General description on theoretical framework & background

Existing research indicates that in the modern world, students are unable to maintain long – term concentration on lectures delivered in a monotonous way. Constant stimulation from social media leads to students being willing to process information only when they are actively involved in the process (Mark, 2023; Marquez et al., 2023). Considerable attention is also paid to the fact that today’s students, as technologically advanced users, expect a high level of technology use from academic staff as well.

At the same time, learning new resources requires additional time for training, preparation, experimentation, and adaptation—time that many lecturers are unwilling or unable to allocate, as they already feel overloaded with existing responsibilities and the overall workload continues to grow (Aparicio-Gomez et al., 2023).

According to a report published in 2024 by Ellucian, a leading provider of higher education technology solutions, despite the recognized advantages of artificial intelligence (AI), its use in higher education institutions is not as widespread as commonly assumed. Faculty hesitation remains high—although 61% of academic staff have used AI in teaching, 88% do so minimally, indicating a cautious approach to integrating AI into study courses.

When conducting a small preliminary study, author also concluded that despite the high level of student digitalization, professors report very limited use of digital and AI tools during their lectures (only 30% of surveyed lecturers have used AI tools at least once), (Stolca, 2025)

A study that would help academic staff better navigate the opportunities provided by AI tools and more effectively digitally enhance he engaging aspects of their lectures would help bridge the gap between the level of teaching expected by modern students and the level that lecturers are currently prepared to offer. In the context of this study, student engagement refers to any activities during lectures involving student participation that are aimed at increasing students’ concentration, involvement in the learning process, and positively influencing learning outcomes.

Research objectives

To explore the most effective solutions for the use of artificial intelligence (AI) and digital tools that would promote both student engagement and the achievement of learning outcomes.

Research questions posed:

1)    Which dimensions of student engagement (behavioral, emotional, and cognitive) are most strongly associated with students’ use of AI tools, as measured by the adapted HESES scale?

2)    Which AI-supported teaching and learning practices are perceived as most effective for enhancing student engagement in higher education study courses?

3)    What relationships emerge between lecturers pedagogical intentions for AI integration and students’ experienced engagement in AI-supported courses?

Methods/methodology During the scientific research project, it is planned to identify and adapt existing student engagement scale HESES - Higher Education Student Engagement Scale (Zhoc, et al, 2019) to the context of AI tool usage in business and STEM education and pilot them in several study courses. From the sample perspective, the study is two-sided. On one side, research participants are lecturers who actively use AI tools to boost engagement during their study courses. Semi – structured interviews are planned to be delivered in the beginning of the research process – to collect their hypothesis regarding the students answers and also after the data on engagement scales are collected – to discuss the given results. On the other side, research participants are the students who attend these lectures and actively participate; their engagement will be measured towards the end of the semester using adapted HESES scale. Putting together the perspective of both students and professors, the research aims to understand, what are the most efficient AI tool to be used for improving student engagement and increasing their learning outcomes.

Gathered data will be extra fresh for the ECER conference in August, as the collection of the data (both from semi – structured interviews and the student survey) will finish in the beginning of June and I am planning to analyze the data and draw the conclusions by the end of July. The research is expected to provide insights into the role of AI tools in fostering student engagement in higher education study courses. First, the study will identify which dimensions of student engagement (behavioral, emotional, and cognitive) are most strongly associated with the use of AI tools, based on the adapted HESES scale results. Second, by combining student survey data with semi-structured interviews with the academic staff members, the research is expected to reveal dependencies between academia expectations and students’ reported engagement levels. Thirdly, the study is expected to identify a set of AI practices that could be efficiently used for increasing student engagement (e.g., formative feedback tools, interactive content generation, adaptive learning support) Most importantly, findings will be grounded in real course implementations rather than hypothetical use cases. Overall, the results will support evidence-based decision-making for academic staff seeking to integrate AI tools in their study course.

References 1.Aparicio – Gomez, O. Y., Ostos-Ortiz, O. L., & Abadia – Garcia C. (2024). Convergence between emerging technologies and active methodologies in the university. Journal of Technology and Science Education, 14(1), 31–44. https://doi.org/10.3926/jotse.2508 2.Ellucian. (October 2024 ). AI in Higher Education: Understanding the Present and Shaping the Future. Ellucian.com. https://lp.ellucian.com/ai-innovation-survey.html 3.Mark, G. (February 2023). Why our attention spans are shrinking. American Psychological Association. https://www.apa.org/news/podcasts/speaking-of-psychology/attention-spans 4.Stolca, P. (2025). Student Participation Methods to Improve Engagement and Understanding of the Material: Pre-Research. Human, Technologies and Quality of Education, 2025. 698 p. https://doi.org/10.22364/htqe.2025 5.Zhoc, K. C. H., Webster, B. J., King, R. B., Li, J. C. H., & Chung, T. S. H. (2019). Higher Education Student Engagement Scale (HESES): Development and Psychometric Evidence. Research in Higher Education, 60(2), 219–244. https://doi.org/10.1007/s11162-018-9510-6