- Spivakovskiy, Oleksandr (orcid.org/0000-0001-7574-4133), Cherkashyna, Tetiana (orcid.org/0000-0002-7307-2262), Revenko, Yevheniia (orcid.org/0000-0002-1768-9436), Petukhova, Liubov (orcid.org/0000-0002-0751-6961), Lemeshchuk, Oleksandr (orcid.org/0000-0002-9876-3502) and Soloveiko, Oleksandr (orcid.org/0009-0009-5450-2042) (2025) Artificial intelligence as a component of measuring students' engagement in learning in the online educational environment of a higher education institution Information Technologies and Learning Tools, 2 (106). pp. 134-149. ISSN 2076-8184
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Abstract
Artificial intelligence (AI) has become an integral part of the education sector, driving advancements in both teaching methods and assessment tools. With the rise of online and hybrid learning models, particularly in response to global challenges such as the COVID-19 pandemic and the ongoing war in Ukraine, higher education institutions face unique challenges. The educational process in some higher education institutions, especially those that have been relocated, is conducted in a mixed or fully remote format. This situation demands not only enhancements in personalized student learning and revisions in the assessment systems for knowledge quality but also an in-depth exploration of students' attitudes and motivations toward the educational process under these challenging circumstances. In such conditions, the use of AI will help make the educational process more organized, efficient, and innovative. Effectively organizing distance learning requires tools that can assess students' knowledge while considering their concentration, engagement, and interest in the material, as well as their willingness to interact with teachers and provide feedback. This approach improves the educational process and ensures high-quality training for future professionals. When students focus on the material and actively engage in learning, they better understand and retain new information, which deepens their knowledge and enhances their professional competencies. The article explores the use of artificial intelligence to measure students' attention levels, class engagement, and readiness to provide feedback during blended or remote learning. AI enables automatic, unbiased analysis of student behavior during classes, capturing metrics such as attention, interaction levels, gestures, posture, lip movements, eyelid blinking frequency, and physiological responses. This approach provides precise and objective data on student engagement, which traditional observation methods cannot offer. Key areas for further research into the application of artificial intelligence in measuring the online educational environment include: the potential for AI to analyze video recordings of educational sessions based on criteria that impact learning quality (such as attention levels, interaction activity, gestures and postures, body tension, breathing, blinking frequency, lip and jaw movements, and reactions to content); the capability of AI to generate analytical reports based on quantitative data related to learning outcomes or survey results; and the potential for AI to develop automated tools or applications that enhance personalized, student-centered learning in higher education institutions.
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