According to recent reports, 67% of faculty members in United States colleges and universities reported using classroom interaction technologies, demonstrating the deep influence these tools have had on higher education. They improve interactivity, accessibility, and personalisation in learning, while also advancing teaching methodologies, learning experiences, writing skills, and research development. By combining traditional and innovative pedagogical methods, education can offer a holistic experience, equipping students for the evolving job market. In the field of marketing, digital tools for consumer insights are effectively employed in both teaching and research. Eye-tracking and facial expression analysis technologies, for instance, have become invaluable for training marketing students and conducting research. These technologies offer insights into consumer behaviour and are increasingly accessible for classroom application. They not only equip students for future careers but also enable effective teaching, maintaining engagement and learning quality. The application of modern technologies is particularly valuable for teaching complex concepts such as consumers’ perceptions of advanced technologies (e.g., service robots). This method aligns with Kolb’s Experiential Learning Theory, which highlights the influence of experiences, including cognitive, emotional, and environmental factors, on the learning process. By integrating real-world projects and industry collaborations into coursework, students can apply theoretical knowledge to practical scenarios. Experiential learning projects effectively address complex phenomena by incorporating systems-level perspectives. Nonetheless, the area of usage of modern technologies for teaching complex subjects through experiential learning is understudied. Therefore, this paper addresses this gap and explores the utilisation of modern technologies, such as AI-powered facial expression analysis software, to bridge teaching and research in addressing complex subjects, including human-robot interaction.

This study represents the initial phase of a project aimed at implementing modern technologies to teach complex topics such as consumers' perceptions of service robots and human-robot interaction in retail, to students through experiential learning. In this phase, a course design is developed based on Kolb’s experiential learning cycle, utilising Artificial Intelligence (AI)-powered facial expression analysis software, FaceReader by Noldus Information Technology company, as a technical solution. During the course, students examine consumer behavioural insights from the perspectives of observers, analysts, and consumers.

This study highlights the transformative role of modern technologies, such as AI-powered facial expression analysis software, in facilitating the integration of teaching and research in higher education, particularly for addressing complex topics like human-robot interaction in retail. By adopting Kolb’s Experiential Learning Theory as a framework, the proposed course design bridges theoretical knowledge with practical applications, enhancing students’ critical thinking, decision-making, and industry readiness. This study contributes to the theory by offering a robust pedagogical model that integrates experiential learning with modern technological tools, providing a replicable framework for teaching complex and interdisciplinary subjects.

Keywords: Experiential Learning, Human-Robot Interactions in Retail, Facial Expression Analysis.