Extending guest lecture learning: Hybrid delivery, ai review supports, and reflection in large classes
Guest lectures are widely used in higher education to connect taught material with current professional practice. In large classes, however, their educational value can be uneven. Although guest speakers may enhance authenticity and relevance, student access, interaction, and post-session consolidation can be difficult to sustain at scale. These challenges are particularly visible in postgraduate settings, where cohorts are diverse and students may differ in attendance patterns, professional experience, and competing commitments. This study examines a teaching design intended to extend guest lectures beyond the live event through hybrid delivery, AI-supported review resources, and reflective integration. The study is based on a large postgraduate project management module in a business school, enrolling 284 students across four MSc programmes. Over a 12-week semester, four guest lectures were delivered by senior practitioners from different sectors. One session was delivered remotely and three took place in person on campus; all were supported through Zoom livestreaming and recording. Following each lecture, students were given access through the virtual learning environment to the recording, an AI-generated transcript, and AI-generated summary notes. These materials were lightly edited to improve clarity and correct obvious errors. Students were also encouraged to draw on guest lecture insights within a low-stakes reflective task at the end of the module. Evaluation data were gathered through an anonymous post-series survey. The findings indicate that students generally preferred in-person learning, but also made substantial use of the recorded guest lectures across the series. Many respondents viewed the AI-generated transcripts and summary notes as useful for revisiting lecture content, locating key ideas, and supporting later reflection. At the same time, the results suggest a consistent trade-off associated with remote delivery: students reported lower perceived engagement and a weaker sense of connection with the remotely delivered speaker than with speakers physically present in the lecture theatre. These findings point to both the potential and the limitations of technology-supported guest lecture design in large-class contexts. This study suggests that guest lectures in large postgraduate classes may be strengthened when they are designed across three linked phases: access to the event and its resources, structured interaction during delivery, and post-event consolidation through reflection. It also indicates that AI-generated transcripts and summaries may be most valuable when positioned as aids for review, retrieval, and integration rather than as substitutes for active listening or critical engagement. The study offers a practice-based contribution to current discussions on large-class pedagogy, hybrid teaching, and the careful integration of AI-supported learning resources in higher education.