# AI-Powered System to Facilitate Personalized Adaptive Learning in Digital Transformation
## 2 Related Work
### Knowledge-Driven AI Agents for Helping Personalized Adaptive Learning  
Personalized adaptive learning refers to an educational approach that uses technology and data-driven insights to tailor learning experiences to the unique needs, preferences, and abilities of individual learners [37]. Unlike traditional \"one-size-fits-all\" methodologies, adaptive personalized learning adjusts dynamically to the pace, level of knowledge, and learning style of the student, ensuring that instruction is relevant and effective [38]. The goal of personalized adaptive learning is to optimize educational outcomes by recognizing and accommodating the diversity of learners, thus fostering deeper understanding and long-term retention.  
This approach needs to integrate advanced technologies such as artificial intelligence (AI), machine learning (ML), and real-time analytics to assess a learner's progress and adjust the content or schedules accordingly. Throughout the entire process, providing students with personalized real-time arrangements is the key to the successful implementation of personalized adaptive learning, which requires that the AI system has accurate and relevant knowledge support, and is able to flexibly handle differences between different students in context. In previous research by [39], a successful example was demonstrated in which large language models (LLMs) were utilized as chatbots to deliver personalized adaptive learning features in a practical setting. In the research of [PERSON] et al. [40], the researchers used a transformer-based model that achieved an Area Under the Curve (AUC) of 0.865 in knowledge tracing. Furthermore, in the research in [41], the importance of incorporatingspecific knowledge from both students and teachers into the design of the learning process was emphasized, highlighting its potential to improve adaptability in learning. In addition, some of the latest research [42; 43] has also shown us the great potential of robots and multi-agent in human-machine interaction and examples of effectively promoting learning. More quantitative results were reported by [44], where the research demonstrated that an AI-based tutor significantly improved post-test scores for underperforming math students, with those in the lowest pretest quartile gaining an average of 8.2 points, compared to 3.5 points in the control group, an improvement of over 130%. Another research study [45] used an AI model to generate personalized feedback, and the given model outperformed the baselines, resulting in a 45% improvement in student learning gains.  
To integrate AI models and user-specific knowledge into the learning process, knowledge-driven agents could be a useful tool for the corresponding implementation. AI agents (empowered by LLMs) can play a crucial role in the implementation of personalized adaptive learning by using intensive human-machine interaction to tailor educational experiences to individual needs. With personalized domain-specific knowledge support, they can analyze learner behavior, preferences, and progress to provide customized content and support, fostering more effective and engaging learning outcomes. In addition, by adapting in real time and incorporating feedback from both students and teachers, AI agents improve adaptability, improve learning efficiency, and make education more accessible and inclusive. The latest explorations and attempts to combine agents with personalized adaptive learning have yielded some very successful cases, with especially promising results in adapting to customized knowledge to providing personalized services [46; 47; 48]. In our approach, particular AI agents retrieve the extra relevant knowledge (i.e., from domain-specific knowledge bases) based on the user's requests and use this specific knowledge to improve the performance of LLMs in each task. With this, the system can not only generate personalized responses based on context but also ensure the accuracy of the contents by providing domain-specific and context-related knowledge.