Date
Publisher
arXiv
Due to their interactive nature, serious games offer valuable opportunities for supporting learning in educational contexts. Recent advances in large language models (LLMs) have further opened the door to new forms of personalized scaffolding in education. In this study, we combine both worlds and study three types of AI-based scaffolding designs in a serious game: (i) no scaffolding, (ii) chat-based (verbal) scaffolding provided by an AI-based non-player character (NPC), and (iii) combined chat-(verbal) and action-based (visual) scaffolding in which the AI may both try to explain or demonstrate the next step towards a solution. The scaffolding conditions are embedded in Qookies, a serious game designed to introduce fundamental concepts of quantum technologies. A total of 152 school students, university students, and members of the general public were randomly assigned to one of the three conditions. The results show that all groups experience significant learning gains, confirming the overall effectiveness of the serious game itself. No significant differences in learning outcomes emerged between scaffolding conditions. However, intrinsic cognitive load was lower in the combined chat-and-action (verbal+visual) scaffolding condition compared to the chat (verbal)-only condition, suggesting that visual demonstrations may offer more accessible support. Interaction analyses further revealed that players engaged with the AI character primarily for level-related questions and action recommendations, while deeper interactions were relatively rare.
Study at a glance
- Question: How do verbal and visual AI scaffolding affect student learning and cognitive load in a serious educational game?
- Setting and data: 152 school students, university students, and members of the public playing a quantum technology game on computers.
- Method: Randomized controlled trial with three conditions, pre- and post-tests, self-reported cognitive load surveys, and chat log analysis.
Why it matters
Educational games require appropriate instructional support without reducing active problem solving. AI-based non-player characters could personalize help, but their educational effectiveness and effects on cognitive load remain uncertain.
Key findings
- Participants across all three scaffolding conditions achieved significant learning gains, with no significant differences in conceptual test scores between conditions.
- Intrinsic cognitive load was significantly lower for learners given combined verbal and visual AI scaffolding compared to verbal-only scaffolding.
- Learners interacted with the AI character primarily to ask level-specific questions and seek action recommendations, whereas deeper discussions were rare.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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