## 4 Findings and Discussions
### Research Trends and Patterns
#### 4.2.2 Analysis of the Abstract and Keywords  
This section proposes broad emerging research themes identified using the text-mining outputs of the abstracts (see Figure 5) and social network outputs of the keywords (see Figure 6). For text mining of the abstracts, the researchers performed a lexical analysis that employs \"two stages of co-occurrence information extraction--semantic and relational--using a different algorithm for each stage\" [18] (p. 262). Thus, text-mining analysis enabled the researchers to identify hidden patterns and visualize them on a thematic concept map. Keywords are the granular representatives, and, for the analysis of the keywords, SNA was used. SNA \"provides powerful ways to summarize networks and identify key people, [entities], or other objects that occupy strategic locations and positions within a matrix of links\" [12] (p. 6). In this study, the keywords were analyzed based on their co-occurrences and visualized on a network graph. By doing this, the authors were able to identify strategic  
Figure 4: t-SNE analysis of the titles of the sampled publications. Figure 3: Subject areas of the publications on AI in online distance education (one publication can be coded for more than one subject area). **Educational data mining, learning analytics, and artificial intelligence for adaptive and personalized learning** (see the connected paths in Figure 5: _AI, intelligence, learning, personalized, system, e-learning, adaptive, environments_; see the connected nodes in Figure 5: _adaptive learning, AI, learning analytics, educational technology, higher education_ and _data mining, machine learning, classification, prediction, online education_ and _online learning, deep learning, e-learning, recommendation system_). The first theme identified by the analysis was educational data mining, learning analytics, and artificial intelligence for adaptive and personalized learning. Educational data mining (EDM) is an emerging interdisciplinary research field dealing with the development of methods for examining educational data [31]. Data mining in education is an alternative for exploring data with the goal of extracting knowledge and useful information to make teaching and learning more efficient [32]. The rapid growth and availability of large volumes of data in education show that the distillation of vast amounts of data requires increasingly sophisticated technologies, such AI and AI-powered algorithms. The field of EDM, which emerged from the aforementioned needs [33; 34] demonstrated a capacity increase with the use of AI technologies. Conventional algorithms for data mining cannot be applied to educational problems directly, because they often have a peculiar function and goal [35] (p. 15991). EDM is concerned with all kinds of data coming from educational environments, which create a base and further promote another concept, dealing with collection, measurement, analysis, and reporting of data about learners and their contexts for the purpose of understanding and optimizing learning and learning environments, known as learning analytics (LA) [36] (p. 2). LA focuses on analysis of data on educational environments to improve teaching and learning activities [37; 38; 39]. [PERSON] et al. [40] categorize LA into two analytic approaches: (1) descriptive analysis, which focuses on the data-based actions learners leave behind when they use digital tools or interact on online platforms, and (2) predictive analysis, which uses learner behavioral data, historical data (e.g., previous course grades), and sociodemographic data to predict educational outcomes such as dropout rates. However, the use of AI raises many critical questions about the role and competencies of teachers and the role of students as autonomous, self-directed learners. In current adaptive learning environments, learning activities occur as individualized, personalized learning experiences [41]. The related literature also supports the above-mentioned arguments. For instance, in their study, [PERSON] et al.