## 4 Findings and Discussions
### Research Trends and Patterns
#### 4.2.2 Analysis of the Abstract and Keywords
[42] used data mining algorithms for customizing learning environments to create personalized learning for a creativity course, and participants in their study achieved a 90% chance of having higher-than-average scores in the personalized learning environment designed with the help of AI. Similarly, [PERSON] and [PERSON] [43] utilized AI for the planning and construction of personalized e-learning routes for learners by creating learning objects, and their findings showed that using AI for planning the study of learners makes it easier to have a personalized learning route for learners. Likewise, many other studies show that utilizing AI and machine learning through EDM and LA brings success to the planning and construction of personalized learning environments, especially in e-learning. In sum, AI has empowered EDM and LA approaches and paved the way for adaptive and personalized learning. **Algorithmic online educational spaces, ethics, and human agency** (see the connected paths in Figure 5: _AI, intelligence, learning, model, algorithms_ and _learning, machine, automatically, method, classify, training, data, extract_ and _human, educational, knowledge, environment_; see the connected nodes in Figure 5: _neural networks, AI, machine learning, online learning, deep learning, sentiment analysis, recommender system, e-learning_). The second theme identified was algorithmic online educational spaces, ethics, and human agency. Algorithmic online educational spaces refer to learning environments that utilize AI and machine learning techniques for implementation and design of the learning environment. Using AI has advantages in creating a success path for learners. AI can provide the learner an enhanced learning experience with personalized features leading to better learning outcomes [44]. AI-driven learning experiences in online learningenvironments, such as massive online open courses (MOOCs), utilize machine learning algorithms (ML) to track learner actions for enhancing learners' performance. Processing large amounts of learning data with ML algorithms can clarify the relationship between learning behavior and effectiveness and create efficient paths with a recommender system making decisions for learners in online learning [45]. However, there are serious constraints on the way these algorithms function when using educational data to make decisions [46]. Algorithmic decision-making should be considered within the social structure which algorithms are designed for and function in [47]. Studies in this context show that the data used to train models for prediction of educational data in AI-driven systems may have strong bias and create inequalities in online education [48; 49; 47]. In AI-driven systems, the quality of data used by the algorithms can be poor or biased, or unjustified decisions can be made. The decisions may also lack equality and fairness for social groups [7; 50]. The unequal or unfair decisions made by algorithms are not easy to track. In short, AI-driven learning designs utilizing ML algorithms to provide better learning experiences can also have ethical problems when functioning due to the algorithms' way of working and create inequalities for learners in online learning environments. Finally, it is perhaps more important to ask, if we design a learning environment based on AI-powered decision-making processes, what is the value of human agency, and how should we position learners in an algorithmically woven learning space? **Online learning through detection, identification, recognition, and prediction** (see the connected paths in Figure 5: _teaching, online, students, recognition_ and _recommendation, techniques, learning, based, analysis_ and _learning, deep, detection, emotion_; see the connected nodes in Figure 5: _machine learning, classification, prediction_). The third theme revealed was online learning through detection, identification, recognition, and prediction.