Generative AI-Based Tutoring for Enhancing Learning Engagement and Achievement
Saved in:
| Title: | Generative AI-Based Tutoring for Enhancing Learning Engagement and Achievement |
|---|---|
| Language: | English |
| Authors: | Tian Belawati (ORCID |
| Source: | Open Praxis. 2025 17(2):211-226. |
| Availability: | International Council for Open and Distance Education. Lilleakerveien 23, 0283 Oslo, Norway. Tel: +47-22-06-26-30; Fax: +47-22-06-26-31; e-mail: icde@icde.org; Web site: https://openpraxis.org/ |
| Peer Reviewed: | Y |
| Page Count: | 16 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Artificial Intelligence, Technology Uses in Education, Distance Education, Foreign Countries, Learner Engagement, Academic Achievement, Feedback (Response), Man Machine Systems, Natural Language Processing, Intelligent Tutoring Systems, College Students |
| Geographic Terms: | Indonesia |
| ISSN: | 1369-9997 2304-070X |
| Abstract: | This paper presents the findings of a pilot study on the use of generative AI (GAI) in tutorial sessions within a large-scale distance education institution in Indonesia. The primary aim of the experiment was to assess the impact of GAI-based tutoring on student engagement and academic achievement. A secondary objective was to explore how GAI could reduce the workload of human tutors by automating routine tasks, such as responding to frequently asked questions and providing initial feedback, thereby allowing human tutors to concentrate more on moderation and higher-order teaching tasks. The development team adopted a modular design approach, utilizing OpenAI's ChatGPT models (versions 3.5 and 4.0) and integrating them with the Moodle learning management system. The resulting system consisted of three core modules: Management, LMS Integration, and Backend. These modules enabled efficient administration, seamless integration with the existing learning platform, and effective generation of AI responses for student interactions. The study involved data collection from four online courses with a total of 37,743 students. The results indicated that students in GAI-assisted classes participated more actively in discussion forums and achieved slightly higher scores in their assignments compared to those in non-GAI-assisted settings. These findings suggest that GAI-based tutor assistants can provide considerable enhancements in online learning environments, particularly in fostering engagement and improving student outcomes. The study suggest further improvement to make it more effective and have a greater positive impact on student engagement and achievement. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1481062 |
| Database: | ERIC |
Be the first to leave a comment!
