Can generative AI Be an effective Co-Teacher? An experiment
This study investigates the effectiveness of integrating Text Generative AI as a supplementary teaching tool within a blended learning approach, aiming to provide educators with additional pedagogical flexibility and enhance classroom efficiency. Employing an experimental methodology with the 400 participants of control and experimental groups, the study utilized pre-test and post-test evaluations, stratified sampling, and impartial reviews of prepared scripts. The intervention incorporated AI tools like ChatGPT, Copilot, and Gemini as co-teaching assistants, applied only to the experimental group, with a unique integration strategy designed to seamlessly blend these tools into the instructional process. Findings revealed that the AI-integrated approach, that allows students to self-learn a portion of the academic contents using AI, enabled deeper learning, allowing physical teachers to cover 25 % more content, resulting in enhanced understanding, higher grades, and increased student satisfaction. The study, however, is limited to exploring cognitive and behavioral changes based on written assessments and decoy grades only. Future studies hold potential to explore more complex relationships and allow a geographically stratified sample for deeper insights. But regardless of the limitations, this research not only offers quantitative validation of AI's role in education, addressing gaps in previous studies by empirically assessing the effectiveness of AI integration and its impact on student satisfaction, but also works as a foundational study for future authors in exploring this phenomenon. • AI chatbots in classrooms boost content learning and enhance student satisfaction. • AI can assist educators to teach 25 % more contents to students compared to a regular syllabus. • AI blended learning experience helps students achieve better academic grades. • AI blended learning reduces dissatisfaction amongst students if they receive undesirable grades.
Authors
- Rup Chowdhury (ORCID: https://orcid.org/0009-0000-6899-3254)
- Nayeema Sultana (ORCID: https://orcid.org/0000-0002-3633-0453)
- Amit Sen (ORCID: https://orcid.org/0009-0000-5944-1175)
- Mashruha Zabeen (ORCID: https://orcid.org/0000-0001-7507-4563)
- Ahnaf Chowdhury Niloy (ORCID: https://orcid.org/0000-0002-1103-1385)
- Muslima Afroz (ORCID: https://orcid.org/0009-0006-5744-3648)
- Md. A. Rahman (ORCID: https://orcid.org/0009-0000-9956-0988)
- Salma Akter (ORCID: https://orcid.org/0000-0003-0109-6457)
- Jakia Sultana (ORCID: https://orcid.org/0000-0002-1421-8778)
- Mrinmoy Sarkar (ORCID: https://orcid.org/0009-0005-5967-1372)
- Saadman Mahmud (ORCID: https://orcid.org/0009-0004-7106-2230)
- Mehnaz Tabassum (ORCID: https://orcid.org/0009-0000-0653-1660)
- Tasnem Islam Prome (ORCID: https://orcid.org/0009-0007-7424-0620)
- Nusrat Jahan Isha (ORCID: https://orcid.org/0009-0009-4509-6345)
Institutions
- Bangladesh Institute of Development Studies (BD)
- University of Dhaka (BD)
- Bangladesh Open University (BD)
- BRAC University (BD)
- East West University (BD)
- University of Chittagong (BD)
- Bangladesh University of Professionals (BD)
Publication Details
- Journal
- Computers and Education Artificial Intelligence
- Published
- 2025-05-22
- DOI
- https://doi.org/10.1016/j.caeai.2025.100418
- Citations
- 2
- Primary Topic
- Collaborative Teaching and Inclusion
- Type
- article
- Field-Weighted Citation Impact
- 17.85