Typology: Digital Learning
Language: English
Date: 2025
Gamifying Human-AI Interaction: Using Large Language Models for Enhanced Engagement and Learning
Theme: Digital Learning
Author: Carlos J. Costa, Manuela Aparício, João Tiago Aparício
Keywords: gamification; LLM; Human-AI interaction; Artificial Intelligence; framework.
This study investigates the synergy between gamification and large language models (LLMs) with human-AI interaction. Motivated by the need to enhance user engagement and learning outcomes, the research addresses how gamification principles can improve the design of LLM-powered systems. This work proposes a framework integrating game mechanics such as narrative-driven interactions, adaptive challenges, personalized feedback, and collaborative problem-solving into LLM applications. Using a mixed-method approach combining conceptual analysis and software prototyping, we illustrate how gamified LLMs enhance motivation, foster trust, and improve performance in diverse contexts, including education, research, and therapy. The findings accentuate the transformative potential of gamification in human-AI collaboration, with implications for designing more intuitive and effective systems.
Costa, C. J., Aparício, M., & Aparício, J. T. (2025). Gamifying Human-AI Interaction: Using Large Language Models for Enhanced Engagement and Learning. In IEEE ICHMS 2025: 5th IEEE International Conference on Human-Machine Systems (pp. 325-331). IEEE Press. https://doi.org/10.1109/ICHMS65439.2025.11154284
