## 3 Results
### Expanding Learning Analytics Through Generative AI
#### 3.3.2 Technical Approaches and Emerging Trends in GenAI for Learning Analytics  
The integration of generative AI (GenAI) into learning analytics (LA) is significantly reshaping methodological frameworks in higher education. Across twelve studies [111, 112, 113], large language models (LLMs), including GPT-4, GPT-4o, DistilBERT, and Gemini 1.5 Pro, have emerged as central tools for tasks such as feedback generation, affective state detection, and instructional scaffolding. Despite broad usage, most implementations remain preliminary, lacking comprehensive validation in real-world educational environments. Furthermore, the influence of instructional context on model effectiveness and learning outcomes remains underexamined. GPT-4 variants dominate, appearing in at least eight studies, often integrated with emotion detection tools (HSEmotion), facial recognition (MTCNN), or generative adversarial networks (GANs) for enhanced multimodal analysis [114]. DistilBERT specifically excels in detecting confusion within discourse data [112]. Applications leveraging ChatGPT and GitHub Copilot have effectively captured learner strategies in coding activities [113]. Additionally, collaborative interactions supported by GPT-4 have been linked to improved hint quality and heightened critical thinking [115], while CustomGPT offers tailored insights by analyzing interactions with digital educational resources [116]. Application domains are concentrated mainly around engagement analytics, automated feedback, and risk detection. GPT-driven dashboards and conversational agents provide adaptive support and multimodal instructional scaffolding in real-time interactions [117, 118]. Nevertheless, the recurrent usage of basic LLM pipelines--primarily prompt engineering combined with simple classifiers--highlights limitations in methodological innovation and adaptation to diverse instructional contexts. The optimization of LLM outputs typically involves fine-tuning and strategic prompt design. For instance, GPT-4o has been customized to interpret natural language queries and generate educationally relevant SQL queries and visualizations [118]. Similarly, DistilBERT has successfully facilitated efficient confusion classification in MOOCs [112], while GAN-based techniques have enriched otherwise sparse datasets [114]. Some studies prioritize explainability. For instance, NLP and ChatGPT have been utilized to uncover patterns in collaboration and bias in peer-generated feedback [119]. Furthermore, GitHub Copilot interaction logs have been employed to track cognitive transitions during learning activities [113]. However, instances of feedback inaccuracies or reinforcement of misconceptions indicate the crucial role of human oversight [120]. Hybrid methodologies that combine clustering, regression, and sequential analysis with NLP techniques frequently analyze multimodal data, including LMS logs, reading behaviors, keystrokes, and emotional inputs [111, 121]. Although explainable AI methods such as integrated gradients and anchors have been incorporated, their practical impact on instructional decision-making remains inadequately assessed [92, 112]. Key applications include:  
* Automated formative feedback in STEM disciplines through GPT4 and ChatGPT [113, 115, 122];
* Real-time confusion detection in online learning environments using DistilBERT [112];
* Multilingual engagement analytics leveraging bilingual prompts and chat logs [120, 121];
* Bias detection and fairness evaluation in peer feedback through explainable GenAI models [119]. Challenges and opportunities: Despite promising advances, significant challenges persist. Many GenAI implementations focus predominantly on retrospective evaluation rather than proactive, real-time instructional utility. Although explainability methods are increasingly employed, their pedagogical grounding and effectiveness in supporting actual learning processes remain unclear.