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Responsible use of AI in education at Monash

Monash University supports the responsible and ethical use of AI and Generative AI as outlined in the Responsible Use Principles. Additionally, refer to guidance about expected AI literacy capabilities for educators at Monash.

The use of tools such as Gemini is governed by existing University policies, including those covering artificial intelligence, information management, recordkeeping, and cyber security. Concerns about AI often relate to how information is used, rather than the technology itself.

If you have concerns about AI use

If your concern involves privacy, such as the unauthorised use, disclosure, access, loss, or modification of personal or health information, submit a report via Report a privacy incident.

For other concerns, go to Raise your concerns to report an issue. This is the best starting point to access support and guidance, including matters where AI may be involved.

You may also seek advice from the Office of the General Counsel (OGC).

Educating for responsible technology use

To equip our graduates with the skills and knowledge that they need for the future, we have a duty to explore and educate students on the benefits of the judicious use of technologies while also ensuring they understand the risks and ethical considerations.

Responsible use in education includes:

Viewing AI as a collaborator and thoughtfully combining it with human intelligence(s) to achieve outcomes.
Using AI with honesty, in ethical and defensible ways, with human responsibility taken for the quality, accuracy and veracity of outcomes.
Being transparent about AI use, which means explicitly acknowledging that use and clearly documenting and attributing its contributions.
Making concerted efforts to maximise safe and secure use ensuring appropriate AI tools with appropriate data and privacy protections and with clear understanding of terms of service are being used.
Recognising that AI provides benefits, challenges and risks that need to be carefully considered
Using AI in ways that mitigate negative impacts, societal biases, ensure fairness, and are inclusive and accessible.

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Protecting data privacy View

AI complicates and challenges existing practices of gathering, use of, and deployment of personal data. In some cases the data you enter may be kept private and in others you may be contributing data for further training of the AI system or handing over information that the tool can store and use in other contexts.

Reinforcing one of the Monash Responsible Use Principles, protecting data privacy is essential when using AI. Monash enterprise systems offer much better data protection than free AI systems. Here are some general rules to follow:

Avoid providing personal information to AI systems, especially if they are training on and potentially sharing the information.
Do not put any identifiable student or staff data into AI systems that are not Monash enterprise systems with approved and appropriate data and privacy protection.
Do not put any Monash information that requires commercial confidentiality into AI systems, unless they are official Monash enterprise systems with proper data and privacy protections in place.

Resources supporting safe use of AI

Decision making workflows to ensure safe and responsible use of data with AI tools and systems (Monash staff only).

Considerations for educators

Educators must check the data privacy policy for the tool or platform to understand what data is collected and how this data may be transformed or redeployed

Preference tools that have Monash enterprise level approval and protection
Check terms of service and data policies of All tools used to understand if the data protection is appropriate for your particular use case/need
Model and direct student behaviour to uphold data privacy expectations and respect for the rights use appropriate materials in AI systems

Resources

Copyright and intellectual property View

AI technologies are rapidly transforming leading to gaps between current law and AI usage and practice. Monash staff and students should gain a basic understanding of the limitations imposed by copyright law so they can make informed decisions about using AI. There are several key issues to consider.

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When uploading information into an AI system that will be training on or sharing the data then who owns the copyrights to the work being shared?
For example, uploading copyright material to AI could be breaching copyright or licence terms and conditions. Licences for e-journal articles and e-book chapters do not allow uploading to AI. This is a legal risk that needs to be understood by all users if they are to use any Monash-subscribed resources.
The only risk free material acceptable for AI uploading to AI that do not have strict privacy and data protections would be:
- where users own or have permission to use/upload the material to AI
- Creative Commons licensed material (excluding NonCommercial use only and No Derivatives material)
- Out of Copyright material
Copyright needs a human author and demonstration of significant contribution to the production of the material.
Under current Australian, EU and US law, material created by AI has no copyright protection since copyright requires an identifiable a human author. This means that AI-generated outputs -- be it for research, education or professional purposes -- created by Monash staff or students, that have been produced with AI may not be owned by the staff members, students, or by Monash. Even though the more human intervention in the AI output, the more likely there might be some copyright protection, anyone could subsequently use the material without permission. This requires users to consider how they might use AI-generated outputs in their academic works regardless of their role at Monash.
If outputs created by AI are too close to existing copyright material then it could be considered a copyright infringement.
For example, if an AI creates an image in the style of Picasso and because it is trained on Picasso’s existing artworks, it looks the same as an existing Picasso. This issue relates to how the use of copyrighted materials by the AI platform might translate to the copyright’s of AI-generated outputs' (usually unbeknownst to its users).
Engaging with these tools raises issues with both appropriate authorship and ownership of training material. The training data for some AI systems draws on information broadly available on the internet and the AI systems typically use text, images, videos, sounds etc. indiscriminately in response to a prompt or a request. Additionally, there are questions about how submitted material may be used to further train or refine models, and how platforms plan to retain data for these purposes.
On a personal level, you may consider how and why you are directing students to engage with tools that have been assembled from stolen, scraped, and copyright infringing datasets. On a practical level, you may want to consider the durable contributions your students may be making to specific platforms and tools.

