Dr. Ayse Ozturk

My Hands-On Approaches in the Classroom

These days, AI has become a practical teaching tool that can help students learn more effectively. Here are some examples on how I’ve integrated AI into my courses, along with key takeaways that might help you do the same.

Why AI Matters in Higher Ed

Students already use AI-driven apps in daily life. Bringing AI into the classroom teaches them to leverage these tools for real-world problem-solving. It fosters engagement, encourages creativity, and helps build professional skills. It also helps to collectively realize and address the issues that come with AI.

Hands-on Examples of How I Integrate AI in the Classroom

Below are some strategies on how I integrate AI in my classes to enhance the AI literacy and skills of students:

1. AI-Generated Study Guides and Practice Tests

When students request study guides or practice tests before the exam, I assign a few minutes to help them with what they need using NotebookLM. They upload lectures and readings into NotebookLM. Then, NotebookLM generates study guides or questions. It also offers text-to-speech audio versions for diverse learning styles.

• Pros: Students gain practice prompts curated from their own materials. The study guides refer back to the uploaded course materials with minimal risk of hallucinations.
• Cons: Remind them to verify accuracy and correct any AI errors.

2. Collaborative Chatbot Workspaces

I set up a course folder on BoodleBox which is a learning portal with various AI assistants installed. Students chat with the chatbot in the course folder and get insights to use in their term projects. In the meantime, students can also see each other’s chats and get inspired by peers’ interaction with the bot. I am also using it to ask students to provide feedback on each other’s work or to create some engaging activity to see who will get the best output on a conversational-AI assignment.

• Pros: Reduces repetitive questions and builds community. Students can also share feedback on assignments or test prompts that they’ve improved through AI.
• Cons: Monitor for plagiarism or over-reliance on AI prompts.

3. Creative Brand-Building Projects

Students use AI to develop branding. They brainstorm brand names, create logos with image generators (e.g., Midjourney), and produce short commercials (e.g., Lumen5). They even compose background music using platforms like Suno and build simulated websites on Butternut AI.

• Pros: Gives students tangible marketing, design, and collaboration experience in a single project.
• Cons: Integrating students’ own inputs is necessary at each step.

4. Accessible Data Analysis

Not every student feels confident in analyzing quantitative data. So, we use AI assistants (e.g., Claude, Copilot) to interpret sample datasets. AI tools might identify trends or outliers, but I require students to explain or critique those findings.

• Pros: Builds analytical confidence and shows how businesses actually use AI-driven analysis.
• Cons: We need to remind students not to accept the results as they come. We need to encourage them to question data sources and assumptions.

5. Promoting AI Exploration & Literacy

Beyond these core tasks, I nudge students toward other AI tools. Some common ones are:

• Gamma for presentations.
• MyLens AI or Napkin to create consultant-grade visuals similar to BCG or McKinsey’s.
• Google AI Studio for tutoring (with a reminder not to share sensitive info).

This variety helps them figure out which platforms fit their needs. I believe with exposure to a variety of tools; students gain an essential skill and confidence for using AI that will help them in today’s tech-driven world.

Some Risks and Challenges

1. Accuracy Issues: AI sometimes creates incorrect information which are also called hallucinations. We need to remind students to double-check important facts.
   • To address this issue, I require students to include at least two credible sources verifying key facts or statements provided by the AI in their assignments. This ensures that students develop the habit of cross-checking for accuracy.
2. Over-Reliance: Some might be tempted to let AI do all the thinking. We need to assign reflection tasks and peer evaluations to keep them critically engaged.
   • To address this issue, I ask students to explicitly annotate AI-generated responses and their own insights or critiques. For example, after using AI to draft answers, students write a short paragraph explaining whether they agree or disagree and why. Or, I ask them to evaluate the chatbot’s output from their own perspective, fostering critical thinking and personal accountability.
3. Ethical & Data Privacy Concerns: Students must treat AI outputs as public, even if it feels private. We need to emphasize responsible data sharing.
   • To address this issue, I ask students to create a back up email account and use that back up email account rather than their personal accounts to sign up on these AI tools.

Results & Takeaways

As a result of my gradual AI integration in my classes, I’ve observed excitement growing over time in class, as well as better collaborations, and higher-quality final projects. By working with AI, students develop crucial workplace skills and learn to think more critically about this new technology’s strengths and limits.

Conclusion

Educators do not need to be AI experts to introduce these tools effectively. A few small integrations (such as AI-generated study guides using NotebookLM or collaborative chatbots using BoodleBox) can quickly open students’ eyes to new ways of learning. Over time, you can expand to more ambitious projects, all the while maintaining the rigor and academic integrity of your course.

-by Ayse Ozturk

For the resources and tools mentioned, please refer to the Resources page here.