IFN521 Trust and Artificial Intelligence
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | IFN521 |
|---|---|
| Prerequisite(s): | (192cps in SV03 or SV04 or IV04 or IV05 or MV05 or MV06 or BV06 or BV07 or EV08 or EV07) OR (admissions into IV53 or IV57 or IV54 or IV59 or IV55 or IV52 or IV56 or IV51 or IV58 or IV60) OR (admission into IN14 or IN17 or IN23 or IN20 or IN19 or IN30 or IN26 or IN27 or IN31 or BS11 or DE99). |
| Equivalent(s): | IFQ521 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $1,164 |
| Domestic tuition unit fee | $3,816 |
| International unit fee | $5,352 |
Unit Outline: Semester 1 2025, Gardens Point, Internal
| Unit code: | IFN521 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (192cps in SV03 or SV04 or IV04 or IV05 or MV05 or MV06 or BV06 or BV07 or EV08 or EV07) OR (admissions into IV53 or IV57 or IV54 or IV59 or IV55 or IV52 or IV56 or IV51 or IV58 or IV60) OR (admission into IN14 or IN17 or IN23 or IN20 or IN19 or IN30 or IN26 or IN27 or IN31 or BS11 or DE99). |
| Equivalent: | IFQ521 |
| Coordinator: | Peter Bruza | p.bruza@qut.edu.au |
Overview
Human beings engage in information environments which are increasingly being powered by AI. Trust plays an important role in the use of AI and collaboration in human-AI systems. This unit covers two aspects within this context 1) the social and cognitive principles and processes surrounding trust between humans and intelligent agents, machines, algorithms, and/or other emergent technologies, (2) how interactions with AI shape human beliefs, perceptions, attitudes, and behaviours.
Learning Outcomes
On successful completion of this unit you will be able to:
- Synthesise knowledge of human cognition and theories of machine intelligence to understand human judgements of trust in interactions with AI systems
- Analyse, and report on interactions between humans and AI for indicators of using a range of methodologies
- Critically assess how trust and AI applies to contemporary IT contexts.
- Employ teamwork skills and processes to participate collaboratively, and demonstrate an understanding of trust and AI within the context of groups.
- Reflect on your individual learning journey to develop an understanding of your learning in the context of future IT applications and career aspirations.
Content
In this unit, students will explore the concept of trust within Human-AI interactions. The content will cover the cognitive basis of trust, including how trust evolves dynamically in interactions such as Human-AI teaming, and curating trust interventions when it breaks down.
Students will investigate the implications of trust and AI by addressing questions such as what trust and AI is, and how interactions between humans and AI shape beliefs, perceptions, attitudes, and behaviours. This will involve examining how interactions with AI influence human judgements of trust in real world applications and contexts.
Learning Approaches
Underlying concepts and principles will be examined as case studies of real systems impacting trust and AI. Cases will include contemporary illustrations such as: deceptive AI generated content, social manipulation through AI embedded systems, high stakes Human-AI decision systems, super-intelligent AI systems.
Interactions will be explored through a series of practical exercises which highlight the influence of AI on human thought and behaviour.
The unit will be delivered in a modular style by a teaching team with a focus on interactive, collaborative approaches to learning. Each module is based around a core topic designed to engage students in problem-based learning (individually or as part of a group). Throughout the unit acitiveities students will have opportunities to ask questions or seek formative feedback. Students are expected to engage with unit activities including attending classes, engaging in self-directed learning, preparing assessments, and reflecting on their experiences as a learner.
Feedback on Learning and Assessment
Tutorials and drop-in sessions will include opportunities for discussion and receiving immediate feedback on ideas related to the conceptual content.
Practical opportunities will be provided for the teaching team to view your work and provide direct feedback on it. You will be encouraged to use this feedback to enhance your opportunity for success in graded assessment tasks.
The teaching team will monitor the cohort as a whole and provide ongoing feedback throughout the semester on general progress of the cohort, or addressing specific issues that arise during the unit Individual feedback will be provided between assessment tasks to allow improvement over the course of the semester.
Detailed criteria sheets with any relevant comments will be provided for all assessment.
Opportunities will be provided for peer feedback to enhance the authenticity of assessment tasks, and encourage engagement with significant themes.
Opportunities will be provided for self-reflection to integrate learning, feedback and self assessment.
Assessment
Overview
The assessment for this unit is designed to integrate conceptual material on Cognition, Information Interaction, and Information Technologies, within a practical context. Two assessment tasks focus on foundational knowledge, critical understanding of knowledge in context, and application of knowledge. One task will focus on self-reflection. Foundational knowledge and self-reflection tasks will include formative components. All tasks are criteria referenced.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Synthesis and Application Task
Critique selected theories and perspectives on trust in human-AI interactions and demonstrate your understanding through applying these to a scenario or case that you develop.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Research Report
Develop an experiment-based research design that explores one or more aspects of trust and human-AI interactions. Report on the impilications of the research design and analysis according to methods and techniques taught during the unit, and link to core concepts.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Reflective Practice Journal
Document reflections on personal learning and growth practices throughout the semester in relation to unit content and develop strategies for future learning and development.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Resources to support your learning in this unit will be provided via the unit Canvas site.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit.
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.IN20 Master of Information Technology
- Demonstrate advanced specialist IT knowledge in at least one information technology discipline.
Relates to: ULO1, Synthesis and Application Task - Critically analyse complex IT problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: ULO1, ULO2, ULO3, Synthesis and Application Task, Research Report, Reflective Practice Journal - Integrate advanced, industry-best practice, IT methods, tools and techniques to develop and implement complex IT systems, processes and/or software.
Relates to: ULO2, Research Report - Employ leadership and initiative in both self-directed and collaborative contexts to create value for others.
Relates to: ULO4, Research Report - Create positive change through critically reflecting upon and actioning responses to the social, cultural, ethical, sustainability, legal and accessibility issues in the IT field, including how they relate to First Nations Australians and diverse populations.
Relates to: ULO5, Reflective Practice Journal
IN28 Master of Artificial Intelligence
- Demonstrate advanced specialist IT knowledge in Artificial Intelligence discipline.
Relates to: Synthesis and Application Task - Critically analyse complex Artificial Intelligence problems and opportunities and use creativity and problem-solving skills to generate innovative and novel solutions that are convincingly justified.
Relates to: Synthesis and Application Task, Research Report, Reflective Practice Journal - Integrate advanced, industry-best practice, Artificial Intelligence methods, tools and techniques to develop and implement complex Artificial Intelligence systems, processes and/or software.
Relates to: Research Report - Demonstrate knowledge of Artificial Intelligence research principles and methods and their application to Artificial Intelligence focused, real-world scholarly or professional projects.
Relates to: Research Report - Demonstrate business acumen and well-developed values, attitudes, behaviours and judgement in professional contexts.
Relates to: Reflective Practice Journal
IN30 Graduate Certificate in Data Science
- Demonstrate general knowledge of the principles, concepts, techniques, and approaches in data science.
Relates to: Synthesis and Application Task - Employ appropriate data science methods to derive insights from data to support decision-making.
Relates to: Synthesis and Application Task, Research Report, Reflective Practice Journal - Work both independently and collaboratively in teams to enable successful processes and outcomes.
Relates to: Research Report - Communicate professionally in oral and written form for diverse purposes and audiences.
Relates to: Research Report - Appraise personal values, attitudes and performance in your continuing professional development.
Relates to: Reflective Practice Journal