Research article | Open Access
International Journal of Trends and Developments in Education 2026, Vol. 6(1) 1-12
The Efficacy of Octo-Focus as an AI-Based Self-Regulation System to Maximize Students’ Productivity
pp. 1 - 12 | DOI: https://doi.org/doi.org/10.5281/zenodo.18490722
Publish Date: February 05, 2026 | Single/Total View: 0/0 | Single/Total Download: 0/0
Abstract
This study investigates the effectiveness of Octo-Focus, a novel AI-driven coaching system designed to enhance students’ self-regulated learning. Contemporary students increasingly struggle with distraction, procrastination, and ineffective study planning, which negatively impact academic performance. To address these challenges, Octo-Focus integrates AI-supported personalized planning, real-time focus and fatigue detection using Computer Vision, and behavioral analytics into a unified study support platform. A mixed-method approach was employed, combining survey data from secondary and higher education students with performance evaluations of a deep learning–based attention detection model. The results demonstrate that the proposed system reliably identifies attention and fatigue states with high accuracy and supports more structured, goal-oriented study behaviors. The findings suggest that Octo-Focus contributes a novel, holistic approach to digital learning support by transforming studying from a passive process into an adaptive, data-driven coaching experience.
Keywords: Deep Learning, Detection, Student, Productivity, Coaching, Education, AI
APA 7th edition
Unal, F., Calik, T., Coskun, A.A., Alkan, E.E., Kayis, M.N.S., Bulut, B., & Basarici, M.S. (2026). The Efficacy of Octo-Focus as an AI-Based Self-Regulation System to Maximize Students’ Productivity. International Journal of Trends and Developments in Education, 6(1), 1-12. https://doi.org/doi.org/10.5281/zenodo.18490722
Harvard
Unal, F., Calik, T., Coskun, A., Alkan, E., Kayis, M., Bulut, B. and Basarici, M. (2026). The Efficacy of Octo-Focus as an AI-Based Self-Regulation System to Maximize Students’ Productivity. International Journal of Trends and Developments in Education, 6(1), pp. 1-12.
Chicago 16th edition
Unal, Fatih, Tilsim Calik, Asya Ayse Coskun, Eren Efe Alkan, Meryem Nur Sultan Kayis, Beyzanur Bulut and Mahmut Sami Basarici (2026). "The Efficacy of Octo-Focus as an AI-Based Self-Regulation System to Maximize Students’ Productivity". International Journal of Trends and Developments in Education 6 (1):1-12. https://doi.org/doi.org/10.5281/zenodo.18490722
Akbulut, F., & Arslan, M. (2021). The effects of using digital materials in teaching Turkish abroad. Journal of Turkish Culture Research, 58(1), 55-70. Clear, J. (2018). Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones. Penguin Random House. Council of Europe. (2020). Multilingualism and cultural diversity report. Council of Europe Publishing. Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 15583-15587. OECD (2022). PISA 2022 Results: Learning During – and From – Disruption. OECD Publishing. Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of motivational and cognitive components. Psychological Bulletin, 133(1), 65–94. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64-70. Yıldırım, A., & Şimşek, H. (2018). Qualitative Research Methods in Social Sciences (11th edition). Ankara: Seçkin Publishing. Khandelwal, K., & Upadhyay, A. K. (2021). The advent of artificial intelligence-based coaching. Strategic HR Review, 20(4), 137-140. Terblanche, N., & Cilliers, D. (2020). Factors that influence users’ adoption of being coached by an artificial intelligence coach. Philosophy of Coaching: An International Journal, 5(1), 61-70. Terblanche, N., Molyn, J., De Haan, E., & Nilsson, V. O. (2022). Coaching at Scale: Investigating the Efficacy of Artificial Intelligence Coaching. International Journal of Evidence Based Coaching & Mentoring, 20(2). Graßmann, C., & Schermuly, C. C. (2021). Coaching with artificial intelligence: Concepts and capabilities. Human Resource Development Review, 20(1), 106-126. Cheng, K., Wu, S., Peng, B., & Wang, X. (2025). An artificial intelligence enhanced coaching mode. International Journal of Surgery, 10-1097. Jud, M., & Thalmann, S. (2025). AI in digital sports coaching–a systematic review. Managing Sport and Leisure, 1-17. Dontre, A. J. (2021). The influence of technology on academic distraction: A review. Human Behavior and Emerging Technologies, 3(3), 379-390. Duncan, D. K., Hoekstra, A. R., & Wilcox, B. R. (2012). Digital devices, distraction, and student performance: Does in-class cell phone use reduce learning. Astronomy education review, 11(1), 1-4. Beland, L. P., & Murphy, R. (2016). Ill communication: technology, distraction & student performance. Labour Economics, 41, 61-76. Flanigan, A. E., Brady, A. C., Dai, Y., & Ray, E. (2023). Managing student digital distraction in the college classroom: A self-determination theory perspective. Educational Psychology Review, 35(2), 60. Dietz, S., & Henrich, C. (2014). Texting as a distraction to learning in college students. Computers in Human behavior, 36, 163-167. Blasiman, R. N., Larabee, D., & Fabry, D. (2018). Distracted students: A comparison of multiple types of distractions on learning in online lectures. Scholarship of Teaching and Learning in Psychology, 4(4), 222. Parekh, V., Shah, D., & Shah, M. (2020). Fatigue detection using artificial intelligence framework. Augmented Human Research, 5(1), 5. Al Imran, M. A., Nasirzadeh, F., & Karmakar, C. (2024). Designing a practical fatigue detection system: A review on recent developments and challenges. Journal of Safety Research, 90, 100-114. Hartley, L., Horberry, T., Mabbott, N., & Krueger, G. P. (2000). Review of fatigue detection and prediction technologies. National Road Transport Commission, 1-41. Dai, D. (2021, November). An introduction of cnn: models and training on neural network models. In 2021 International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR) (pp. 135-138). IEEE. Sharma, K., Giannakos, M., & Dillenbourg, P. (2020). Eye-tracking and artificial intelligence to enhance motivation and learning. Smart Learning Environments, 7(1), 13. Ndiaye, Y., Lim, K. H., & Blessing, L. (2023, November). Eye tracking and artificial intelligence for competency assessment in engineering education: a review. In Frontiers in Education (Vol. 8, p. 1170348). Frontiers Media SA.
