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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.04645 |
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| _version_ | 1866909368717410304 |
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| author | Do, Tiffany D. Shafqat, Usama Bin Ling, Elsie Sarda, Nikhil |
| author_facet | Do, Tiffany D. Shafqat, Usama Bin Ling, Elsie Sarda, Nikhil |
| contents | 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 learning styles. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_04645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PAIGE: Examining Learning Outcomes and Experiences with Personalized AI-Generated Educational Podcasts Do, Tiffany D. Shafqat, Usama Bin Ling, Elsie Sarda, Nikhil Human-Computer Interaction 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 learning styles. 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. |
| title | PAIGE: Examining Learning Outcomes and Experiences with Personalized AI-Generated Educational Podcasts |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.04645 |