Quantifying the benefits to students of faking your fluency in English in recorded lectures

Authors

DOI:

https://doi.org/10.52041/iase25.102

Abstract

Heavily accented speech can hinder learning, particularly for non-English speaking students who struggle with comprehension and engagement. This study explores whether AI-generated voice-modded lectures, mimicking native English speech, improve academic outcomes and engagement in lectures by non-native English-speaking instructors. The study was conducted in an Introductory Statistics large enrolment service unit and compared student performance across original and AI-enhanced lectures. Surveys were used to collect engagement feedback from both domestic and international students. No significant performance difference was observed for international students. However, domestic students showed improved marks when exposed to synthetic lectures, highlighting this intervention's low-cost, scalable impact. This research offers practical insights into using AI voice-modding to improve engagement and equity in large, diverse classes.

References

Banerjee, A. V., Banerji, R., Berry, J., Duflo, E., Kannan, H., Mukerji, S., Shotland, M., & Walton, M. (2016). Mainstreaming an effective intervention: Evidence from randomized evaluations of ‘Teaching at the Right Level’ in India. MIT Department of Economics Working Paper No. 16-08. SSRN. http://doi.org/10.2139/ssrn.2846971

Ben-Zvi, D. (2016). Three paradigms in developing students' statistical reasoning. In S. Estrella, M. Goizueta, C. Guerrero, A. Mena, J. Mena, E. Montoya, A. Morales, M. Parraguez, E. Ramos, P. Vásquez & D. Zakaryan (Eds.), XX Actas de las Jornadas Nacionales de Educación Matemática (pp. 13–22). SOCHIEM. https://www.sochiem.cl/documentos/actas-jnem/2016- valparaiso-xx-pucv.pdf

Binkowski, K. P. (2023). Mastery of learning - does it make a difference to students’ online engagement and performance in a first-year statistics unit?. In E. M. Jones (Ed.), Fostering Learning of Statistics and Data Science - Proceedings of the Satellite conference of the International Association for Statistical Education (IASE). ISI/IASE. https://doi.org/10.52041/iase2023.107

Croke, K., & Atun, R. (2019). The long run impact of early childhood deworming on numeracy and literacy: Evidence from Uganda. PLOS Neglected Tropical Diseases, 13(1). https://doi.org/10.1371/journal.pntd.0007085

McClure, K. L., & Chen, H.-T. M. (2024). “I could not understand anything they said!”: Non-native English-speaking instructors, online learning, and student anxiety. Psychology of Language and Communication, 28(1), 233–260. https://doi.org/10.58734/plc-2024-0010

Sabharwal, M. (2011). Job satisfaction patterns of scientists and engineers by status of birth. Research Policy, 40(6), 853–863. https://doi.org/10.1016/j.respol.2011.04.002

Thurn F. (2023, January 25). Enhancing ‘teacher presence’ to improve online engagement. TECHE Macquarie University’s learning and teaching blog. https://teche.mq.edu.au/2023/01/enhancing- teacher-presence-to-improve-online-engagement/.

Zhao, G., Ding, S., & Gutierrez-Osuna, R. (2021). Converting foreign accent speech without a reference. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29, 2367–2381. https://doi.org/10.1109/TASLP.2021.3060813

Downloads

Published

2026-02-21

Conference Proceedings Volume

Section

Topic 5: Innovating and Expanding the Boundaries in Statistical and Data Science Education