Social robots hold promise for boosting student engagement and motivation through gamified learning experiences. However, effective human-robot interaction (HRI) in education demands a multimodal approach that harnesses both verbal and non-verbal communication. While existing research explores the individual benefits of robots, gamification, verbal communication, and nonverbal human-robot interaction in education, a gap exists in understanding their combined impact. Current frameworks, like Huang's GAFCC (Goals, Access, Feedback, Challenge, Collaboration) and Chou's Octalysis (eight core drives), excel in gamification design, but lack integration with robots and human-robot interaction methodologies. Similarly, Urakami's work on nonverbal HRI codes, despite its value, doesn't address gamification integration, particularly for educational settings. Studies exploring large language models for natural human-robot interaction, though promising, overlook gamification's role in education and how these cues can create a richer learning environment. This research addresses this gap by proposing a novel gamified human-robot interaction framework for educational settings. We modify the GAFCC framework to incorporate key verbal and non-verbal human-robot interaction elements commonly used in education. These elements include pattern recognition, robot gestures, background music, and stable robot navigation. Our framework aims to enhance student motivation, engagement, and overall learning experience by fostering a collaborative and immersive learning environment. This approach leverages the unique capabilities of HRI to create a gamified learning experience that goes beyond existing web and mobile-based gamification frameworks like GAFCC and Octalysis. Future research will focus on empirically testing and refining this framework to optimize gamified HRI for educational contexts.

Keywords: Gamified learning, Human-Robot Interaction (HRI), Robot-Assisted Learning, Multimodal Learning, Student engagement.