## 2 Materials and Methods
### Data Selection  
To refine the search, advanced filtered queries were used, limiting the results to articles in English. In WoS, searches were performed using the \"Subject\" field (consisting of title, abstract, keyword plus, and other keywords); in Scopus, they were performed using article title, abstract, and keywords; and in dblp, they were performed using manual sets of keywords. The databases were searched for articles using keywords such as \"artificial intelligence,\" \"digital twin,\" \"personalization,\" and \"adaptation\" (Table 2). The selected set of publications was then further checked by manually re-screening the articles, and removing irrelevant items and duplicates, which allowed for determining \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \multicolumn{2}{c}{**Parameter**} & **Description** \\ \hline Inclusion criteria & Articles (original, reviews), books, and chapters up to ten years after publication, including conference proceedings, in English \\ \hline Exclusion criteria & Books older than 10 years, letters, communication, editorials, conference abstracts without full text, and other languages than English \\ \hline Keywords used & artificial intelligence, AI, machine learning, ML, deep learning, DL, digital twin, personalization, adaptation \\ \hline Used field codes (WoS) & “Subject” field (consisting of title, abstract, keyword plus, and other keywords) \\ \hline Used field codes (Scopus) & article title, abstract, and keywords \\ \hline Used field codes (dblp) & Manually \\ \hline Boolean operators used & Yes, e.g., “digital twin” AND (“AI” OR “ML”) AND adaptation \\ \hline Applied filters & Results refined by publication year, document type (e.g., articles, reviews), and subject area (e.g., industry, engineering) \\ \hline Iteration and validation options & Query run iteratively, refinement based on the results, and validation by ensuring relevant articles appear among the top hits \\ \hline Leverage truncation and wildcards used & Used symbols like * for word variations (e.g., “digital twin**”) and? for alternative spellings (e.g., “personalization”) \\ \hline \end{tabular}
\end{table} 
Table 2: Detailed search queries over databases. the final sample size (Figure 1). In the article selection process, we employed a combination of methodological quality assessment tools to ensure their reliability and validity. Tools such as Abstrackr and SWIFT-Active Screener were used to semi-automatically screen abstracts and prioritize studies based on relevance and quality. Citationchaser supported forward and backward citation tracking and structured evidence synthesis, providing a comprehensive and systematic literature review. Additionally, we considered journal impact factors (IFs) and CiteScore quartiles, prioritizing sources from journals in the first and second quartiles to enhance the credibility and scientific value of the reference list. The general summary of the results of the bibliographic analysis is presented in Table 3 and Figures 1-4. The review included 85 articles (2018-2025)--no older ones were included. Articles published before 2018 were excluded because integrating personalization with AI-based DTs in engineering is a relatively new phenomenon. Significant advances in AI/ML and real-time data integration, which enable personalization, have only been widely applied in DTs in the last few years. Including older studies would risk including outdated methods or technologies that do not reflect current capabilities or trends. The 2018-2025 timeframe ensures that the review focuses on the most relevant, innovative, and technically feasible approaches. This limited timeframe also aligns with the rapid development of AI-based engineering solutions, providing a clearer picture of current and emerging best practices. Figure 1: PRISMA flow diagram of the review process. Figure 3: Publications by micro-topic.