Outline

Overview

In today's rapidly evolving environments, leaders must navigate the impacts and implications of disruptive technologies while anticipating future challenges and opportunities. This unit equips students with the knowledge and tools to understand technological disruption, particularly from Artificial Intelligence and generative AI, and their implications for business and society. Students learn to apply strategic foresight methods, moving beyond simple prediction to engage productively with future possibilities. Topics include the nature of disruptive technologies, AI fundamentals and applications, megatrends and societal shifts, forecasting, scenario thinking, and identifying weak signals in uncertain environments. Through a real-world case study, students develop practical skills in analysing emerging technology, disruptive forces, and crafting forward-looking strategies. Learning involves discussion, hands-on exercises, reflective thinking, and a collaborative project. This unit is essential for students seeking to drive innovation, manage uncertainty, and position themselves at the forefront of technological and societal change, emphasising preparedness and sense-making over predicting specific future outcomes.

Unit details and rules

Academic unit	Business Information Systems
Credit points	6
Prerequisites ?	None
Corequisites ?	DMBA6001
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Katina Michael, k.michael@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Portfolio or journal	Stakeholder Needs and Expectations In this assessment the student translates stakeholder expectations into system requirements.	30%	Week 05 Due date: 29 Mar 2026 at 23:59 Closing date: 08 Apr 2026	1500 Words	AI allowed
Outcomes assessed:
Case studies	Integrated Technology Impact Assessment In this case study the student explores social, economic, legal, ethical, operational impacts of an emerging technology in a given project context.	40%	Week 08 Due date: 26 Apr 2026 at 23:59 Closing date: 06 May 2026	4000 words	AI allowed
Outcomes assessed:
Research analysis	Socio-Technical Futures and Foresight Analysis In this assessment the student develops coherent scenarios to 2050 with respect to socio-technical futures in their chosen project context.	30%	Week 13 Due date: 31 May 2026 at 23:59 Closing date: 10 Jun 2026	1500 words	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Stakeholder Needs and Expectations: . Students are to submit a sample of data gathered in the form of 10 diverse sources evidence, inclusive of newspaper articles, government agency sources (at federal, state, local levels), industry perspectives (at the macro, meso and micro levels), third sector voices (e.g., citizen, advocacy, NfP), and a general landscape analysis to build their portfolio. Their portfolio must be accompanied by metadata for each sample, and a brief narrative of why this data is relevant to the project. A reference list of sources must be included using a referencing tool of choice.

Integrated Technology Impact Assessment: Students are to self-organise into groups and to choose a critical emerging technology to study that is relevant to their given project context. Students are to determine what the main criteria and dimensions of their integrated technology impact assessment are, and to carry out the assessment for a given technological application for a given project. The assumption made in this assignment is that the technology impact assessment is taking place at the very outset of the diffusion of an innovation, known as the pre-deployment phase. Students need to submit their assessment in 4 parts, roughly 1000 words per student, in one group report clearly identifying who did which section. The technology impact assessment requires diverse sources of evidence, and 10 key references for each of the sub-sections carried out by each student. The assessment requires tables, diagrams, and other exhibits where available.

Socio-Technical Futures and Foresight Analysis: This is a written assignment that provides the impetus for asking what the socio-technical future of the given project context looks likes into the long-term (i.e., 2050). Students can choose 4 foresight analysis techniques, including scenarios, to depict their possible alternate futures. The expectation in this individual assignment is for students to consider ways to capture foresight in two-dimensional matrices, weak signals and wild cards, trends and emerging issues analysis, backcasting/futurecasting, and other roadmapping elements of their choice. While the work is predominantly predictive in nature, 20 sources of evidence for these predictions need to be included. Please include tables and diagrams and/or exhibits as required.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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:

Late penalties apply in accordance with policy.

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 01	Introduction and overview	Self-directed learning (2 hr)
Week 01	Introduction and overview	Workshop (2 hr)
Week 02	Disruption	Self-directed learning (2 hr)
Week 02	Disruption	Workshop (2 hr)
Week 03	Introduction to AI	Self-directed learning (2 hr)
Week 03	Introduction to AI	Workshop (2 hr)
Week 04	AI capabilities	Self-directed learning (2 hr)
Week 04	AI capabilities	Workshop (2 hr)
Week 05	Implications of AI	Self-directed learning (2 hr)
Week 05	Implications of AI	Workshop (2 hr)
Week 06	Introduction to futures thinking	Self-directed learning (2 hr)
Week 06	Introduction to futures thinking	Workshop (2 hr)
Week 07	Megatrends	Self-directed learning (2 hr)
Week 07	Megatrends	Workshop (2 hr)
Week 08	Scenario thinking	Self-directed learning (2 hr)
Week 08	Scenario thinking	Workshop (2 hr)
Week 09	Signals and strategic learning options	Self-directed learning (2 hr)
Week 09	Signals and strategic learning options	Workshop (2 hr)
Week 10	Group project preparations	Self-directed learning (2 hr)
Week 10	Group project presentations	Workshop (2 hr)

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.

Outcomes
Graduate qualities

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

LO1. explain the fundamental concepts and capabilities of AI and generative AI to illustrate their potential impact on business and society
LO2. describe key principles and methodologies of futures thinking to grasp their relevance in strategic planning
LO3. apply knowledge of AI capabilities to assess potential ethical and strategic challenges for businesses in different industry contexts
LO4. apply strategic foresight techniques to business cases in order to anticipate potential future challenges and opportunities
LO5. analyse the nature of disruptive technologies, in particular AI, to identify their potential effects on existing business models and industry structures
LO6. evaluate weak signals and societal shifts to propose strategic learning options that enhance organisational adaptability in uncertain environments.

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.

This unit is being offered for the first time in 2026.

Disclaimer

Important: the University of Sydney 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 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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

DMBA6002: Emerging Technology, Disruption and Foresight

Semester 1, 2026 [Online] - 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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links