General Information
Leading with AI equips students with the understanding, and critical capacities necessary to lead with confidence in a world shaped by AI. Students will explore foundation models, bots, and agents, and develop advanced prompt engineering skills through hands-on projects and creative exploration. More than just a technical overview, the subject empowers students to build their own AI toolkits and reflect on how AI can augment their personal agency, creativity, and decision-making.
Through simulated scenarios, students will manage a “personal AI workforce,” applying AI tools to practical tasks such as communication, research, time management, and content creation. They will examine the social, ethical, and political implications of AI disruption across domains including business, education, healthcare, and media. Emphasis is placed on the role of human judgment, the limits of automation, and how AI reshapes identity, relationships, and responsibility.
Future-focused assessments challenge students to critically evaluate AI developments, design ethical implementation strategies, and envision how intelligent systems could transform their chosen field. By the end of the subject, students will be equipped with practical skills, critical insight, and digital agency to not only keep pace with AI, but to lead its responsible and innovative use across industries, communities, and all areas of life and work.
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Details
Academic unit: Transformation CoLab Subject code: COLB11-103 Subject title: Leading with AI Subject level: Undergraduate Semester/Year: January 2026 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Forum: x12 (Total hours: 24) - Weekly forum
- Tutorial: x12 (Total hours: 12) - Weekly tutorial.
- Personal Study Hours: x12 (Total hours: 84) - Recommended Study Hours
Attendance and learning activities: Students are encouraged to attend all subject sessions in order to contribute to the collective experiences that promote engaged, active and authentic learning. -
Resources
Prescribed resources: Books
- Arvind Narayanan,Sayash Kapoor (2024). AI Snake Oil. n/a, Princeton University Press 360
iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.
Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
Class recordings: The majority of this subject's classes will not be recorded due to one of the reasons outlined in the Recording policy.
Students are encouraged to attend all sessions as these recordings will not be available for revision purposes.
For further information please contact the subject coordinator.
| Academic unit: | Transformation CoLab |
|---|---|
| Subject code: | COLB11-103 |
| Subject title: | Leading with AI |
| Subject level: | Undergraduate |
| Semester/Year: | January 2026 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
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| Attendance and learning activities: | Students are encouraged to attend all subject sessions in order to contribute to the collective experiences that promote engaged, active and authentic learning. |
| Prescribed resources: | Books
|
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
| Class recordings: | The majority of this subject's classes will not be recorded due to one of the reasons outlined in the Recording policy. Students are encouraged to attend all sessions as these recordings will not be available for revision purposes. For further information please contact the subject coordinator. |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Demonstrate foundational knowledge of key AI technologies and explain their role in contemporary digital systems.
- Use a range of generative AI tools and prompt strategies to complete real-world tasks.
- Critically evaluate the ethical, social, and political implications of AI, with reference to current events, global challenges, and diverse spheres of influence.
- Design and manage a personalised AI toolkit or “digital workforce” demonstrating awareness of digital agency and responsible use.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Portfolio This assessment introduces students to the practical use of AI tools by simulating a "personal AI workforce." Over four weeks (Weeks 4 to 7), students will be presented with realistic challenges involving communication, research, content creation, and personal productivity. Students will choose an appropriate AI tool, use it to complete the task, and reflect on the experience. 40.00% Week 4 1, 2 Written Report In this capstone assessment, students will choose a real-world context relevant to their field (e.g. business, education, media, healthcare, or personal life) and develop a strategy to implement AI tools responsibly and effectively. This includes identifying a specific problem or opportunity, evaluating available tools, and proposing an ethical and practical AI implementation plan. 40.00% Week 10 2, 3, 4 Oral Pitch In this capstone assessment, students will choose a real-world context relevant to their field (e.g. business, education, media, healthcare, or personal life) and develop a strategy to implement AI tools responsibly and effectively. This includes identifying a specific problem or opportunity, evaluating available tools, and proposing an ethical and practical AI implementation plan. 20.00% Week 12 3, 4 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Portfolio | This assessment introduces students to the practical use of AI tools by simulating a "personal AI workforce." Over four weeks (Weeks 4 to 7), students will be presented with realistic challenges involving communication, research, content creation, and personal productivity. Students will choose an appropriate AI tool, use it to complete the task, and reflect on the experience. | 40.00% | Week 4 | 1, 2 |
| Written Report | In this capstone assessment, students will choose a real-world context relevant to their field (e.g. business, education, media, healthcare, or personal life) and develop a strategy to implement AI tools responsibly and effectively. This includes identifying a specific problem or opportunity, evaluating available tools, and proposing an ethical and practical AI implementation plan. | 40.00% | Week 10 | 2, 3, 4 |
| Oral Pitch | In this capstone assessment, students will choose a real-world context relevant to their field (e.g. business, education, media, healthcare, or personal life) and develop a strategy to implement AI tools responsibly and effectively. This includes identifying a specific problem or opportunity, evaluating available tools, and proposing an ethical and practical AI implementation plan. | 20.00% | Week 12 | 3, 4 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
Students are expected to attempt all items of assessment in this subject. Students may be asked to respond to questions from the subject coordinator regarding the content of their assessments. Students are expected to keep evidence of drafting and research. For the purposes of quality assurance, Bond University has commenced an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
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Week 1: Understanding the Moment: Why AI, Why Now?