Considerations for educators

AI tools and platforms interact in new ways with existing understandings of copyright, authorship and moral rights.

Select tools with clear frameworks around authorship and use of copyrighted material
Model and direct student behaviour to uphold copyright restrictions.

Resources

Bias, cultural sensitivity and appropriateness View

Ethics and transparency View

For any use of AI technologies, ethical engagement requires transparency about when and how tools are employed. Clear communication about the integration of AI tools in teaching supports accountability and enables educators to model responsible use. This openness not only demystifies the technology but also encourages students to think critically about its implications, fostering a more informed and reflective learning environment.

Acknowledgement and accountability are important parts of the evolving etiquettes for responsible AI use. Responsible use of AI should be explainable in terms of:
- What AI was usedWhich AI tools assisted in producing the work, how many versions and drafts were undertaken, and what known biases may exist with the data sources/AI tools used.
- How AI was usedWhich aspects/parts of the work did AI assist with, including how the AI contributions have been modified, expanded, incorporated, etc.
Refer to guidance for AI Acknowledgement and documentation and Learn HQ for student resources Acknowledge the use of generative artificial intelligence.
Training and use of AI tools, especially large language models, is incredibly energy intensive and has obvious and increasing impacts on carbon footprints. Engagement with tools should be undertaken with an awareness of these costs - recognising that behaviours and expectations set now will shape broader approaches to the tool and expectations around its practical use and availability.
Understanding the known environmental impacts may influence our decision making processes.
Openness here does not refer to the visibility of the underlying AI model guiding the outputs but to the openness of the organisation/creators and their commitment to transparency in their data sources, training approaches and other structural decisions.
Does the tool operate within an open source framework or a closed commercial framework?
Having a clearer picture of the orientation of the provider will help evaluate other components of the tool - and ideally model practices for students that are closer to academic models of knowledge production rather than the closed shop of commercial innovation.
AI tools are often presented as black boxes - systems that defy clear explanation of their processes but retain predictive powers and abilities. There are questions about the possible false dichotomy between ‘explainability and accuracy'. Efforts to achieve explainable AI are a counterbalance to the “black box” nature of many systems (info in, info out but no clear understanding of what occurs in between). Are the AI tools that you choose to use explainable?
If the internal workings of the tool or model are not visible then we should still be concerned with other relevant variables shaping outputs - including training data and general principles informing the training and deployment of the tool.

Considerations for educators

Model and direct student behaviour to always acknowledge when and how AI tools have been used.
Be aware of environmental and social concerns about AI systems.

Accuracy and reliability View

Pedagogical choices View

Educators should always evaluate their pedagogical reasoning for incorporating the use (or restriction) of AI technologies, ensuring the appropriate choices for tools and activities in their teaching practice. The onus is on the educator to provide context and information around the use and implementation of particular tools and activities. In other words, carefully explain why.

This section introduces key considerations to ensure ethical, equitable, and effective integration of AI tools in educational settings. Expand the accordions for details.

