Relevance, Effort, and Perceived Quality: Language Learners’ Experiences With AI-Generated Contextually Personalized Learning Material

MCML Authors

Albrecht Schmidt

Prof. Dr.

Principal Investigator

Human-Centered Ubiquitous Media

Abstract

Artificial intelligence has enabled scalable auto-creation of context-aware personalized learning materials. However, it remains unclear how content personalization shapes the learners’ experience. We developed one personalized and two non-personalized, crowdsourced versions of a mobile language learning app: (1) with personalized auto-generated photo flashcards, (2) the same flashcards provided through crowdsourcing, and (3) manually generated flashcards based on the same photos. A two-week in-situ study (n = 64) showed that learners assessed the quality of the non-personalized auto-generated material to be on par with manually generated material, which means that auto-generation is viable. However, when the auto-generation was personalized, the learners’ quality rating was significantly lower. Further analyses suggest that aspects such as prior expectations and required efforts must be addressed before learners can actually benefit from context-aware personalization with auto-generated material. We discuss design implications and provide an outlook on the role of content personalization in AI-supported learning.

inproceedings

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DIS 2023

ACM Conference on Designing Interactive Systems. Pittsburgh, PA, USA, Jul 10-14, 2023.

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Authors

F. Draxler • A. Schmidt • L. L. Chuang

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Links

DOI

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Research Area

 C5 | Humane AI