Introduction to AI in Education

At 7:02 in the podcast, you’ll hear the AI say, “Make sure to come back for part two.” If you’re a regular podcast listener, you might recognize this as a common feature of podcast episodes—where a host says something like, “We’ll be right back after this message from our sponsor,” and then the episode immediately resumes without an ad actually playing.

So why did this happen?

This is an interesting example of how bias in an AI’s training data can lead to unexpected outputs. The AI that generated this podcast would have been trained on a vast amount of text, including real podcasts that often include these types of break cues. However, the AI doesn’t understand two important points that are obvious to us: i) that these commercial breaks are meant to signal an ad that would be inserted later, and ii) that podcasts are often recorded with this space for a commercial but then do not get a sponsor, resulting in the host saying, “We’re going to take a quick break” and then “Welcome back” with no ad played in between. As a result, the AI imitates the structure it has seen in its training data, mistaking this often-repeated pattern for a necessary feature of a podcast episode that it should include rather than recognizing it as something that should be ignored. 

This is an example of bias in an AI’s output resulting from errors in its training data. While the bias is benign in this case, it does illustrate how AI’s mimic the patterns in their training data without being able to understand what those patterns represent.

Introduction to AI in Education

AI is transforming education, reshaping how we teach, learn, and prepare students for the workforce. But what does that mean for faculty? How does AI work, and how can it be integrated responsibly into the classroom?

In this post, we’ll explore:

• What AI is (and what it isn’t)

• How AI impacts teaching and learning

• How to prepare students for an AI-driven workforce

• The ethical and practical considerations faculty should keep in mind

What is AI?

When people hear "AI," they often think of ChatGPT, but artificial intelligence extends far beyond chatbots. AI is already embedded in everyday technology, from facial recognition systems to voice assistants like Siri and Alexa. Some of the most common types of AI include:

• Computer Vision – Powers applications like facial recognition and self-driving cars.

• Speech Recognition – Converts spoken language into text, enabling tools like Google Assistant and dictation software.

• Recommendation Systems – Personalizes content suggestions on platforms like Netflix and Amazon.

• Generative AI – Produces new content, including text (ChatGPT, CoPilot), images (DALL-E, Midjourney), and even music.

Large Language Models (LLMs) and Chatbots

The type of AI we focus on in this discussion is a Large Language Model (LLM)—the technology behind chatbots like ChatGPT and Microsoft’s CoPilot. These models generate human-like responses based on vast amounts of training data.

While ChatGPT is widely recognized, several other AI chatbots are making an impact:

• ChatGPT (OpenAI)

• CoPilot (Microsoft) – The only AI tool officially supported by Lambton College IT.

• Gemini (Google) and NotebookLM (Google)

• Claude (Anthropic) – A powerful but lesser-known competitor.

• Llama (Meta) – Integrated into social media and business applications.

• Perplexity – A research-focused AI that blends chatbot capabilities with web search.

• AI Tutor Pro and AI Teaching Assistant Pro – Purpose-built educational AI tools.

Understanding these AI tools is the first step in using them effectively in education.

How Do Large Language Models Work?

Many people think of AI chatbots as a "smarter Google," but they function very differently. Search engines retrieve existing content, whereas LLMs predict the next word in a sequence based on probability.

Think of it like predictive text on a smartphone—except on a much larger scale. These models generate responses by drawing from massive datasets, which means:

Their predictions are shaped by patterns in the training data.

They can reflect biases present in the information they were trained on.

Their accuracy depends on the quality and diversity of their training sources.

They sometimes produce hallucinations—plausible-sounding but false information.

Memory and Context Windows

One key limitation of LLMs is their context window—the amount of recent input they can consider when generating a response. Once that window is exceeded, the AI no longer "remembers" previous interactions.

For long or complex discussions, users may need to reintroduce context to keep conversations on track.

What Happens to Data Entered in an AI Chat?

One of the biggest concerns about AI is data privacy. What happens to the information users input into a chatbot?

AI models process inputs in real-time and do not store permanent memory across sessions.

Some platforms temporarily retain chat data for training or quality improvement.

Users should assume that public AI models may analyze inputs, even if they don’t store them long-term.

Institutional vs. Public AI Use

At Lambton College, faculty and staff handling sensitive student data must use Microsoft’s CoPilot and be logged in with their College credentials. Entering student information into ChatGPT or other public AI tools violates privacy policies.

For example, using ChatGPT to check a student’s essay for grammar would be a privacy violation, whereas using AI to generate discussion prompts would not. Faculty should consult IT if unsure about compliance.

AI Literacy: A Foundational Skill for Educators and Students

Just as digital literacy became essential in the internet era, AI literacy is now a crucial skill.

What is AI Literacy?

AI literacy refers to the ability to understand, evaluate, and effectively use AI. For faculty, this means:

Knowing how AI models generate responses.

Recognizing their capabilities and limitations.

Using AI strategically in teaching and research.

For students, AI literacy fosters:

Critical thinking about AI-generated content.

The ability to verify sources and recognize misinformation.

A deeper understanding of AI bias and ethical considerations.

By integrating AI literacy into education, faculty can better prepare students for an AI-augmented workforce.

Bringing AI Into the Classroom: Best Practices

Moving Beyond "AI as a Shortcut"

One of the biggest concerns in education is that students may use AI to complete assignments without real learning. Instead of banning AI, faculty can:

✅ Encourage AI for brainstorming and feedback rather than for generating entire assignments.

✅ Require students to reflect on AI use, explaining how it shaped their work.

✅ Create assignments that emphasize process over product, requiring drafts, outlines, or iterative refinement.

Ethical and Responsible AI Use

Faculty can model ethical AI use by:

Avoiding over-reliance on AI—it should enhance thinking, not replace it.

Teaching students to cross-check AI outputs for accuracy and bias.

Clarifying institutional policies on acceptable AI use in coursework.

Practical Uses of AI in Teaching & Learning

Faculty can leverage AI for:

Generating discussion questions and lesson plans

Creating formative assessments (e.g., quiz questions, practice tests)

Providing AI-powered tutoring to personalize concept review

Summarizing complex research materials to support student learning

However, AI should always be a starting point, not a final authority. Faculty should teach students to verify AI-generated summaries and critique AI-driven insights.

AI in the Workplace: Preparing Students for the Future

As AI becomes more embedded in professional environments, employers will expect graduates to:

✅ Use AI as a research and productivity tool.

✅ Understand AI’s limitations and biases.

✅ Leverage AI without sacrificing critical thinking.

By teaching AI literacy and responsible AI use, faculty can ensure students are ready for careers where AI is an essential skill—whether in healthcare, business, or technology.

Final Thoughts: Shaping AI’s Role in Education

AI is not going away—it’s becoming part of the fabric of work and learning. Faculty have an opportunity to guide students in using AI critically, ethically, and effectively.

By integrating AI into coursework thoughtfully and staying informed on its capabilities and risks, educators can:

✅ Enhance critical thinking and creativity.

✅ Improve student engagement and productivity.

✅ Ensure academic integrity while embracing AI’s potential.

Whether you start by experimenting with AI for personal productivity or integrating it into your teaching, the key is to approach AI with curiosity, ethical awareness, and a commitment to lifelong learning.