There can be technical, physical and economic barriers to entry for some technologies. Identify and mitigate these barriers by assessing the accessibility of chosen tools and providing alternative formats and adaptive support where needed.
Emed inclusive teaching has further guidance about ensuring the accessibility of resources as well as other inclusive teaching practices.
The university supports and provides some access to equipment and technologies, but when selecting AI tools for inclusion in teaching and study activities, think about how to structure activities with everyone equally using free or paid platforms and equitable access to the tools required for the activity.
Following established logics for apps, AI tools are typically offered in combinations of free, freemium and premium options, each with different levels of access and different cost structures. Paid access may grant faster outputs or more powerful models, or being able to access the service without rate limits. Consider which level of access may be required to effectively use the tool for a given educational application and what strategies may be used to try to ensure fair use across a diverse cohort of students. For example:
- Can you require everyone to use only the free version and its functionalities?
- Should everyone purchase a particular level of access that is clearly identified as a manageable cost associated with undertaking the unit?
- Can the use case be structured so that receiving a faster output via a paid version does not necessarily disadvantage someone using a free version to accomplish the same task?
Educators should recognise that students enter the classroom with varying degrees of familiarity with AI tools, some may already possess technical fluency or conceptual understanding, while others may be encountering these technologies for the first time.
- To ensure equitable access and use, foundational instruction should cover both the technical aspects (e.g., how to use a tool) and the critical content knowledge (e.g., ethical implications, limitations, and biases).
- By scaffolding learning experiences that build AI literacy progressively, educators can empower all students to engage responsibly and confidently with AI in their academic work.
Engagement with tools is not without effort. Time is required to understand the tool, approach, interface, and conventions. This cognitive load can impact on learning experiences and student wellbeing.
- If learning is structured with multiple platforms and tools across a unit, students will need to retain working knowledge of these tools.
- Diversity, range, and types of tools employed in a unit should be carefully considered from the perspective of variations in their interface, approaches, systems and conventions.
Universities maintain enterprise agreements and technical support for a select range of software, platforms and hardware. Yet AI tools are emerging at a rapid rate and at various scales.
- In selecting an AI tool/platform what kind of technical support (instructional material, problem solving, community of practice, etc.) exist?
- What kind of additional support will students and staff need to use the tool/platform and how will it be provided?
We need to develop capacities to think for ourselves, think with AI and collaborate with emerging technologies. However, we need to also guard against over-reliance.
- We can take advantage of the conveniences and efficiencies of AI and emerging technologies, which we can calibrate to our preferences and ways of engaging with information, but we should try to avoid becoming too narrow and too dependent on technologies.
- We need to engage with diversity in order to be better equipped to adapt when needed. We all have certain preferences but occasionally trying something different or expanding our comfort zones can contribute to our growth.
- We have to actively mitigate letting AI think and work for us in order to maintain our human capacities and valuable contributions.
You may want to consider a broader set of student experiences and interrogate what is embedded or embodied in interacting with the tool. For example:
- What kinds of experiences are being constructed for the student? How do these contribute to an overall sense and experience?
- Does engagement with the tool replace authentic learning events, dialogues, and interactions?
The current landscape is characterised by startups and emerging tools with rapidly changing terms of use and product scope. With this in mind, what duration might you expect this tool to be available for? a semester; a year; or the duration of your students’ course?
- On a practical level - how translatable are the steps or processes or principles embedded in the use of the tool, and what will happen if the tool disappears, is discontinued, or changes dramatically in scope, purpose or function.
- Given that we often engage with ecosystems of software, consider whether the tool works with a durable set of other associated tools that the student could reasonably expect to re-use, e.g. Google, Microsoft, Adobe product families.
- When selecting a tool, are you building in skills that will scaffold and support the student across other semesters and contexts, or are you locking a student into a particular way of working and a captive of a particular platform, that may or may not be the current industry standard.
Having engaged with a tool and developed a working knowledge of its processes a student should reasonably expect to be able to re-deploy this knowledge at a later date. There may need to be consultation around a set pathway through and appropriate scaffolding of specific AI tools across areas of study within courses.
- Consider student experiences in other units.
- Coordinate consistency of tools across units, year levels, courses, faculties.
- Plan how engagement with the tools will develop complexity over the duration of the course.

Considerations for educators

Make informed decisions when selecting and using tools in the teaching environment.
Ensure fair access to tools across a diverse cohort of students.
Scaffold the use of AI technologies in learning and assessment activities to enable all students to meaningfully engage in learning.
Map the selection and integration of AI technologies across the course to minimise duplication and avoid gaps in student development from foundational to discipline-contextualised engagement.
Ensure your understanding of Theme 4: AI pedagogy and practice from AI literacy capabilities for educators.

Resources

Supporting students’ responsible use

The Monash responsible use approach to AI includes supporting students to engage responsibly with AI in their study. Educators should design learning activities that cultivate and guide students responsible use of evolving technologies within their discipline. Educators have a responsibility to be explicit about expectations and help students understand the pedagogical reasons for their assessment conditions.

AI resources and advice for students View

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