%0 Conference Paper %1 10.1145/3563657.3596112 %A Draxler, Fiona %A Schmidt, Albrecht %A Chuang, Lewis L. %B Proceedings of the 2023 ACM Designing Interactive Systems Conference %C New York, NY, USA %D 2023 %I Association for Computing Machinery %K sfbtrr161 2023 c06 %P 2249–2262 %R 10.1145/3563657.3596112 %T Relevance, Effort, and Perceived Quality: Language Learners’ Experiences with AI-Generated Contextually Personalized Learning Material %U https://doi.org/10.1145/3563657.3596112 %X 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. %@ 9781450398930

@inproceedings{10.1145/3563657.3596112, 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.}, added-at = {2024-04-12T11:19:01.000+0200}, address = {New York, NY, USA}, author = {Draxler, Fiona and Schmidt, Albrecht and Chuang, Lewis L.}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2162ad71a35e7c9abc75583e314f84319/sfbtrr161}, booktitle = {Proceedings of the 2023 ACM Designing Interactive Systems Conference}, doi = {10.1145/3563657.3596112}, interhash = {39c5efa2a65b513d345f21eae5014144}, intrahash = {162ad71a35e7c9abc75583e314f84319}, isbn = {9781450398930}, keywords = {sfbtrr161 2023 c06}, location = {<conf-loc>, <city>Pittsburgh</city>, <state>PA</state>, <country>USA</country>, </conf-loc>}, numpages = {14}, pages = {2249–2262}, publisher = {Association for Computing Machinery}, series = {DIS '23}, timestamp = {2024-04-12T11:19:01.000+0200}, title = {Relevance, Effort, and Perceived Quality: Language Learners’ Experiences with AI-Generated Contextually Personalized Learning Material}, url = {https://doi.org/10.1145/3563657.3596112}, year = 2023 }