AI-powered lecture copilot: A framework for enhancingstudent engagement and knowledge retention in an International MBA Program

Michael Faustino Bauer and Kadir Ider

No 6, BPS Working Paper Series from Berlin School of Economics and Law (HWR Berlin), Berlin Professional School (BPS)

Abstract: This paper presents the development, piloting and testing over two academic years (2024/25, 2025/26) of an AI-powered Lecture Copilot, an interactive assistant designed to improve inclusivity, engagement, and knowledge retention in live lectures within an international MBA program. It represents a practical classroom example of integrating AI into the teaching process and the curriculum per se, for enhancing the students' learning experience while also providing a use case for driving faculty development. The conceptual basis is rooted in the context of Andragogy for adult learning and the Copilot approach itself shows evident links to Experimental Learning Theory which is referred to often with recent AI advances. The system was prototyped using Google's Gemini large language model and integrated into live university lectures. A public survey (N = 191) showed broad optimism: more than half of respondents indicated they would ask more questions with such a tool and many anticipated greater comprehension, though concerns about accuracy, distraction, and over-reliance remained. Early classroom trials in an MBA class supported these expectations, with students (including those previously hesitant to speak up) posing substantially more questions and reporting improved conceptual clarity, alongside modest gains in short-term recall. However, ambivalence about long-term retention and the need for instructor oversight highlight ongoing limitations and risks. The findings emphasize both the potential of AI classroom copilots to improve immediate participation and comprehension, and the necessity of accountable, trustworthy, and pedagogically integrated design for sustainable adoption. These findings will inform the iterative refinement of the Lecture Copilot. The learnings from this application will add to the experience stack of the MBA program and enrich the learning repository on which the faculty members can build other classroom case studies and applications.

Date: 2026
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