# Key Barriers to Personalized Learning in Times of Artificial Intelligence: A Literature Review
## 3 Results
### Correlational Analysis  
Table 4 shows the correlation between Academic Level, Modality, Technology, and Barrier Categories, where the percentage indicates the proportion within the academic level category. These correlations reveal how different variables relate to each other in the context of personalized learning, showing specific patterns for each educational level. It is noteworthy that percentages are calculated concerning the total number of studies at each academic level. Also, some minor categories (such as Face-to-face, M-learning, other technologies and barriers) have been omitted for clarity, and finally, a study may use  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Academic Level** & **E-Learning** & **B-Learning** & **AI** & **OP** & **LMS** & **Technol. ** & **Pedag. ** & **Psychol. ** \\ \hline Higher & 63.60\% & 31.80\% & 59.10\% & 27.30\% & 31.80\% & 63.60\% & 40.90\% & 27.30\% \\ Secondary & 22.20\% & 66.70\% & 44.40\% & 55.60\% & 22.20\% & 44.40\% & 55.60\% & 33.30\% \\ Elementary & 12.50\% & 50.00\% & 50.00\% & 50.00\%multiple technologies or mention multiple barriers, so percentages in these categories may sum to more than 100%. The analysis indicates that learning modality varies significantly by educational level. E-learning is predominant in higher and continuous education, whereas blended learning (B-learning) is more common in secondary and elementary education. The use of technology also follows distinct patterns: while artificial intelligence is widely employed at all levels, it is especially prominent in higher education. In contrast, other platforms are more evenly distributed across different educational levels. Additionally, Learning Management Systems are more frequently used in higher education, likely due to greater technological infrastructure in this sector. Regarding barriers, we see that technological barriers are more frequent in higher and continuous education, while pedagogical barriers are more common in secondary and elementary education. Furthermore, psychological barriers seem to be more relevant in secondary and elementary education than at other levels. The quantitative analysis of personalized learning implementation across various educational levels reveals significant correlations between academic level, learning modality, technology utilization, and encountered barriers. These correlations provide valuable insights into the diverse approaches and challenges in PL adoption across the educational spectrum. In the realm of higher education, a distinctive pattern emerges, characterized by a predominant adoption of E-learning modalities and a pronounced utilization of artificial intelligence technologies. This predilection for E-learning, accounting for 63.6% of higher education studies, suggests a strong inclination towards digital and remote learning environments in tertiary education. Concurrently, the substantial employment of AI technologies (59.1%) in this sector indicates a trend towards sophisticated, data-driven personalization strategies. The prevalent use of Learning Management Systems in higher education, surpassing other educational levels, further corroborates the sector's inclination towards technologically advanced learning ecosystems. However, this technological emphasis is not without its challenges, as evidenced by the high frequency of technological barriers (63.6%) reported in higher education PL implementations. Contrastingly, secondary and elementary education exhibit distinct characteristics in their approach to PL. In these sectors, Blended learning (B-learning) emerges as the predominant modality, with 66.7% of secondary education studies and 50.0% of elementary education studies employing this approach. This preference for B-learning may reflect an attempt to balance the benefits of technology-enhanced learning with traditional face-to-face instruction, particularly crucial for younger learners.