Volume 4, 2026 – Issue 3
Effects of a Multimodal Artificial Intelligence Tutor on Conditional Statement Achievement and Engagement
Dimitrios Kravvaris 
1
Independent Scholar, Ioannina, Greece
* Corresponding author: jkravv@gmail.com
DOI: 10.5281/zenodo.23583827
Abstract
Conditional statements are a well-documented stumbling block for novice programmers. Beginners tend to misread the flow of control rather than trace which branch a given condition actually selects, and that habit, left alone, tends to follow them into harder material. This study reports a between-groups experiment. We examined whether a multimodal artificial intelligence (AI) tutor, one that generates visual representations of conditional execution and offers adaptive help when a student answers incorrectly, improves learning outcomes and engagement relative to traditional classroom instruction. Forty-four students aged 11 to 12 were assigned to two intact classes of 22. The first group received a standard, teacher-led lesson on if and if-else statements, built around textbook explanations and worked examples on the board. The second covered the same material in pairs, using a multimodal AI tutor that supplied step-by-step flowcharts and execution traces whenever a pair answered incorrectly, following the driver-navigator pair programming technique. Learning was measured with a pre-test and post-test on predicting the output of conditional code; engagement was gauged through a short questionnaire and classroom observation. The tutor group scored significantly higher on the post-test and reported substantially greater interest and enjoyment. These findings suggest that multimodal AI tools, used to visualize conditional execution and to scaffold corrections after errors, can lift both achievement and motivation in introductory programming.
Keywords: multimodal artificial intelligence, conditional statements, programming education, student engagement, intelligent tutoring systems
Published
2026/10/31
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Section
Research Papers
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Kravvaris, D. (2026). Effects of a multimodal artificial intelligence tutor on conditional statement achievement and engagement. The International Journal of Technology, Innovation, and Education, 4(3), 110–129. https://doi.org/10.5281/zenodo.23583827
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Kravvaris, Dimitrios. 2026. “Effects of a Multimodal Artificial Intelligence Tutor on Conditional Statement Achievement and Engagement.” The International Journal of Technology, Innovation, and Education 4 (3): 110–29. https://doi.org/10.5281/zenodo.23583827.
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Kravvaris, Dimitrios. “Effects of a Multimodal Artificial Intelligence Tutor on Conditional Statement Achievement and Engagement.” The International Journal of Technology, Innovation, and Education, vol. 4, no. 3, Oct. 2026, pp. 110–29, https://doi.org/10.5281/zenodo.23583827.
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[1]
D. Kravvaris, “Effects of a multimodal artificial intelligence tutor on conditional statement achievement and engagement,” The International Journal of Technology, Innovation, and Education, vol. 4, no. 3, pp. 110–129, Oct. 2026, doi: 10.5281/zenodo.23583827.
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Kravvaris, D. (2026) “Effects of a multimodal artificial intelligence tutor on conditional statement achievement and engagement,” The International Journal of Technology, Innovation, and Education, 4(3), pp. 110–129. Available at: https://doi.org/10.5281/zenodo.23583827.
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Kravvaris D. Effects of a multimodal artificial intelligence tutor on conditional statement achievement and engagement. The International Journal of Technology, Innovation, and Education [Internet]. 2026 Oct 31;4(3):110–29. Available from: https://ijtie.com/wp-content/uploads/2026/10/N40329.pdf
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Kravvaris, D. (2026). Effects of a multimodal artificial intelligence tutor on conditional statement achievement and engagement. The International Journal of Technology, Innovation, and Education, 4(3), 110–129. https://doi.org/10.5281/zenodo.23583827
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About the authors
- Dimitrios Kravvaris holds a Ph.D. in Informatics from the Ionian University, Greece, where his research focused on the analysis of educational video lectures using social and linguistic metadata. He currently serves as Deputy Headmaster and Computer Science teacher at the 3rd Primary School of Ioannina. His research interests include educational data mining, social network analysis, STEM education, educational robotics, and AI in teaching. He serves as a reviewer for the Journal of the Knowledge Economy (Springer) and Engineering Applications of Artificial Intelligence (Elsevier).
jkravv@gmail.com
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