# AI-Powered System to Facilitate Personalized Adaptive Learning in Digital Transformation
## 4 Results and Discussion
### Test with Customized Knowledge Update  
The discussed AI system aims to help personalized adaptive learning in a digital transformation context. This goal requires an excellent learning process, with comprehensive knowledge integration in multiple domains. Taking into account the diversity of student backgrounds and the many cross-domain challenges in learning, a personalized adaptive learning approach will be widely adopted in the system to improve learning efficiency and reduce cross-regional and cross-domain challenges. This means that the knowledge we use needs to be customized with each user and be dynamically updated based on the requests of users. In this section, we will discuss the results related to the customized knowledge updates and their importance. To assess the significance and effectiveness of personalized knowledge updates in practical applications, we performed an experimental evaluation using a real-world scenario of study plan creation. In this study, the system processed 100 anonymized resumes and generated customized study plan recommendations tailored to the corresponding students based on the content of each resume. The recommendations were designed to provide preparatory guidance for a specific course. To evaluate the quality of these recommendations, we established five key assessment criteria based on expert opinions, which were used to measure user satisfaction and assign performance scores. The anonymized resume data were downloaded from the Kaggle Resume Dataset, which was obtained by scraping individual resume examples from the www.livecareer.com, accessed on 29 April 2025 [67]. Figure 10: Comparison of LLM performance in various domains. Blue curve: The performance of the LLM with knowledge support; Red curve: the performance of the LLM without knowledge support (based on F1 score). In our test, we used the system as an AI assistant to help new students with suitable study strategies, recommended based on their profiles. The experiment procedure included the following steps:  
* Data Preparation: Anonymized resume data available online were downloaded and used to simulate student registration processes to initiate the program. * Personal Knowledge Extraction: The resume and personal information of each student were entered into the system to provide a basic dataset for personalized recommendations. * Profile and Customized Knowledge Update: The system was tasked with retrieving the most suitable user profile knowledge and relevant domain-specific knowledge (i.e., description of the course module) from the knowledge base. This data were used to prepare a customized learning plan tailored to the individual's background and interests. All domain-specific knowledge was extracted from relevant literature documents and stored in the system knowledge base. The user could ask the system to extend this customized knowledge by updating more files or inputs at any time. * Knowledge Integration and Recommendation: Based on domain-specific knowledge and the student profile, the system was requested to generate advice and compile a report detailing possible suggestions for the student. * Interactive Guidance: Finally, the system interacted with the students to address specific questions and provide contextual guidance based on the generated learning plan. The default LLM used in this experiment was Llama 3.1 (8b) [68] and the user request was as follows: \"Based on the applicant's resume and profile, you suggest to the applicant how to prepare for the given course [cloud computing].\" To evaluate the quality of the LLM responses (suggestion), we listed five rules to check if the content satisfied each important aspect based on the learning outcome specification of the given course. The five rules are listed below:  
* Rule0: The suggestion should include a recommended reading list. * Importance: A reading list provides tangible, actionable resources to start learning, and bridges the gap between wanting to study and knowing where to begin.