The course begins by setting the stage for the AI moment we’re living through. Students are introduced to the concept of artificial intelligence as a general-purpose technology and explore recent breakthroughs, including foundation models, bots, and agents. This week invites students to reflect on how AI is already embedded in daily life and the implications of accelerating change. Emphasis is placed on digital agency and how individuals can begin leading rather than reacting.
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Week 2: What Is It: Building Your AI Toolkit
Students explore how generative AI works in practice, including an introduction to foundation models (e.g., GPT-4, Claude, Gemini), agents, and AI-driven tools. Through hands-on exploration and prompt experimentation, students begin to build a personal AI toolkit for communication, research, and productivity. This week introduces the “invisible what”, technical grounding through applied use rather than abstract theory.
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Week 3: Advanced Prompt Engineering and Task Design
This week deepens students’ practical skills in prompt engineering. Students test strategies for instructing AI tools, chaining prompts, and iterating outputs across tasks such as summarisation, ideation, and drafting. Emphasis is placed on understanding how prompt specificity influences output quality and how different models respond to variation.
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Week 4: Personal AI Workforce: Delegation, Automation, and Friction
Students are introduced to the idea of managing a personal AI “workforce.” Through simulations and roleplay, they experiment with using different AI tools to complete a coordinated set of tasks (e.g., project planning, meeting prep, content creation). Reflection activities explore what tasks are best suited to automation, and how friction or bias can emerge in delegation.
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Week 5: Identity and Agency: Who Are You With AI?
This week turns inward, asking students to reflect on how AI influences their sense of identity, judgement, and personal agency. Students explore scenarios involving AI-generated content, digital doubles, and decision support tools. Class discussion centres on how AI can both extend and diminish personal responsibility, and how to lead with intentionality in its use.
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Week 6: AI in Society: Education, Work, and the Human-AI Divide
Students analyse case studies of AI implementation in different sectors such as education, business, and creative industries. They explore the tension between augmentation and replacement and consider what it means to lead change in systems where AI challenges traditional human roles. Ethical risks and unintended consequences are a key theme.
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Week 7: Ethics, Power, and Pitfalls. What AI Can’t Do
Building on last week, this session focuses on the ethical, legal, and political implications of AI adoption. Topics include misinformation, surveillance, algorithmic bias, and transparency. Students evaluate popular myths around AI, applying a critical lens to distinguish hype from capability. The concept of “AI snake oil” is introduced and debated.
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Week 8: AI Disruption Across Global Domains
Students analyse how AI is reshaping different global domains: religion, governance, healthcare, media, and conflict. Through group presentations, they investigate domain-specific disruptions, evaluating both opportunity and risk. This week highlights the need for future leaders to engage with AI at the system level.
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Week 9: AI Strategy and Change Leadership: Navigating Uncertainty
This week focuses on how to lead AI-driven change in real-world settings. Students examine the roles of strategic foresight, scenario planning, and adaptive leadership in an environment where AI capabilities are evolving faster than policies, norms, or infrastructure. They will also reflect on the psychological and organisational resistance to AI adoption, and develop communication approaches to foster trust, uptake, and human-centred integration. This session reinforces the idea that responsible leadership requires not just technical or ethical knowledge, but the ability to act amid complexity.
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Week 10: Responsible Implementation: Ethics Meets Action
This week turns from analysis to strategy. Students explore frameworks for ethical implementation and governance of AI in organisational or community contexts. Topics include policy design, risk mitigation, consent, transparency, and stakeholder engagement. Students begin shaping ideas for their final assessment.
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Week 11: Designing for Your Domain, Strategy Workshops
Students workshop ideas for how AI can be applied in their chosen field, using a design thinking approach. They evaluate tools, design ethical use strategies, and consider how AI might augment their future career path or organisation. Drafts of the final assessment are peer-reviewed in class.
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Week 12: Leading with AI, Project Showcase and Reflection
Students present their final projects, reflecting on what they’ve learned about AI, agency, and leadership. The session includes structured peer feedback, wrap-up reflections, and a forward-looking discussion: how do we stay ahead in a world where AI evolves faster than curricula?