PAIGE: Examining Learning Outcomes and Experiences with Personalized AI-Generated Educational Podcasts

要旨

Generative AI is revolutionizing content creation and has the potential to enable real-time, personalized educational experiences. We investigated the effectiveness of converting textbook chapters into AI-generated podcasts and explored the impact of personalizing these podcasts for individual learner profiles. We conducted a 3x3 user study with 180 college students in the United States, comparing traditional textbook reading with both generalized and personalized AI-generated podcasts across three textbook subjects. The personalized podcasts were tailored to students’ majors, interests, and self-described instructional preferences. Our findings show that students found the AI-generated podcast format to be more enjoyable than textbooks and that personalized podcasts led to significantly improved learning outcomes, although this was subject-specific. These results highlight that AI-generated podcasts can offer an engaging and effective modality transformation of textbook material, with personalization enhancing content relevance. We conclude with design recommendations for leveraging AI in education, informed by student feedback.

受賞
Best Paper
著者
Tiffany D.. Do
Drexel University, Philadelphia, Pennsylvania, United States
Usama Bin Shafqat
Google, New York, New York, Pakistan
Elsie Ling
Google, Mountain View, California, United States
Nikhil Sarda
Google, Mountain View, California, United States
DOI

10.1145/3706598.3713460

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713460

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Recommendation and Personalization

G401
7 件の発表
2025-04-29 20:10:00
2025-04-29 21:40:00
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