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A Transparency Index Framework for AI in Education

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13356))

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Abstract

Numerous AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI such as fairness, explainability, and safety. Yet, no such work has been done on developing transparent AI systems for real-world educational scenarios. This paper presents a Transparency Index framework that has been iteratively co-designed with different stakeholders of AI in education, including educators, ed-tech experts, and AI practitioners. We map the requirements of transparency for different categories of stakeholders of AI in education. The main contribution of this study is that it highlights the importance of transparency in developing AI-powered educational technologies and proposes an index framework for its conceptualization for AI in education.

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Author information

Authors and Affiliations

  1. University College London, London, UK

    Muhammad Ali Chaudhry, Mutlu Cukurova & Rose Luckin

Authors
  1. Muhammad Ali Chaudhry
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  2. Mutlu Cukurova
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  3. Rose Luckin
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Corresponding author

Correspondence to Muhammad Ali Chaudhry .

Editor information

Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Chaudhry, M.A., Cukurova, M., Luckin, R. (2022). A Transparency Index Framework for AI in Education. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_33

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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