This research project introduces an AI-powered, web-based serious game platform designed to enhance software developers' understanding of the General Data Protection Regulation (GDPR). By integrating adaptive difficulty and real-time feedback mechanisms through reinforcement learning and ChatGPT, the platform bridges the gap between theoretical knowledge and practical implementation. A study among software developers showed improved GDPR knowledge, engagement, and motivation to adopt privacy-preserving practices.

In the rapidly evolving digital landscape, data breaches continue to pose significant threats, exposing users to privacy risks and causing substantial financial losses to organizations. In 2023, the United States witnessed 3,205 data compromises affecting over 353 million individuals, marking a 78% increase from the previous year. The global average cost of a data breach in 2024 reached $4.88 million.

These incidents highlight the urgent need for implementing robust privacy-preserving mechanisms. Although methodologies such as Data Minimization (DM), Fair Information Practices (FIP), Privacy by Design (PbD), and regulatory frameworks like the General Data Protection Regulation (GDPR) have been introduced, the lack of awareness and training among developers has resulted in insecure coding practices. Developers often prioritize functionality over security due to time constraints and limited knowledge of privacy techniques.

To address these challenges, researchers have emphasized the need for comprehensive privacy training for developers. However, traditional training methods such as lectures and documentation often fail to engage learners, leading to poor knowledge retention and limited real-world application.

Consequently, there is growing interest in exploring more engaging educational approaches. Serious games are increasingly used to improve knowledge retention. Although prior frameworks, such as the serious game model by Arachchilage and Hameed, and its extension by Alhazmi, introduced GDPR-focused game-based learning tools, they lacked mechanisms to sustain player engagement, such as adaptive feedback and dynamic difficulty adjustment.

Building on this foundation, our study enhances the existing framework by introducing two key mechanisms: adaptive difficulty and interactive feedback. Adaptive difficulty is achieved through Reinforcement Learning (Q-learning), which adjusts question levels based on player performance to maintain an optimal challenge. Interactive feedback is delivered through Large Language Models (LLMs), such as ChatGPT, providing real-time, personalized responses to improve understanding and motivation.

The proposed game framework integrates GDPR principles with adaptive learning and real-time AI support to enhance developers' engagement, knowledge retention, and motivation to implement privacy-preserving software practices.

Numerous studies have highlighted the difficulties developers face in implementing privacy-preserving systems, including challenges in interpreting abstract guidelines and assessing privacy risks from the user’s perspective. Researchers like Sheth, Ayalon, and Oetzel emphasize the lack of practical frameworks to guide developers, pointing to a need for systematic approaches that embed privacy directly into the software development process.

Although frameworks such as Privacy by Design exist, they are not widely adopted due to knowledge gaps and misconceptions—many developers still confuse privacy with security. Arachchilage et al. introduced a game-based framework using Bloom’s taxonomy and data minimization models but lacked empirical validation and adaptive learning features. Alhazmi and Arachchilage later extended this work to include all GDPR principles and validated its effectiveness through a gamified browser-based tool, showing positive impacts on developers' GDPR knowledge.

Despite these advancements, existing frameworks still lack critical components like real-time feedback and adaptive difficulty, which are essential for maintaining engagement. Literature from fields like cybersecurity and education demonstrates the effectiveness of serious games and adaptive learning mechanisms in improving retention and motivation. Reinforcement Learning (Q-learning) and Large Language Models (ChatGPT) have shown promise in dynamically adjusting game difficulty and providing personalized feedback.

Together, these studies support the need for a comprehensive, AI-driven serious game that combines GDPR education with adaptive learning and personalized feedback to enhance developer engagement, understanding, and compliance.

Our AI-powered serious game effectively improves GDPR knowledge, user engagement, and developer motivation. Adaptive difficulty and personalized feedback mechanisms were especially impactful in enhancing learning outcomes and retention.

Publications will be added here once the research has been published.