from various marketing journals, industry magazines, and business media. The references will be on the lecture slides and students are encouraged toaccess those sources for full details. The key sources for such insights include the following.

Academic journals that are particularly relevant include:

Journal of Services Marketing

Journal of Service Management

Journal of Service Research

Journal of Service Theory and Practice (previously Managing Service Quality)

Academic journals can be accessed via the ANU library:

https://anulib-anu-edu-au.virtual.anu.edu.au/

For contemporary issues and examples, the following sources are particularly relevant;

ABC Business News: https://www.abc.net.au/news/business/

Harvard Business Review: https://hbr.org/

Marketing Week:https://www.marketingweek.com/

The Conversation: https://theconversation.com/au/business

Other resources, such as video clips, web sites will be made available on the course Canvas site.

Staff Feedback

Students will be given feedback in the following forms in this course:

• written comments
• verbal comments
• feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Other Information

AI in MKTG2023: Where, How, and How to Disclose It

Seminars in MKTG2023 are run as professional problem-solving laboratories, not passive lectures. Building on pre-recorded micro-lectures and assigned readings (book chapters and academic articles), students debate case studies, role-play service encounters, and prototype service innovations in real time, with AI embedded as both a learning tool and an object of critical inquiry.

Hands-on AI work spans the whole semester including generative AI for service-script and persona design, and predictive analytics for forecasting customer behaviour and churn. This practical work is anchored in the frontiers of contemporary service practice, with AI-mediated service encounters, omnichannel customer journeys, platform and sharing-economy models, social-media-driven service recovery, and the ethical, sustainability, and inclusivity dimensions of service design equipping students with both the theoretical fluency to evaluate emerging service phenomena against peer-reviewed evidence and the practical proficiency to translate insight into actionable strategy.

Critically, students learn not just to use AI but to interrogate it by auditing outputs for bias, hallucination, ethical risk, and customer-trust implications, consistent with ANU's Academic Integrity Rule 2021.

AI Use Disclosure is an integral course requirement. Every assessment in MKTG2023 is classified under a Green / Amber / Red traffic-light system:

Green - AI is fully permitted (e.g., Task 2 AI-augmented service audit)

Amber - AI is permitted only for specified purposes such as ideation, structuring, or refinement.

Red - AI is not permitted at the point of assessment (e.g., Task 5 live oral examination)

For every Green and Amber assessment task, students must submit the RSM Generative AI Use Disclosure Statement as a compulsory appendix, identifying the tools used, the areas of assistance, and how outputs were verified. Green tasks also require a Progression of Prompts log, briefly documenting prompt iterations and refinements, together with a short note explaining how AI outputs were checked for accuracy, bias, hallucination, and relevance before inclusion in the submission.

For group or pair tasks, each member must additionally provide a signed statement of contribution agreed to by all members, confirming individual roles and contributions. These requirements are designed to make transparent, critical, and ethical AI use an integral part of professional marketing practice.

