Boosting Engagement with GenAI: How Interview Practice Improves Learning

As a self-identified early adopter, I am very curious about new technologies and how they can be incorporated into the student learning experience. I have been experimenting with early versions of web-based creative platforms and Generative AI over the past ten years and have found some to be very effective with increasing engagement and retention of course topics.

I have designed GenAI-assisted in-class activities for COMM280 Fundamentals of Entrepreneurship, COMM386M Green Entrepreneurship and Sustainable StartUps, and BA562 Creativity for Business. For example, I have offered a warm-up activity focused on introducing the basic elements of storytelling for several sections of COMM280 and COMM386M, both with typical class sizes of approx. 40 students (n= 228). I provide a ‘templated prompt’ that requires students to fill in the blanks. As they generate multiple short Mad Libs, they quickly discover that the more precise and descriptive prompts (vs generic prompts with very broad terms) result in richer and diverse storylines.

Another example is asking students to initially investigate the potential needs or causes lurking below the proposed problem they seek to solve. They learn to improve their ‘AI prompts’ by asking their preferred GenAI platform to offer evidence (with links to sources) that their identified problem is actually real and sufficiently urgent. Following this exercise, they are then guided to explore the various UBC library databases to effectively identify supporting facts to their argument and point of view.

Through classroom-based participant observation and surveys, I can offer the following findings. One example is an intentional exercise where students used GenAI tools to deepen their understanding of leading and practicing empathy interviews with different GenAI platforms. They describe that the low-stakes practice encourages experimentation with trial and errors, offers instant feedback, encourages iteration, and the ability to practice on-demand roleplaying with multiple scenarios quickly and effectively. One student stated “Wow, this practice interview has provided so many themes and insights to pursue in our real person interviews.”

What I value most about integrating GenAI into the classroom is the opportunity for active, transparent engagement with students through hands-on experimentation. Students appreciate being part of a “teaching-learning lab,” where educators openly test industry-leading tools and thoughtfully guide them through the potential pitfalls and benefits of GenAI application to classroom topics. For example, they discover more derivative thinking than original thinking where commonly used concepts are presented instead of new combinations of concepts or observe cultural or stereotype biases, such as prompting the GenAI to generating an image of an entrepreneur and the output being a young white man, reflecting a repeated pattern in its data. They also experience direct GenAI benefits such as the rapid synthesis of diverse concepts with direct links to original published sources.

I aim to help students think critically about the process of using outputs from GenAI tools, by first encouraging them to surface their own reflections from the GenAI-assisted exercises and then reviewing the benefits and harms at the end of each lesson. For example, it is reassuring that the students can identify key issues with AI outputs that include: a lack of real human nuance from the GenAI responses that are more logical and less emotional when practicing a role-play based interview scenario; a false sense of confidence that seeks validation vs challenging personal assumptions when exploring root causes of a problem space; and the risk of confirmation and algorithmic bias of many GenAI platforms.

I strongly encourage all educators to invest the time to review their course content and active learning exercises and identify at least two or three simple ways to integrate GenAI. For example, consider adding an effective ‘hook’ at the start of a lesson that involves asking the students to explore the topic through GenAI prompts and then leading the students through a discussion of their discoveries. Another example is to ask students to engage in comparing their analysis of a course reading with a GenAI assisted analysis and have them find benefits and issues with both approaches.

My classroom GenAI experiments continue in terms of collecting student feedback on using AI effectively and spotlighting the inherent biases that both machines and humans express when seeking to build knowledge and skills and separating facts from hallucinations. I envision the future of AI will exponentially shape the student learning experience as they are adapting and experimenting with new technologies at a pace not seen before. My role as an educator is to embrace my curiosity of both new technologies and their impact on students by asking courageous questions such as “How might GenAI reduce time on grading and increase time actively teaching?” The ChatGPT response was: “Consider automating rubric-based feedback, enable students to run their draft review and feedback through GenAI for early feedback, and offer interactive concept and formative quizzes — these approaches reallocates your time from repetitive grading toward higher-impact teaching moments like discussion facilitation, mentoring, and feedback on complex thinking.”