Author
Abstract
This study investigated the integration of Artificial Intelligence (AI) with gamification techniques to enhance student motivation, engagement, and academic performance in management education. A non-probability sample of 200 undergraduate students from Greece was randomly assigned to one of two groups: an experimental group that experienced AI-driven gamification, or a control group that followed traditional instructional methods. Quantitative analysis revealed that students in the experimental group demonstrated a statistically significant increase in active participation and a statistically significant improvement in course completion rates compared to the control group. This was complemented by significant enhancements in academic performance, with final grades showing a notable difference. The AI-driven gamified environment, which included personalized challenges and real-time feedback, effectively boosted engagement and academic outcomes. Qualitative feedback from students indicated that the AI system’s ability to adapt to individual learning needs was particularly valued. Students appreciated the personalized support, which helped them stay on track and better understand complex concepts. The study highlights the potential of combining AI with gamification to create a more interactive and tailored educational experience, addressing diverse learning needs and preferences. These findings highlight the transformative impact of AI-driven gamification on management education and emphasize the critical role of courses in integrating these innovative methods. They also suggest avenues for future research to further optimize these approaches, enhancing both educational practices and student outcomes in management training.
Suggested Citation
Emmanouil Choustoulakis & Pinelopi Athanasopoulou & Yannis Pollalis & Dimitris Nikoloudakis, 2025.
"Integrating Artificial Intelligence with Gamification Techniques to Enhance Student Motivation and Engagement,"
Springer Proceedings in Business and Economics,,
Springer.
Handle:
RePEc:spr:prbchp:978-3-031-81962-9_58
DOI: 10.1007/978-3-031-81962-9_58
Download full text from publisher
To our knowledge, this item is not available for
download. To find whether it is available, there are three
options:
1. Check below whether another version of this item is available online.
2. Check on the provider's
web page
whether it is in fact available.
3. Perform a
for a similarly titled item that would be
available.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-031-81962-9_58. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.