AI Tools for Teaching and Learning

Course number: STAT 115A: Intro to Probability Theory

Intentional, Interactive, Student-centered

Reflection on Instructional Change:

One practice I implemented from the AI Tools for Teaching & Learning Workshop was using an AI tutor bot (via the PingPong platform) to support student exam preparation. Specifically, I designed a guided activity where students used the tutor bot to generate flashcards and pose clarifying questions for Exam 1 topics. I first modeled the process during class, demonstrating how to input meaningful prompts and how to evaluate and refine the bot’s responses. Then, I created a structured assignment that asked students to submit five AI-generated flashcards along with two clarifying questions on concepts they found confusing. I took these steps to move students beyond passive review and into a more reflective, student-centered approach to studying—one that engaged them in creating their own learning materials while interacting critically with AI.

Implementing this practice had a noticeable impact on both student engagement and depth of learning. Students became more proactive in identifying their own knowledge gaps and used the tutor bot to explore those areas with curiosity. Many students reported that the process helped them “see what [they] didn’t realize [they] didn’t know,” which deepened their conceptual understanding. One student shared, “The flashcards helped me realize I wasn’t actually clear on conditional probability. I used the bot to ask more questions and finally got it.” Another wrote, “I liked that it gave me a starting point but made me think about how I’d explain it in my own words.” Overall, the activity increased motivation and made exam prep feel more interactive and personalized. It also allowed me to spot common areas of confusion and address them more effectively in class.

The AI Generated Content: Discussion and Reflection activity was the most influential in shaping changes I made to my instruction. This activity prompted me to critically examine how AI-generated responses could be used not just as content delivery tools, but as springboards for deeper learning. It challenged me to move from viewing AI as a passive output generator to positioning it as an interactive tool students could question, critique, and revise. That shift directly informed my decision to have students use the PingPong tutor bot to generate flashcards and clarifying questions, then reflect on the quality and clarity of the responses. The learning activity helped me realize that AI's greatest instructional value may lie not in what it produces, but in how students engage with, question, and learn from that output.

One “aha moment” from my Observe and Analyze Session was realizing how much I naturally lean into informal check-ins and student-generated questions as part of my teaching—but I hadn’t fully recognized their power as structured learning moments. Watching my own class through the lens of my colleagues helped me see that these interactions, especially those facilitated by the PingPong tutor bot, weren’t just engagement tools—they were key points where students were actively making sense of the material. That insight is pushing me to be more intentional about how I frame and follow up on those moments.

Evidence of Instructional Change

Evidence of Instructional Change
Example 1	Example 2	Example 3
Discussion Assignment instructions for using Tutor Bot to create flash cards for an Exam.	An example of student's response to the discussion thread.	Badge shows I have successfully completed the CSU Systemwide AI Tools for Teaching and Learning course.

Practice and Reflect Teaching and Learning with AI:

The course provided a thorough exploration of both generative and detection tools, equipping me with valuable insights into how AI is reshaping education. The generative AI tools, such as ChatGPT, Claude, Perplexity, Gemini for text, and Zoo, Firefly, DALL-E, and MidJourney for images, have demonstrated how these technologies can be used to create a wide range of content—from written assignments to images and even music. I found it fascinating how tools like MidJourney and DALL-E can generate visually rich content, which could be leveraged in classrooms to enhance learning through visual aids or creative assignments. Similarly, ChatGPT and Claude have potential in helping students with brainstorming and drafting ideas, providing valuable assistance in writing tasks.

However, as these generative tools become more accessible, the ethical implications surrounding their use are crucial. The course emphasized the importance of using these tools responsibly, ensuring students don’t over-rely on them for assignments and academic work. This is where AI detection tools, like GPTZero, DetectGPT, Maybe AI Image Detector, and Turnitin’s AI detection, come into play. These tools allow instructors to identify AI-generated content, helping uphold academic integrity. Still, false positives remain a significant challenge, underscoring the need for critical thinking when using them.

Integrating both generative and detection tools into teaching practices opens up new possibilities for engagement and creativity. For instance, I could encourage students to use generative AI for brainstorming, while also educating them on how to detect AI content responsibly. Tools like CustomGPTs, Tutor bots could be particularly valuable, enabling me to create personalized chatbots that answer course-related questions, fostering a more interactive learning environment. Ultimately, this course has inspired me to explore how these tools can be ethically and effectively integrated into my teaching, balancing AI's benefits with critical thinking and academic integrity.

Course Syllabus

Syllabus STAT 115A_02 (SPRING 2025).pdf

Faculty Bio

Faculty Biography
Faculty Picture	Faculty Professional Background
Jas Pannu	I am an Associate Professor in the Department of Mathematics and Statistics at California State University, Sacramento, where I have been teaching since Fall 2019. I earned my M.S. in Mathematics from Eastern Michigan University, where my love for exploring different kinds of data began. This passion led me to pursue a Ph.D. in Statistics at Auburn University, where I discovered my enthusiasm for teaching, research, and consulting. I am deeply involved in mentoring students and have participated in numerous professional learning communities focused on enhancing equity in STEM education. I always strive to create inclusive and engaging learning experiences for my students. I am also passionate about community building and service-learning projects, where I connect students with real-world applications of statistics. Outside of academia, I love to cook, watch movies, and socialize. I cherish spending time with my two young children- Sifti and Pernam, finding joy in our everyday adventures.

Funding Source
	This work was supported by: US Department of Education P031S230256 and P031C210012 *Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the US Department of Education.