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journal · 2022

Cognitive Load in Multimodal Tutoring Systems

Riya Patel, Abhiraj Bibhar · International Journal of Artificial Intelligence in Education

EdTechHCI

Abstract

Multimodal tutoring systems that combine text, audio, and visual explanation can improve learning outcomes, but they can also increase extraneous cognitive load if poorly designed. Across three classroom studies with a combined 240 students, we measure cognitive load using dual-task response times and find that adaptive modality selection — choosing which modality to emphasize based on a learner’s real-time performance — reduces extraneous load without harming learning gains.

Key contributions

  • Three classroom studies measuring cognitive load in multimodal tutoring, totaling 240 students.
  • An adaptive modality-selection algorithm responsive to real-time learner performance.
  • Evidence that adaptive modality selection reduces load without sacrificing learning outcomes.
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Authors

  • R Riya Patel
  • A Abhiraj Bibhar (this author)

Cite this paper

Patel, R. & Bibhar, A. (2022). Cognitive Load in Multimodal Tutoring Systems. International Journal of Artificial Intelligence in Education.

@article{patel2022cognitive,
  title     = {Cognitive Load in Multimodal Tutoring Systems},
  author    = {Patel, Riya and Bibhar, Abhiraj},
  journal   = {International Journal of Artificial Intelligence in Education},
  year      = {2022}
}