# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 4 Discussion of Findings and Implications
### Challenges in Explainability  
This section addresses a critical pattern that cuts across multiple research questions: despite the richness of audio-derived data (RQ2), its combination with other modalities (RQ3), the sophistication of processing techniques (RQ4), and even its intended use for feedback (RQ1 and RQ5), few studies succeed in translating analytics into pedagogical action. As seen in RQ2, researchers extract a wide array of features, from low-level acoustic metrics and speaker diarization to sophisticated NLP indicators, offering a detailed representation of classroom discourse. These features are then processed using machine learning (RQ4), including explainable models such as decision trees and feature attribution methods or with deep learning pipelines that promise high performance but limited interpretability. For audio analytics in particular, misidentified prosodic cues or diarization errors can quickly erode teacher confidence; without transparent rationales, educators are unlikely to trust or act on the model's recommendations. While a few studies do attempt to provide actionable feedback, often through visual dashboards or targeted recommendations, these remain exceptions. The vast majority stop at reporting descriptive indicators, shifting the responsibility for interpretation and pedagogical action entirely to the user. This limitation is particularly evident in RQ1, where feedback provision emerges as one of the most common use cases. However, as shown in RQ5, the feedback offered is typically generic, quantitative, and delivered post hoc. While teachers may receive dashboards displaying metrics such as talk ratios or the frequency of authentic questions, these indicators are seldom accompanied by an explanation of their relevance or instructional implications. Moreover, when feedback is provided, it often lacks evidence of real-world deployment or user adoption, as most systems are confined to academic prototypes and remain untested in practical classroom environments [103]. Embedding a human-in-the-loop (HITL) methodology can address both the need for explainability and meaningful stakeholder involvement. By design, HITL systems require transparent, interpretable outputs so that educators can review, correct, and refine model predictions in real time. This process ensures that each step, from feature extraction to final recommendations, is accompanied by an explanation that teachers understand and trust. Simultaneously, involving teachers (and, when possible, students) in iterative model refinement grounds development in actual classroom practices, leading to tools that align with pedagogical goals and have higher adoption rates. For example, [PERSON] et al. [104] demonstrate usage of GenAI for teaching and learning systems in which instructors monitor and adjust LLM-generated feedback during live sessions, ensuring that suggested interventions remain contextually appropriate. A further challenge lies in the widespread reluctance to make normative claims about what constitutes \"good\" teaching. This hesitation is understandable, given the diversity of educational contexts and pedagogical philosophies. However, in the absence of interpretive scaffolds or evaluative frameworks, feedback risks becoming either meaningless or misinterpreted. Translating analytical outputs into actionable insights would require the integration of explainable models with established pedagogical principles, an approach that remains rare in the current literature. Yet the problem may not lie in the individual contributions of each study but in their isolation. When viewed collectively, the field has already developed many of the building blocks needed for real-world applications. Tools like TeachFX ([[https://teachfx.com](https://teachfx.com)]([https://teachfx.com](https://teachfx.com))) demonstrate that it is possible to deliver personalized, real-time feedback on teacher discourse using audio analytics.