Assessment of Learner Engagement and Expert Evaluations of AI-Generated Versus Human-Created Interactive Content in an Online Course

Generative artificial intelligence (GenAI) has introduced a novel aspect to educational methodologies and sparked fresh dialogues regarding the creation and evaluation of instructional resources. This project seeks to investigate the impact of GenAI on the development and assessment of online course materials and learners’ engagement with these materials in the online learning environment. The study analyzed GenAI-generated multiple-choice questions, fill-in-the-blank exercises, and true-false activities during 3 weeks of a 14-week online course. Subject matter experts assessed these documents in regards to content, relevance, and clarity. Data was collected through an online form with open-ended questions. The interactions of learners with the GenAI-created learning activities were analyzed using log records of the learning management system and compared to the content provided by the course instructor regarding interaction levels. The study’s conclusions elucidate the capability of GenAI technologies to produce course-specific content and their efficacy in education. We stress that human specialists’ critical evaluations play a crucial part in improving the pedagogical validity of GenAI-powered learning materials. Further research into topics including the ethical dimension, the effect on academic achievement, and student motivation is recommended.

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Publication Details

Journal
The International Review of Research in Open and Distributed Learning
Published
2025-11-21
DOI
https://doi.org/10.19173/irrodl.v26i4.8608
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
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article

Assessment of Learner Engagement and Expert Evaluations of AI-Generated Versus Human-Created Interactive Content in an Online Course

Hamza Aydemir, Şeyda Kır
The International Review of Research in Open and Distributed Learning
Intelligent Tutoring Systems and Adaptive Learning
article

Assessment of Learner Engagement and Expert Evaluations of AI-Generated Versus Human-Created Interactive Content in an Online Course

Hamza Aydemir, Şeyda Kır
article en

Abstract

Generative artificial intelligence (GenAI) has introduced a novel aspect to educational methodologies and sparked fresh dialogues regarding the creation and evaluation of instructional resources. This project seeks to investigate the impact of GenAI on the development and assessment of online course materials and learners’ engagement with these materials in the online learning environment. The study analyzed GenAI-generated multiple-choice questions, fill-in-the-blank exercises, and true-false activities during 3 weeks of a 14-week online course. Subject matter experts assessed these documents in regards to content, relevance, and clarity. Data was collected through an online form with open-ended questions. The interactions of learners with the GenAI-created learning activities were analyzed using log records of the learning management system and compared to the content provided by the course instructor regarding interaction levels. The study’s conclusions elucidate the capability of GenAI technologies to produce course-specific content and their efficacy in education. We stress that human specialists’ critical evaluations play a crucial part in improving the pedagogical validity of GenAI-powered learning materials. Further research into topics including the ethical dimension, the effect on academic achievement, and student motivation is recommended.

The International Review of Research in Open and Distributed LearningVol. 26(4)
Yozgat Bozok Üniversitesi (TR), Kahramanmaraş Sütçü İmam University (TR)
Quality Education
Openalex Percentile: Top 19%
Intelligent Tutoring Systems and Adaptive Learning
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Assessment of Learner Engagement and Expert Evaluations of AI-Generated Versus Human-Created Interactive Content in an Online Course — Hamza Aydemir, Şeyda Kır · The International Review of Research in Open and Distributed Learning (2025) | TGRS Research Map | TGRS