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

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Foilsithe in:	International Review of Research in Open and Distributed Learning vol. 26, no. 4 (Nov 2025), p. 1-24
Príomhchruthaitheoir:	Aydemir, Hamza
Rannpháirtithe:	Kır, Şeyda
Foilsithe / Cruthaithe:	International Review of Research in Open and Distance Learning
Ábhair:	Pedagogy Students Mobile communications networks Instructional design Learning management systems Ethics Distance learning Generative artificial intelligence Teachers Chatbots Case studies Educational objectives Personalized learning Educational materials Content creation Multiple choice Qualitative research Education Learning Analytics Learning Activities Educational Practices Data Collection Intelligent Tutoring Systems Influence of Technology Distance Education Addition Doctoral Programs Computers Learning Experience Educational Technology Electronic Equipment Engineering Technology Artificial Intelligence Student Motivation Data Analysis Instructional Material Evaluation Language Processing Course Content Learner Engagement Educational Strategies
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Cur síos
Achoimre:	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.
ISSN:	1492-3831
Foinse:	Education Database