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2021 | OriginalPaper | Chapter

Scrutability, Control and Learner Models: Foundations for Learner-Centred Design in AIED

Author : Judy Kay

Published in: Artificial Intelligence in Education

Publisher: Springer International Publishing

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Abstract

There is a huge, and growing, amount of personal data that has the potential to help people learn. There is also a growing and broad concern about the ways that personal data is harvested and used. This makes it timely to draw on the decades of AIED research towards creating systems and interfaces that enable learners to truly harness and control their learning data. This invited keynote will present a whirlwind tour of my learner modelling research and a selection of other work that has influenced my own towards the goal of putting people in control of their own learning data and its use. I will explain the rationale for my focus on scrutability, as a foundation for users to harness and control their learning data, especially for learning contexts.
I will share key lessons from my work for creating AIED systems that are deeply learner centred. Building on this, I will present a vision for AIED, one that takes a learner-centred perspective to designing AIED systems and recognises the inherent limitations of learning data. This is a broad view of AIED that returns its founding goals to create advanced learning technologies.

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next chapter Open Learner Models for Multi-activity Educational Systems
Footnotes
1
European Union’s General Data Protection Regulation (GDPR) https://​gdpr-info.​eu/​.
 
5
Of course, it is the nature of AIED systems that the learner may be more or less self-aware and they may be more or less honest about sharing their self-assessment with the system.
 
6
Where accretes means that it builds up over time − in my work [10, 11], this means that timestamped raw data is added to the learner model, potentially triggering inferences that add more timestamped inferred data which also accretes.
 
Literature
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Czarkowski, M., Kay, J.: Giving learners a real sense of control over adaptivity, even if they are not quite ready for it yet. In: Advances in Web-Based Education: Personalized Learning Environments, pp. 93–126. IGI Global (2006)
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Eiband, M., Buschek, D., Hussmann, H.: How to support users in understanding intelligent systems? Structuring the discussion. In: 26th International Conference on Intelligent User Interfaces, pp. 120–132 (2021)
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Guerra-Hollstein, J., Barria-Pineda, J., Schunn, C.D., Bull, S., Brusilovsky, P.: Fine-grained open learner models: complexity versus support. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 41–49 (2017)
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Kay, J.: Learner know thyself: student models to give learner control and responsibility. In: Proceedings of International Conference on Computers in Education, pp. 18–26 (1997)
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Kay, J.: A scrutable user modelling shell for user-adapted interaction. Ph.D. thesis, Basser Department of Computer Science, Faculty of Science, University of Sydney (1998)
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Kay, J., Kummerfeld, B.: Creating personalized systems that people can scrutinize and control: Drivers, principles and experience. ACM Trans. Interact. Intell. Syst. (TiiS) 2(4), 1–42 (2013)
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Kay, J., Kummerfeld, B.: From data to personal user models for life-long, life-wide learners. Br. J. Edu. Technol. 50(6), 2871–2884 (2019)CrossRef
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Long, Y., Aleven, V.: Enhancing learning outcomes through self-regulated learning support with an open learner model. User Model. User-Adap. Inter. 27(1), 55–88 (2017)CrossRef
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Mitrovic, A., Martin, B.: Evaluating the effect of open student models on self-assessment. Int. J. Artif. Intell. Educ. 17(2), 121–144 (2007)
Metadata
Title
Scrutability, Control and Learner Models: Foundations for Learner-Centred Design in AIED
Author
Judy Kay
Copyright Year
2021
Book
Artificial Intelligence in Education

Print ISBN: 978-3-030-78269-6

Electronic ISBN: 978-3-030-78270-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-78270-2_1

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