Artificial Intelligence in Learning & Teaching

Artificial Intelligence (AI) is technology that enables computers to perform tasks that usually require human intelligence (Russell & Norvig, 2021). Users engage with these tools through prompts, allowing them to direct the tool’s responses. Generative AI refers to AI tools that create new content, like text, images, video, or music, that looks and sounds similar to what humans would produce. It learns patterns from existing data and uses that knowledge to generate new output. This includes tools like ChatGPT and Microsoft CoPilot.

Try out the following to explore the capabilities of these AI tools:

• Input a longer text and ask for summary or a translation into a different language
• Compare the output of different AI tools using the same prompt
• Request things beyond the capabilities of a typical search engine such as generating a rubric, creating multiple-choice questions, and/or developing a lesson plan on a specific topic.

To learn more about how to use AI tools to improve your workflow and enhance your instruction, consider attending our Generative AI Crash Course workshop session! 

As the interest in Generative AI has grown, so has the interest in effective AI detection solutions. While there are AI detection tools out there, they can often be unreliable, producing false positives and negatives (Bender et al., 2021). They may also exhibit bias against non-native English speakers and neurodiverse students (Liang et al., 2023). Additionally, these tools can be easily manipulated (Jobin et al., 2019). 

If you suspect that a student might have used generative AI for their assignment, having a conversation with them is a great initial approach. Additionally, due to privacy concerns, it’s advised not to upload student work to other AI detectors without their consent.

Efficiency: AI has the potential to enhance what its users are capable of. Some of the ways that generative AI tools can help instructors is by assisting in the development and modification of lesson plans, rubrics, assessments, case studies, summaries, reports, outlines, tables, revisions, graphics, audio, and more. With a well-developed prompt, various tools can develop the resources above often in a matter of seconds to minutes (Luckin et al., 2016).

Concept Exploration: Users across disciplines can use generative AI to assist in brainstorming, investigating a topic, exploring alternative perspectives, and/or jump-starting an email (Luckin et al., 2016). 

Learning Support: As a learning tool, students can interact in real-time with generative AI tools to get immediate feedback and support. With that said, AI is not a substitute for instructor-student or student-student interaction but can be an effective tool when those types of interactions are not possible.

Career Skills: Currently, there has been an increasing demand for individuals equipped with the knowledge and skills to create AI programs, critically assess AI technologies, collaborate effectively with AI, and leverage it as a tool in various capacities in the workforce. This will likely only expand as AI tools are utilized more in the workplace (Luckin et al., 2016).

Equity and Access: While there are free versions of AI tools, there are more robust AI tools behind a paywall that offer an expanded feature set, more current information, and better output. Additionally, not all AI tools are available globally and not all tools meet accessibility requirements which can make it difficult for students with diverse learning needs to benefit from AI tools as compared to their peers (Crawford & Joler, 2018).

Inaccuracies / Bias: AI tools such as large language models and image generators are trained from data that exists on the internet including digitized materials, online discussion boards, and other human-generated sources. As a result, the outputs that AI tools generate can contain misinformation as well as reflect views of racism, sexism, ableism, violence, stereotypes, and more that were “learned” from what is published on the internet (Bender et al., 2021). 

Academic Dishonesty: The rise of AI tools has made generating written content and other forms of content more efficient and easier. It is not always easy to determine if work is generated by AI or not and AI generators have been found to not only be unreliable but in some cases biased (Jobin et al., 2019). 

Privacy: Not all AI tools offer protected versions that safeguard data. In most cases, it is safe to assume that information shared with AI tools may not be private and there is a risk that data can be exposed. It is important to consider this as well as speak to your students about this privacy issue when using AI tools (Jobin et al., 2019). 

Intellectual Property: Since AI tools learn from what is available on the internet, there have been challenges with copyright and intellectual property. Users in creative fields such as artists, writers, and publishers have challenged AI developers in court since their copywritten material is often used to train their AI programs often without permission (Metz, 2023).

Unethical Labor Practices: In many cases, research suggests that there are reports of unsafe and unfit labor conditions that human workers who provide feedback to train AI models face on a daily basis. (Crawford & Joler, 2018)

Carbon Footprint:As AI tools continue to evolve, they will require the use of more energy compared to traditional Google searches and other online tasks that we are used to. This can impact emissions in a negative way and make the task of lowering emissions for companies that are planning to use AI often a significant challenge (Strubell et al., 2019).

It is important to discuss your policies on AI usage in a clear and unambiguous manner. Including an AI statement in your course syllabus and reviewing this with students at the start of the course helps to begin the AI discussion. It can also be helpful to outline which assignments allow for the use of AI tools and which assignments do not permit the usage of AI tools (Selwyn, 2019).

The following document, created by Lance Eaton, Senior Associate Director of AI in Teaching and Learning at Northeastern University, offers examples of AI syllabus statements tailored to different levels of allowance across a variety of academic disciplines. This resource can serve as a valuable guide when crafting your own AI syllabus statements for your courses. When establishing your policy, it’s crucial to communicate this decision with your students and, as mentioned earlier, include reminders about AI usage in your assignment instructions as well.

Syllabi Policies for AI Tools

In addition, our Graduate School has developed a policy regarding the usage of Generative AI which can be found on the Graduate School Bulletin.

Much like us, students have varying experience, knowledge, and opinions of AI tools. Some students are exploring these tools for the first time and others are using AI tools for various purposes including editing and revision of writing, coding, math, citations, and even everyday uses such as cooking, shopping, and more. This is an important consideration to make when developing AI guidelines for your courses.

The following are some recommendations for discussing AI with your students:

• Explain how your AI policies align with your learning goals and course objectives
• Outline your AI usage expectations for the course as well as individual assignments
• Discuss AI usage with your students and ask about their experiences using AI
• Discuss the importance of academic integrity 
• Explore AI in the classroom together (eg. have students ask AI questions and then critique the output for accuracy and credibility)
• Revisit these talking points and conversations regarding AI throughout the semester as needed

Properly citing AI tools has become more of a common practice as AI tools continue to develop further and evolve. Since many students are at different levels of familiarity with using AI tools, providing instructions on how to do this in your syllabus as well as on assignments where it would be necessary can be helpful for students.

Citing AI Content: Any AI-generated content that is quoted, paraphrased, or otherwise incorporated into a student’s work should be cited including AI-generated images, media, and other visual content. Some AI tools also make their interactions shareable, which can be included as a link or URL. Currently both MLA Style Center: How do I Cite Generative AI in MLA Style? and  APA Style: How to Cite ChatGPT provide further guidance on how to cite generative AI in MLA and APA formats (MLA Style Center, 2023; APA Style, 2023). 

Acknowledgement of AI Assistance: If students are using AI tools to assist them with different learning activities such as research and composition, it is important that they know where and how to document this usage. This could be in the form of an acknowledgement section, methods section, notes section, or another format that has been previously discussed with the students.

Review AI-Generated Content: In many cases, AI tools can develop biased, illogical, or false information and even can generate sources that do not exist. This can be due to where the AI tool is pulling information from, the wording of the prompt that was submitted, and/or other reasons. It is important that anyone who makes use of AI tools to generate content reviews its output for accuracy.