Artificial intelligence (AI) and multimodal data (MMD) are gaining popularity in education for their ability to monitor and support complex teaching and learning processes. This line of research and practice was recently named Multimodal Learning Analytics (MMLA). However, MMLA raise serious ethical concerns given the pervasive nature of MMD and the opaque AI techniques that process them. This study aims to explore ethical concerns related to MMLA use in higher education and proposes a framework for raising awareness of these concerns, which could lead to more ethical MMLA research and practice. This chapter presents the findings of 60 interviews with educational stakeholders (39 higher education students, 12 researchers, 8 educators, and 1 representative of an MMLA company). A thematic coding of verbatim transcriptions revealed nine distinct themes. The themes and associated probing questions for MMLA stakeholders are presented as a draft of the first ethical MMLA framework. The chapter is concluded with a discussion of the emerging themes and suggestions for MMLA research and practice.
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Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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