Class Schedule

Week/Session	Summary of Activities	Assessment
1	Foundations of Services Marketing: From products to service; product as service; service productisation Pre-Class (Flipped): Pre-recorded lectureCourse Outline and Learning Outcomes (Old and Revised to align with AQF level 7) ExpectationsAn explanation of the evidence-based teaching and learning approach within the flipped-classroom mode (pre-recorded lecture + face-to-face tutorial)The role of critical and analytical thinking and reflections in learning and assignment tasks (essays)Use of AI and AI disclosure briefing; Peer review and reflectionsAssessment task 1 briefing	Read Chapter 1 Creating Value in the service economy - Wirtz & LovelockRead: Vargo, S. L., & Lusch, R. F. (2016). Institutions and axioms: an extension and update of service-dominant logic. Journal of the Academy of marketing Science, 44(1), 5-23. Class survey - class expectations
2	Value Propositions, Service consumer behaviour, Positioning Positioning WorkshopTask 2 briefing	Read Wirtz & Lovelock Chapter 2 Understanding Service ConsumersRead Wirtz & Lovelock Chaptger 3 Positioning Services in Competitive Markets Read Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of marketing, 80(6), 69-96.Task 1 Preparation
3	Service products, brands & Tech-mediated services [AI-enabled personalisation of services, Robots in service industry and for customer service] Prompt-Engineering for service scripts	Read Wirtz & Lovelock Chapter 4 Developing Serice Products and BrandsRead Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the academy of marketing science, 49(1), 30-50.Assessment Task 1 (Critique #1) Due Wednesday 12.08.2026
4	Distributing services through physical, digital & platform channelsBlueprint Project Workshop (Task 3)	Read Wirtz & Lovelock Chapter 5 Distributing Services through physicial and electronic brands Read Eckhardt, G. M., Houston, M. B., Jiang, B., Lamberton, C., Rindfleisch, A., & Zervas, G. (2019). Marketing in the sharing economy. Journal of marketing, 83(5), 5-27.
5	Service Pricing & Revenue Management Fundamentals of service pricing; Market segmentation and price discrimination; Ethics, Fairness and StrategyPrice Simulation	Read Wirtz & Lovelock Chapter 6 Service Pricing and Revenue ManagementAssessment Task 1 (Critique #2) Due Wednesday 26.08.2026Wirtz, J. & Zeithaml, V. (2018). Cost-effective service excellence. Journal of the Academy of Marketing Science, 46(1), 59–80.
6	Service marketing communications in omnichannel environments"Simulated Client Meeting" #1 for Task 4 - Capstone (Pair or Team)	Read Wirtz & Lovelock Read Chapter 7 Service Marketing Communications Read De Keyser, A., Köcher, S., Alkire, L., Verbeeck, C., & Kandampully, J. (2019). Frontline service technology infusion: conceptual archetypes and future research directions. Journal of service management, 30(1), 156-183.
7	Servicescape- Physical & Digital Environment and service design Intersection of servicescape and platformizationPublic-service field observation briefing	Read Wirtz & Lovelock Chapter 10 Crafting the Service EnvironmentRead Bitner, M. J. (1992). Servicescapes: The impact of physical surroundings on customers and employees. Journal of marketing, 56(2), 57-71.Assessment Task 2 AI service audit (with AI Use Disclosure Statement + Prompt Log appendix - Due Friday 25.09.2026
8	Service Processes, Queues & AI service Agents Lab: Auditing a real chatbot service journey	Read Wirtz & Lovelock Chatper 8 Designing Service ProcessesRead Wirtz & Lovelock Chapter 9 Balancing Demand and CapacityRead Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of service management, 29(5), 907-931.Assessment Task 1 (Critique #3) Due Wednesday 30.09.2026
9	Managing people for service advantageHR- MarketingRole Play: Service recovery dialogue; "Simulated Client Meeting" #2 for Task 4 - Capstone (Pair or Team)	Read Wirtz & Lovelock Chapter 11 Managing people for service advantageHartline, M. D., & Ferrell, O. C. (1996). The management of customer-contact service employees: an empirical investigation. Journal of marketing, 60(4), 52-70.
10	Customer Relationships and Loyalty	Read Wirtz & Lovelock Chapter 12 Managing Relationships and Building LoyaltyRead Kumar, V., & Reinartz, W. (2016). Creating enduring customer value. Journal of marketing, 80(6), 36-68.Assessment Task 3 Service Blueprint + Servicescape Lab (with AI Use Disclosure Statement) Due Friday 16.10.2026
11	Service recovery & complaint management"Simulated Client Meeting" #3 for Task 4 - Capstone (Pair or Team)	Read Wirtz & Lovelock Chapter 13 Complaint Handling and Service RecoveryRead Van Vaerenbergh, Y., Varga, D., De Keyser, A., & Orsingher, C. (2019). The service recovery journey: conceptualization, integration, and directions for future research. Journal of service research, 22(2), 103-119.
12	Service excellence, sustainability & the future of service	Read Wirtz & Lovelock Part V - Striving for Service Excellence (both chapters 14 - improving service quailty and productivity & Chapter 15 - Building a world-class service organization)Field, J. M., Fotheringham, D., Subramony, M., Gustafsson, A., Ostrom, A. L., Lemon, K. N., ... & McColl-Kennedy, J. R. (2021). Service research priorities: designing sustainable service ecosystems. Journal of Service Research, 24(4), 462-479.Assessment Task 4 Simulated Consulting Capstone Due Friday 30.10.2026 (with Consolidated AI Use Disclosure Statement + Individual statement of contribution)Each team must submit as an appendix a consolidated RSM Generative AI Use Disclosure Statement together with an individual statement of contribution to the task by each member, and agreed upon by all group members or pair. Assessment Task 5 Peer Review Due Friday 30.10.2026
13	End of Semester Examination Period	Task 6 Group Presentation Community Service - Improvement Sprint+ Individual Oral DefensePresentation block and slots to be announced for booking in due course for groups choosing to present in either 'Week 13' or 'Week 14'Submit a consolidated RSM Generative AI Use Disclosure Statement together with a statement of contribution to the task by each member of the group as an appendix.

