{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:07:49Z","timestamp":1759334869023,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032000552"},{"type":"electronic","value":"9783032000569"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Teaching Artificial Intelligence (AI) at scale presents unique challenges in designing effective, learner-centered educational experiences. This paper explores how data-driven feedback from learners in a wide-scale platform is used as\u00a0a reference in mapping the strengths and weaknesses perceived\u00a0by learners in Massive Open Online Courses (MOOCs) on AI.\u00a0Using responses based on post-course surveys, we use statistics and Latent Dirichlet Allocation (LDA) to uncover recurring themes in learners\u2019 choices and open-ended comments. The findings reveal that different demographics have different course perceptions, and in general, integrating interactive content is one of the attractions\u00a0to increase learners\u2019 engagement in completing their courses.\u00a0These insights offer the opportunities to refine MOOC structure\u00a0and content for a more inclusive and engaging learning environment.\u00a0This study contributes to the growing body of research in AI education, especially in large-scale online formats, by demonstrating\u00a0how analysis of user feedback can pinpoint actionable insights.\u00a0The results of this exploratory study can provide recommendations\u00a0to course designers for preparing AI education content across different user segment profiles to continuously improve AI-focused MOOCs\u00a0and better meet the evolving needs of learners.<\/jats:p>","DOI":"10.1007\/978-3-032-00056-9_9","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:24:08Z","timestamp":1759271048000},"page":"98-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Insights on\u00a0Teaching AI at\u00a0Scale: Data-Driven Feedback for\u00a0Learner-Centered Improvement"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9317-053X","authenticated-orcid":false,"given":"Zuhra","family":"Sofyan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9797-6483","authenticated-orcid":false,"given":"Christoph","family":"Meinel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"issue":"4","key":"9_CR1","first-page":"1113","volume":"20","author":"W Du","year":"2021","unstructured":"Du, W., Zhu, H., Saeheaw, T.: Application of the LDA model to semantic annotation of web-based English educational resources. 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