There are a host of new AI tools available to Pacific University staff, faculty, and students, with a broad range of applications for learning and professional life. It is worth being aware of the many issues and concerns associated with this rapidly emerging technology. Students will have concerns about its use, and its impacts and associated practices will go on to shape their lives, so it is worth being ready to address these concerns in class; working with these tools in an ethical and responsible manner will be one of the most important skills to learn in this new context. This page offers a brief introduction to some key topics to be aware of.
These and other concerns are explored in the Framework for accessible and equitable artificial intelligence (AI) in education from the Inclusive Design Research Center.
Table of contents
Reliability and transparency considerations
Diversity considerations
Intellectual property and the use of data and labor
Environmental impacts
Responsible use among students
Privacy & security considerations
Diversity considerations
AI by its nature reflects the most common or “average” data, potentially underrepresenting marginalized groups and flattening out difference. AI can present the dominant or majority view or approach as a definitive, authoritative answer, which carries a danger of further marginalizing minority viewpoints.
Reliability and transparency considerations
AIs are known to confidently invent information when they lack the data to answer a question, and to reproduce the biases present in the data they were trained on. It is important to make a habit of checking for points of weakness and querying them (and once challenged, AIs often will readily discuss their failings).
AI tools are likely to reshape our world in the coming years, and there are concerns about the lack of transparency from AI companies about the architecture of their products. Alongside the trained neural network, LLMs typically use additional layers of algorithmic processing with unknown functionality. This lack of transparency makes it difficult to be confident about the reliability of these tools.
Intellectual property and the use of data and labor
There are many conversations happening around the way data has been acquired and used to train AIs. Large amounts of data have been harvested from mostly online sources, sometimes copyrighted, and sometimes non-consensually, and used to train models; human labor is also used in the training of models, and some of this has been described as exploitative. User interactions with AIs are also often used in subsequent rounds of training, though the above-described agreements prevent this in the case of approved tools.
It is important to be aware that although today’s AIs have been trained on a large amount of data, it is only data available from certain accessible sources, and provided by humans under certain conditions, and this will shape the biases and tendencies present in their behaviors in ways that are subtle and difficult to detect. Though AIs have access to a large amount of data, this should not be thought of as all human knowledge.
In addition, AIs are trained on creative and intellectual work that has taken individual humans years to develop, and are then able to reproduce work in a similar style. The availability of these tools could mean that future employment opportunities of a certain kind are reduced. It is worth being conscious of this shift and developing an understanding of the unique properties and opportunities of human creative work.
Environmental impacts
The boom in AI use has brought with it significant environmental impacts from the rapid increase in the power and water needed to maintain data centers, and the materials involved in the manufacturing of hardware. The training of models is particularly resource intensive; after this stage, each query from a user carries with it a processing cost. All of our online activities, from watching videos and video calls (high resource consumption) to Google searches (lower resource consumption), carry with them a cost–a straightforward AI query sits somewhere between these two examples. It is important to be conscious of the environmental costs of our activities, and to use them thoughtfully and responsibly.
The Learn with AI initiative has developed a tool called What Uses More?, which makes estimates for the water and power use of various tasks that do and do not use AI.
Responsible use among students
The landscape has changed very suddenly, and many students are looking for guidance as to which uses of AI are accepted and which are not. Students should be made aware of what responsible use looks like, and where ethical and educational concerns may arise.
It is worth discussing with students how their data may be used, and possible concerns around privacy, accuracy and bias. Some students will have legitimate concerns about using AI. If students have not had the opportunity to ‘opt in’ (for example, by signing up for a class that signaled that it would involve AI use), it is important to offer ways for students to participate without using AI themselves. This can be achieved by offering multiple options for assignments, or asking students to work in groups or pairs.
When discussing appropriate use of AI in a learning context, it can be effective to frame your conversation in terms of learning outcomes. When students are aware of which skills you want them to develop with a particular assignment and why, and how the activities you are asking them to complete will get them there, they are more likely to see the value in completing the work themselves.
Consider:
- What is the skill/ability you want them to learn?
- What do they need to do for themselves in order to develop that skill/ability?
- Where could AI assist or extend the work?
Where these points are effectively communicated, students have a good idea about which uses of AI will get in the way of, and which develop, their learning.
An effective method for promoting good uses of AI can be to model effective usage in class. In these activities:
- Thoughtful reflection can be made central.
- You can work on co-creating effective prompts, and make these available to students.
- You can supervise, ensure that students are critically engaging, and likely highlight some cautionary tales.
- Students work through the kind of process you want to promote and see the value in it, making them likelier to work in this way in independent work.
- AI use is brought into an above-board, accepted space in which students feel more comfortable asking questions about accepted use.
You can also develop a course policy to clearly communicate to students which uses are accepted and not accepted. For further guidance on this, visit our AI Course Policy Guidance page.
Privacy & security considerations
University Information Services (UIS) has carried out a careful review of the tools they have activated and approved, and secured agreements that specify a degree of data protection and privacy (for instance, ensuring that sensitive data will not be used for training). You can read more about this process and the agreements on their AI Features FAQ page.
The tools that have been activated and approved are as follows:
From the UIS AI Features FAQ:
“Before turning on AI features in any of our core services (the major services maintained by UIS for the use and benefit of the university), UIS does an evaluation of the privacy and security of that data. See our AI Evaluation Procedures. AI features that UIS has not evaluated may not be secure or protect the privacy of the information they process.
[…]
“Sensitive data, such as FERPA protected student records, can be used with Google Gemini, Box AI, Zoom AI, and Apple Intelligence.
“HIPAA-protected data (in a HIPAA-configured folder) can be used with Box AI. No other AI system is currently approved for use with HIPAA-protected data.
“Use of AI products and services that UIS has not evaluated is strictly prohibited.”
The tools cited can be used with FERPA-protected student records, and Box AI can be used with HIPAA-protected data. The tools reviewed and approved by UIS are the only ones for which constraints have been agreed on data use and privacy.
For more guidance on how to use these tools, visit:
General Considerations for Using AI in Teaching and Learning
Course Policy Guidance
Introduction to Prompting
Applications for Student Learning
Assignments in the Context of AI
Labor-Saving Tips for Educators