Tutorial Registration

Classes in this course will be run in a flipped mode with 1 hour pre-recorded lecture followed by a weekly two-hour tutorial session in class. Pre-recorded lectures will be available online on Sunday preceding each weekly tutorial/seminar

Interactive 2-hour tutorial activities will begin in Week 1

Students must watch/listen to pre-recorded lectures and complete assigned readings (book chapter(s) and academic article) before attending seminars

Tutorial Registration opens in MyTimetable two weeks before semester starts

Tutorials will be conducted in-person ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

Assessment Summary

Assessment task	Value	Due Date	Return of assessment	Learning Outcomes
Evidence-Based reading critiques (Individual - 20%)	20 %	*	*	2,4
AI-augmented service audit (Individual - 20%)	20 %	25/09/2026	16/10/2026	3,4
Service Blueprint + Servicescape Lab (Group - 15%)	15 %	16/10/2026	30/10/2026	1,3,5
Simulated Consulting Capstone (Group - 20%)	20 %	30/10/2026	13/11/2026	1,2,3,4,5
Peer Review Assessment (Individual - 5%)	5 %	30/10/2026	13/11/2026	5
Community Service - Improvement Sprint - Group Presentation & Oral Defence (20%)	20 %	*	09/12/2026	1,2,3,4,5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details

Policies

ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Canvas’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Participation

Attendance at tutorials, while not compulsory, is expected in line with the “Code of Practice for Teaching and Learning” clause 2 paragraph (b).

This course is scheduled to be run in face-to-face mode in interactive tutorials.

In-person interactive tutorials will be conducted on a weekly basis where students will be required to fully participate and engage in discussions of lectures and reading materials.

Engagement with the course materials is a key skill in marketing, and as such, is assessed as part of Learning Outcomes 2, 4, 5 and presented as an opportunity for students to learn from their peers, engage with diverse viewpoints, and present their own thoughts to an audience of their peers in a range of different class opportunities.

Examination(s)

There is no final examination in this course (See table of assessment tasks above).

However, a group presentation followed by an individual oral defence examination will take place during examination weeks. Booking of slots will be made available and communicated to students in due course.

Assessment Task 1

Evidence-Based reading critiques (Individual - 20%)

Type: Individual

AOL (Assurance of Learning): CLOs 2,4

Weight: 20%

Three × 500-word structured critiques of peer-reviewed articles from Journal of Service Research, Journal of Services Marketing, Journal of Service Management, or Journal of Service Theory and Practice. Each critique must (a) summarise the theoretical contribution, (b) appraise the methodology, (c) evaluate generalisability to Australian service contexts.

