Research on AI-Driven Adaptive Difficulty and Feedback Mechanisms in Serious Games: A Case Study of Complex Procedural Skills Training
Year: 2025
Type: article
Abstract: Artificial Intelligence (AI) brings new chances for personalized and adaptive learning in Serious Games (SGs). This paper systematically analyzes recent research on AI - based adaptive difficulty and feedback systems in serious games for training complex procedural skills. It starts with key concepts of serious games, procedural skill learning and adaptivity principles. Then, it explores major AI technologies like machine learning, reinforcement learning, natural language processing and generative AI in developing learning models and adaptivity options. The paper presents practical uses and benefits of adaptive systems in medical simulation and technical training, and looks into evaluation challenges of adaptive serious games in skill transfer assessment. It points out research challenges such as complex implementation and ethical issues like data privacy and algorithmic bias, and underlines the need for Explainable AI (XAI) before giving future research opportunities. The study shows AI - driven adaptive systems have great potential to enhance procedural skills training, but their full effectiveness relies on more research in skill transfer validation, integrated model design, explainability and ethical governance.
Author: Hengran Yang
Institution: Kyonggi University
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4412370799