AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations
In recent years, artificial intelligence has been leveraged to develop an automatic question generation (AQG) system that places formative practice questions alongside textbook content in an ereader platform. Engaging with formative practice while reading is a highly effective learning strategy. AQG...
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| Format: | Article |
| Language: | English |
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Croatian Communications and Information Society (CCIS)
2025-06-01
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| Series: | Journal of Communications Software and Systems |
| Subjects: | |
| Online Access: | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0120/ |
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| author | Rachel Van Campenhout Benny G. Johnson Michelle Clark Melissa Deininger Shannon Harper Kelly Odenweller Erin Wilgenbusch |
| author_facet | Rachel Van Campenhout Benny G. Johnson Michelle Clark Melissa Deininger Shannon Harper Kelly Odenweller Erin Wilgenbusch |
| author_sort | Rachel Van Campenhout |
| collection | DOAJ |
| description | In recent years, artificial intelligence has been leveraged to develop an automatic question generation (AQG) system that places formative practice questions alongside textbook content in an ereader platform. Engaging with formative practice while reading is a highly effective learning strategy. AQG made it possible to scale this method to thousands of textbooks and millions of students for free. Previous research studies used aggregated data from all questions answered by all students to complete the largest evaluation of the performance metrics for automatically generated questions. However, these studies also indicated that when assigned in a classroom setting, student behavior and question performance metrics would differ. In this study, we evaluate data collected from 19 course sections taught by four faculty members at Iowa State University to gain a broader understanding of how students engage with these AI-generated practice questions when part of their university courses. Implementation strategies for the courses, student engagement, and question performance metrics are analyzed, and student feedback gathered from surveys and course evaluations are presented. Implications for further use in higher education classrooms are discussed. |
| format | Article |
| id | doaj-art-ffa13cf9d62749d18cd4b64aa7302d57 |
| institution | OA Journals |
| issn | 1845-6421 1846-6079 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Croatian Communications and Information Society (CCIS) |
| record_format | Article |
| series | Journal of Communications Software and Systems |
| spelling | doaj-art-ffa13cf9d62749d18cd4b64aa7302d572025-08-20T01:50:06ZengCroatian Communications and Information Society (CCIS)Journal of Communications Software and Systems1845-64211846-60792025-06-0121217818810.24138/jcomss-2024-0120AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty ObservationsRachel Van CampenhoutBenny G. JohnsonMichelle ClarkMelissa DeiningerShannon HarperKelly OdenwellerErin WilgenbuschIn recent years, artificial intelligence has been leveraged to develop an automatic question generation (AQG) system that places formative practice questions alongside textbook content in an ereader platform. Engaging with formative practice while reading is a highly effective learning strategy. AQG made it possible to scale this method to thousands of textbooks and millions of students for free. Previous research studies used aggregated data from all questions answered by all students to complete the largest evaluation of the performance metrics for automatically generated questions. However, these studies also indicated that when assigned in a classroom setting, student behavior and question performance metrics would differ. In this study, we evaluate data collected from 19 course sections taught by four faculty members at Iowa State University to gain a broader understanding of how students engage with these AI-generated practice questions when part of their university courses. Implementation strategies for the courses, student engagement, and question performance metrics are analyzed, and student feedback gathered from surveys and course evaluations are presented. Implications for further use in higher education classrooms are discussed.https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0120/automatic question generationperformance metricsquestion difficultypersistencenatural learning contextstudent behavior |
| spellingShingle | Rachel Van Campenhout Benny G. Johnson Michelle Clark Melissa Deininger Shannon Harper Kelly Odenweller Erin Wilgenbusch AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations Journal of Communications Software and Systems automatic question generation performance metrics question difficulty persistence natural learning context student behavior |
| title | AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations |
| title_full | AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations |
| title_fullStr | AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations |
| title_full_unstemmed | AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations |
| title_short | AI-Generated Questions in Context: A Contextualized Investigation Using Platform Data, Student Feedback, and Faculty Observations |
| title_sort | ai generated questions in context a contextualized investigation using platform data student feedback and faculty observations |
| topic | automatic question generation performance metrics question difficulty persistence natural learning context student behavior |
| url | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0120/ |
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