Any portion exceeding the word limit will not be read or assessed.

Due: 23:59 Wednesday of Wks 3, 5, 8 via Turnitin.

Return of Assessment: within 15 business days, except the first, which will be returned by 31st August 2026.

AI Disclosure note (Green): Generative AI use is permitted and expected for this assessment under a Green traffic-light rating. Students must submit the RSM Generative AI Use Disclosure Statement as a compulsory appendix, identifying the tool(s) used, the purpose of use, and how outputs were verified and integrated into the final critique. A Progression of Prompts log must also be attached where AI was used to support brainstorming, structuring, drafting, or refinement. The disclosure is a compulsory submission component. The disclosure trains students in the transparent, critical, and ethical use of AI expected of evidence-based service marketing professionals.

Rubrics of assessment will be made available on the course Canvas site at least two weeks before the assessment due date.

Assessment Task 2

AI-augmented service audit (Individual - 20%)

Type: Individual

AOL: CLOs 3 & 4

Weight: 20%

 Max 1500-word report.

Students select a publicly reviewed service brand, extract =200 customer reviews from Trustpilot, Google Reviews, ProductReview.com.au or App Store/Play Store, run sentiment + topic analysis using a tool of choice (Python NLTK, Voyant, MonkeyLearn, or GenAI prompts), benchmark AI-generated insights against their own human interpretation, and propose evidence-based service improvements anchored in =5 peer-reviewed sources.

Any portion exceeding the word limit will not be read or assessed.

Due: 23.59 Friday Week 7 via Turnitin. 

AI Disclosure note (Green): Because generative AI is the analytical engine of this audit, this assessment carries a Green trafiic-light rating. Students must complete and submit the RSM Generative AI Use Disclosure Statement and additionally attach a "Progression of Prompts" log as an appendix, demonstrating iterative refinement and critical veriification of AI-generated outputs for accuracy, bias, and hallucination. The disclosure and log are compulsory submission components: they are how professional service marketers evidence responsible AI practice.

Rubrics of assessment will be made available on the course Canvas site at least two weeks before the assessment due date.

Assessment Task 3

Service Blueprint + Servicescape Lab (Group - 15%)

Type: Group (4 students)

AOL: CLOs 1, 3, 5

Weight: 15%

10-minute narrated PowerPoint or Zoom recording plus a handout, presenting

(a) a service blueprint of a real, publicly accessible service provider,

(b) a visual analysis of its physical and digital servicescape based on a mandatory 2-hour field observation,

(c) identification of fail-points and improvement recommendations grounded in the literature.

Any portion of the video exceeding the running time limit will not be viewed or assessed.

Due: 23:59 Friday Week 10 via Canvas. 

AI Disclosure note (Green): Generative AI use is permitted and expected for this assessment under a Green traffic-light rating. Students must submit the RSM Generative AI Use Disclosure Statement as a compulsory appendix, identifying the tool(s) used, the purpose of use, and how outputs were verified and integrated into the blueprint, servicescape analysis, and recommendations. A Progression of Prompts log must also be attached where AI was used at any stage of the task.

The disclosure is a compulsory submission component and must include a statement of contribution by each member of the group, agreed and signed by all members.

Rubrics of assessment will be made available on the course Canvas site at least two weeks before the assessment due date.

Assessment Task 4

Simulated Consulting Capstone (Group - 20%)

Type: Pair (2 students) OR team of maximum 4 students

AOL: CLOs 1,2,3,4,5

Weight: 20% (15% report + 5% reflective post-review)

A 3000-word consulting report (excluding title page, TOC, references, appendix) responding to a convener-authored simulated client brief that mirrors a real services marketing problem. Each team participates in three scheduled "client meetings" (Wks 6, 9, 11) with the convener and a colleague playing executive stakeholders and/or industry partner.

