## 3 Results
### Expanding Learning Analytics Through Generative AI
#### 3.3.1 Generative AI Applications for Engagement, Feedback, and Adaptation  
The growing integration of generative AI (GenAI) into learning analytics (LA) underscores a methodological shift toward transformer-based models, primarily GPT-3.5, GPT-4, BERT, FLAVA, and Whisper. Frequently, these models operate within hybrid frameworks combining supervised classifiers (e.g., SVM, random forest, logistic regression) and unsupervised techniques, such as clustering and topic modeling. Among the 14 reviewed studies, GPT and BERT variants dominate, occasionally supported by recurrent neural networks (RNNs) or long short-term memory (LSTM) networks. Classical methods like decision trees and naive Bayes are occasionally used for simpler classification tasks [97]. However, the general absence of lightweight models raises scalability and equity concerns, particularly for low-resource educational contexts. Quantitative analysis reveals GPT-4 as the most widely used model, followed by BERT and other GPT variants. Diverse methodologies, including neural networks and clustering algorithms, highlight varied applications, ranging from engagement analytics and adaptive feedback to personalized learning experiences. Large language models (LLMs) facilitate real-time reflective scaffolding [98], the detection of inclusive interactions [99], personalized feedback [100], and improved learner engagement in classroom integration contexts [101]. Some longitudinal research indicates that interaction frequency with GenAI tools enhances learner autonomy via increased social presence [102], yet rigorous empirical evidence on instructional effectiveness or scalability remains sparse. Mixed-method approaches, integrating digital learning traces, student-generated texts, GenAI interactions, and multimodal data (audio, video, gesture), are prevalent. NLP techniques, especially sentiment analysis and topic modeling using transformers, effectively analyze textual data from learners and AI-generated content [103; 104]. Clustering, combined with GPT/BERT embeddings, supports learner profiling and targeted feedback strategies [105]. Sequential models, including RNNs and LSTMs, aid the analysis of trajectories related to self-regulated learning and help-seeking behaviors [101; 106]. While explainable AI (XAI) is gaining attention, its application remains limited. [PERSON] [107] uniquely integrates GPT-4 with semantic knowledge graphs to enhance explainability. However, broader transparency and accountability issues persist, amplifying ethical concerns such as hallucination and trustworthiness. Key applications include:  
* Formative feedback and automated assessment [100; 108];
* Adaptive prompts supporting self-regulated learning [98];
* Visual analytics via GenAI-enhanced dashboards [107];
* Equity-aware collaborative learning tools [99];
* Detection of help-seeking behaviors [109];
* Adaptive content delivery in gamified or flipped learning contexts [101]. Challenges and opportunities: Although GenAI offers considerable promise, significant limitations remain. The predominant opacity of GenAI systems hinders interpretability, with rare exceptions employing explainability frameworks [107]. Additionally, the frequent misalignment with self-regulated learning (SRL) principles risks fostering metacognitive passivity, as discussed in existing critiques [110]. Real-time adaptability is another concern, as many GenAI tools provide delayed or static feedback, inadequately addressing immediate learner disengagement--though real-time adaptive scaffolding offers potential solutions [98]. The underutilization of multimodal inputs, such as audio, video, or physiological data, limits the comprehensive understanding of learner behavior and con text [99, 103]. Lastly, weak theoretical grounding and limited alignment with established instructional frameworks diminish practical pedagogical effectiveness, highlighting the need for evidence-centered design approaches [106].