Impact of AI-assisted microlearning on student engagement in an online environment in higher education
Bibliographic Data
| ID | 22161969 |
|---|---|
| Authors | Reham Salhab (0000-0002-3457-389X, Palestine Technical University - Kadoorie), Mosab M Aboushi (Palestine Technical University - Kadoorie) |
| Year | 2026 |
| Volume | 11 |
| Publication date | 2026-02-20 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Education (JOURNAL) |
| Journal identifiers | ISSN: 2504-284X • E-ISSN: 2504-284X |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/feduc.2026.1766032 |
| OpenAlex | W7130643436 |
| Language | EN |
| References cited | 47 |
Introduction The rapid advancement of technology has transformed teaching and learning in educational settings, introducing innovative tools into teacher education programs. Microlearning integration in the educational process is significantly influenced by the emergence of generative artificial intelligence (AI) tools. This article investigates the impact of the integration of AI-based microlearning on the learning engagement of college students in a multimedia course within the educational technology department. Investigating engagement is essential as it influences students’ motivation, academic performance, and perceptions. Yet, research on AI-assisted microlearning remains limited, particularly in the Middle East, underscoring the need for further investigation. Methods Using an explanatory sequential mixed-methods approach, the study involved 50 students enrolled in an educational technology course. A quasi-experimental design was implemented and supplemented with a phenomenological approach. Data were collected from both experimental and control groups, along with online forum discussions and 25 semi-structured interviews with pre-service teachers who participated in AI-assisted microlearning. Results ANCOVA analysis revealed that AI-assisted microlearning positively influenced student engagement and its components. Through inductiveand deductive content analysis four main themes emerged, including cognitive, emotional, social, and behavioral engagement, further divided into six subthemes: grabbing attention, personalized learning, reduced frustration, task completion, sharing, and competition. Discussion The findings highlight the role of empowering educators through the integration of technology, equity, and innovation in higher education. AI-assisted microlearning serves as a pedagogical tool to foster inclusive, adaptive, and engaging learning environments. Recommendations include using AI to tailor content to individual learning styles, thereby supporting educators in delivering equitable and innovative educational experiences
Content analysis · Educational technology · Higher education · Learning environment · Online learning · Student engagement · Technology integration · Advanced Technologies in Various Fields · E-Learning and COVID-19 · Technology-Enhanced Education Studies
Conversation Technology With Micro-Learning
Student engagement, context, and adjustment
Staying Engaged
ChatGPT in education
What can “thematic analysis” offer health and wellbeing researchers?
Statistical power analyses using G*Power 3.1
Micro-learning principles in teaching EFL in the structure of supplementary and further education
Contribution of Microlearning in Basic Education
Mobile microlearning
Revolutionizing learning in the digital age
Effects of a virtual reality-based pottery making approach on junior high school students’ creativity and learning engagement
Student engagement and its association with academic achievement and subjective well-being
Designing Microlearning Instruction for ProfessionalDevelopment Through a Competency Based Approach
Microlearning through the Lens of Gagne’s Nine Events of Instruction
The artificial intelligence divide
The four pillars of tertiary student engagement and success
Three Approaches to Qualitative Content Analysis
| Citation velocity | historical |
|---|---|
| Highly cited | No |