The report must (a) frame the service problem, (b) analyse it using =10 peer-reviewed sources, (c) propose evidence-based recommendations, (d) include an individual 500-word reflective post-review per student.

Any portion exceeding the word limit will not be read or assessed.

Due: 23:59 Friday, Week 12 via Turnitin. 

AI Disclosure note (Green): Generative AI use is permitted and expected for this assessment under a Green traffic-light rating. Students must submit the RSM Generative AI Use Disclosure Statement as a compulsory appendix, identifying the tool(s) used, the purpose of use, and how outputs were verified and integrated into the consulting report and reflective post-review. A Progression of Prompts log must also be attached where AI was used in problem framing, analysis, drafting, critique, or refinement.

Each individual reflective post-review must include a personal disclosure declaration. These disclosures are compulsory submission components: they are how aspiring consultants evidence the professional, transparent, and accountable use of AI that contemporary service organisations expect.

Rubrics of assessment will be made available on the course Canvas site at least two weeks before the assessment due date.

Assessment Task 5

Peer Review Assessment (Individual - 5%)

Type: Individual [related to group task 4]

AOL: CLO 5

Weight: 5%

Rubrics for peer evaluation will be made available on the course Canvas site at least two weeks before the assessment due date.

Due: 22:00 Friday, Week 12 via Canvas

Assessment Task 6

Community Service - Improvement Sprint - Group Presentation & Oral Defence (20%)

Type: Group [same membership as Task 3] Presentation + Individual oral defense of group project

AOL: CLOs 1,2,3,4,5

Weight: 20% [10% group presentation + 10% Individual oral defense]

Due: Week 13 or 14 (other day of presentation may be required to accommodate presentation and oral defence)

Booking for presentation and oral defence will be provided in Week 9. Group Presentation and Oral Defense will be video recorded, which will enable later validation and verification of assessment if required (in accordance with point 7 in the ANU Student Assessment (Coursework) policy).

Group presentation (10%): Each team (same membership as Task 3) delivers a 10-minute in-class presentation in either week 13 or week 14 of a small service-improvement project conducted with a non-commercial community entity (e.g., an ANU club, student-led service, or volunteer-run community service).

The convenor will stop the presentation once it reaches the time limit

Oral Defense (10%): Immediately following the group presentation, each individual group member undergoes 2-3 minutes of individual viva-style questioning by the convener.

Each student's mark is awarded solely on the basis of their individual oral responses, eliminating free-rider effects and providing direct individual assurance of CLO5

AI Disclosure note for group presentation (Green):This assessment carries a Green traffic-light rating for generative AI use. Students may use approved AI tools in the development of their work, provided that they submit the RSM Generative AI Use Disclosure Statement as a compulsory appendix, identifying the tools used, the areas of assistance, and how the outputs were verified and incorporated into the final submission. Because this is a Green task, students must also attach a Progression of Prompts log that briefly records the prompts, iterations, and refinements used, together with a short note explaining how AI outputs were checked for accuracy, bias, hallucination, and relevance before inclusion in the submission. Because this task is a group presentation, each member must provide a signed statement of contribution, agreed to and endorsed by all other members, confirming the nature and extent of their individual contribution to the final work.

AI Disclosure note for oral defense (Red): This assessment carries a Red traffic-light rating during the live oral examination component: no AI assistance of any kind is permitted at the point of assessment. Standard ANU oral-assessment integrity protocols apply. No AI Use Disclosure Statement is required because the assessment is delivered live.

Rubrics of assessment for the group presentation and the oral defense will be made available on the course Canvas site at least two weeks before the assessment due date.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.

 

The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Individual assessment tasks may or may not allow for late submission. Policy regarding late submission is detailed below:

• Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.
• Late submission permitted. Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

Assignments related to Tasks 1 and 2 will be returned within a maximum of 15 days after the due date.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

Not allowed

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students