Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps

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Abstract
This systematic review synthesizes 82 peer-reviewed studies published between 2014 and 2024 on the use of audio features in educational research. We define audio features as descriptors extracted from audio recordings of educational interactions, including low-level acoustic signals (e.g., pitch and MFCCs), speaker-based metrics (e.g., talk-time and participant ratios), and linguistic indicators derived from transcriptions. Our analysis contributes to the field in three key ways: (1) it offers targeted mapping of how audio features are extracted, processed, and functionally applied within educational contexts, covering a wide range of use cases from behavior analysis to instructional feedback; (2) it diagnoses recurrent limitations that restrict pedagogical impact, including the scarcity of actionable feedback, low model interpretability, fragmented datasets, and limited attention to privacy; (3) it proposes actionable directions for future research, including the release of standardized, anonymized feature-level datasets, the co-design of feedback systems involving pedagogical experts, and the integration of fine-tuned generative AI to translate complex analytics into accessible, contextualized recommendations for teachers and learners. While current research demonstrates significant technical progress, its educational potential is yet to be translated into real-world educational impact. We argue that unlocking this potential requires shifting from isolated technical achievements to ethically grounded pedagogical implementations.
Funding Information
  • Ministerio de Ciencia e Innovación (TED2021-129300B-I00, PID2021-122466OB-I00)
  • Ministerio de Ciencia e Innovación (TED2021-129300B-I00, PID2021-122466OB-I00)

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