What is Artificial Intelligence (AI)?
Artificial intelligence (AI) refers to technologies that enable computers to perform tasks that typically require human intelligence, such as understanding language, recognizing patterns, solving problems, making decisions, and generating or acting on information. AI encompasses a range of technologies, including generative AI, which creates new content, and agentic AI, which can plan and carry out multi-step tasks under human direction. This definition is adapted from .
Two categories of AI that are becoming increasingly relevant in higher education are generative AI and agentic AI.
Generative AI is a type of artificial intelligence (AI) that creates new content in response to user prompts. Depending on the tool, it can generate text, images, video, audio, computer code, simulations, and other forms of content. Generative AI systems are designed to assist users by producing original outputs based on patterns learned from large amounts of existing data.
Common uses of generative AI include:
- Responding to questions and prompts.
- Drafting, revising, summarizing, or translating text.
- Writing or explaining computer code.
- Brainstorming ideas, outlines, or lesson activities.
- Creating images, presentations, or other creative content.
- Adapting content for different audiences or reading levels.
Large Language Models (LLMs)
Large language models (LLMs), such as ChatGPT, are technologies that use large statistical models to generate natural-sounding text. The technology ChatGPT, developed by OpenAI, is powerful enough to generate text-based responses like letters, recipes, essays, songs, etc. Essentially, it does a very good job of predicting what a human would write next; however, it does not understand the content it generates or determine whether or not the information is misleading (Weidinger, et al., 2022).
Agentic AI refers to AI systems that can not only generate content but also plan, make decisions, and carry out multi-step tasks to achieve a goal. Rather than responding to one prompt at a time, agentic AI can coordinate a sequence of actions, use connected tools, and adapt its approach based on progress toward a user's objective.
Examples of agentic AI capabilities include:
- Planning and completing a series of related tasks.
- Organizing and analyzing information from multiple sources.
- Interacting with connected applications or services when permission has been granted.
- Automating repetitive workflows.
- Monitoring progress and adapting actions to achieve a specified goal.
Unlike traditional generative AI, agentic AI may access files, websites, email, calendars, cloud storage, or other connected systems if users authorize these connections.
Because agentic AI may interact with multiple systems and process larger amounts of information, it raises additional considerations around privacy, security, transparency, accountability, and human oversight. Users should exercise caution when granting permissions or providing access to university information and should only use institutionally approved tools in accordance with university policies.
Generative AI vs. Agentic AI Role Overview
This comparison is adapted from guidance on generative AI, and agentic AI from and .
| Aspect | Generative AI | Agentic AI |
|---|---|---|
| Role of the user | Provides prompts and evaluates AI-generated content. | Sets goals, grants permissions, and actively supervises the AI's actions. |
| Role of the AI | Generates content in response to prompts. | May plan, make decisions, and perform tasks using connected tools and services. |
| Human Oversight | Review outputs for accuracy and appropriateness before using them. | Continuously monitor actions, review outputs, and manage permissions throughout the task. |
| Information Access | Typically limited to information provided by the user, unless connected to additional tools. | May access files, email, cloud storage, or other connected systems if permission is granted. |
| Institutional Considerations See AI Applications at Queen’s. | Follow university guidance and avoid sharing confidential, personal, or sensitive information in unapproved AI tools. | Exercise increased caution. Only use institutionally approved tools, grant only the permissions necessary, and ensure access to university information complies with institutional policies and information security requirements. |
AI Tools and Technologies
ľĹĐăÖ±˛Ą recommends using institutionally supported AI tools when working with university information. These tools may provide additional privacy, security, and compliance protection compared to publicly available AI services.
AI tools increasingly combine multiple capabilities within a single platform. Rather than fitting neatly into a single category (such as "text generator" or "image generator"), many tools now support a range of functions. The capabilities available may vary depending on the product, subscription level, and organizational settings.
| AI Capability | Example Tools |
|---|---|
| Text generation & writing assistance | • • • • • • |
| Image generation | • • • • • |
| Document analysis & summarization | • • • • |
| Web search & research assistance | • • • |
| Coding & software development | • • • • |
| Audio & meeting assistance | • • • |
| Video generation | • • |
| Research & literature discovery | • • • |
As an Educator: Questions to ask yourself before using a tool:
- Is this tool designed with accessibility in mind?
- Does it comply with AODA standards? Can it be used with assistive tech?
- Does it comply with AODA standards? Can it be used with assistive tech?
- How does this tool adapt to different kinds of learners?
- Can it serve both a student with ADHD and one who is blind? Or does it privilege a narrow kind of learner?
- Can it serve both a student with ADHD and one who is blind? Or does it privilege a narrow kind of learner?
- What data is being collected from my students?
- Who owns it? Can it be deleted? Is it used to train other models?
- Who owns it? Can it be deleted? Is it used to train other models?
- Will this tool complement or replace existing supports?
- Are we using AI to augment teacher and peer support, or cutting corners?
- Are we using AI to augment teacher and peer support, or cutting corners?
- Am I modeling critical AI literacy? Am I upholding principles of I-EDIAA ?
- Are students learning to question AI-generated content, recognize bias, and advocate for their needs?
Know the Risks
As Generative AI continue to become more sophisticated and versatile, it is crucial to be cautious and assess their capabilities, issues, and potential biases. These include legal, ethical, political, ecological, social, and economic concerns.
Biases and Harms
Generative AI tools, as they have been designed and developed, reproduce biases, reinforce discrimination, and amplify stereotypes, leading to further harm to equity-deserving groups. This is because they use large amounts of data from the internet, and do not distinguish between reliable and unreliable data. For example, they reproduce collective writings (such as Facebook or Reddit comments, porn, and fake news as well as academic journals and “real” (fact-checked) news from across the world. Further, while ChatGPT can provide references, studies have shown that outputs are often made-up or nonexistent. But these are not the only harms that are reproduced by Generative AI. In the interactive graphic below, Sweetman (2023) highlights some of the harms that need consideration, including environmental harms, economic harms, as well as epistemic harms. Click on the hotspots (plus signs on the graph) to learn more about these harms and their implications:
Graph developed by Rebecca Sweetman, "Some Harm Considerations of Large Language Models (LLMs)" focuses on the relationship between Environment, Economy, Social Norms and Knowledge Reproduction with Design and Development, Operationalization, and Future Legacy.
lets others remix, tweak, and build upon our work non-commercially, as long as they credit us and indicate if changes were made. Use this citation format: Understanding AI in Teaching and Learning. Centre for Teaching and Learning, Queen’s University