A generative AI educational game framework with multi-scaffolding supports workplace competency development

Abstract

This study proposes a game design framework using generative artificial intelligence based non-player character for providing simulated interactions and instant feedback as a metacognitive scaffolding to help learners develop workplace communication skills and stress resistance through situational experience learning. This study investigates learning effectiveness and psychological responses, including flow, perceived fidelity, cognitive load, and qualitative feedback, and specifically analyzes the behavioral patterns of learners interacting with generative artificial intelligence.

A total of 91 participants were enrolled in this study and divided into three groups: experimental group 1 (generative artificial intelligence interactive metacognitive scaffolding), experimental group 2 (video metacognitive scaffolding), and control group (text metacognitive scaffolding). The results showed that students in the generative artificial intelligence group responded with higher perceived fidelity and were significantly better than the control group with text-based metacognitive scaffolding in terms of learning effectiveness, flow, and germane cognitive load. Behavioral pattern analysis reveals that learners can effectively obtain a lot of positive help in solving tasks through positive interactions with non-player character chatbot.

This framework and the findings of the study can be used as a reference for related studies in the field of game-based learning in the use of metacognitive scaffolding, contextual learning, and generative AI. Due to the short experimental period, the results may only reflect the short-term learning effects, and the novelty of Gen AI may also cause bias in affecting the learning outcomes, future studies can increase the duration or number of experiences, and explore the effectiveness in other professional domains.