The Convergence of Artificial Intelligence in Measuring Attention and Emotion in Digital Technology-Enhanced Tertiary Education
A Scoping Review
Bibliographic Data
| ID | 22045274 |
|---|---|
| Authors | Javier Arranz-Romero (0009-0003-4198-2661, University of Alicante, corresponding author), Rosabel Roig-Vila (0000-0002-9731-430X, University of Alicante), Miguel Cazorla (0000-0001-6805-3633, University of Alicante) |
| Year | 2026 |
| Volume | 16 |
| Issue | 3 |
| Pages | 433 |
| Publication date | 2026-03-12 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Education Sciences (JOURNAL) |
| Journal identifiers | ISSN: 2227-7102 • E-ISSN: 2227-7102 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/educsci16030433 |
| OpenAlex | W7135055207 |
| Language | EN |
| References cited | 39 |
This scoping review maps AI-based approaches used to infer or measure attention and emotion in technology-enhanced learning (TEL), with a particular focus on tertiary (higher) education and learning analytics-enabled digital environments supporting online and hybrid instruction. Although artificial intelligence (AI) promises personalized digital education, many systems still respond poorly to students’ attentional and emotional fluctuations. We therefore examined the extent to which the literature converges on jointly measuring attention and emotion through AI in educational contexts, especially in virtual and distance-learning settings. Following PRISMA-ScR, we searched Scopus and Web of Science and identified 39 eligible studies. We conducted a methodological quality appraisal using Joanna Briggs Institute tools, a keyword co-occurrence bibliometric analysis, and a narrative synthesis. The evidence shows a rapidly expanding field and a wide range of AI-based techniques, but emotion and attention are typically operationalized and modelled in isolation. Both the bibliometric and narrative results indicate persistent conceptual fragmentation and limitations in the validity of measurement metrics. Overall, the field has not yet established a unified paradigm that integrates attention and emotion within AI-driven educational systems, constraining their adaptive potential. This evidence highlights the need for theory-informed and operational frameworks that enable genuinely holistic, student-centred pedagogical adaptation
Affective Computing · Emotional intelligence · Higher education · Narrative · Operationalization · Scopus · Intelligent Tutoring Systems and Adaptive Learning · Mind wandering and attention · Online Learning and Analytics
The Attention System of the Human Brain
Measuring emotions in students’ learning and performance
The PRISMA 2020 statement
Validation of a teaching model instrument for university education in Ecuador through an artificial intelligence algorithm
Enhancing Student Engagement
University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education
Emotional engagement in a humor-understanding reading task
ChatGPT-enhanced mobile instant messaging in online learning
Assessing how QAA accreditation reflects student experience
Co‐designing enduring learning analytics prediction and support tools in undergraduate biology courses
Multimodal learning analytics—In‐between student privacy and encroachment
Investigating the Association Between Student Engagement With Video Content and Their Learnings
Examining Teaching Competencies and Challenges While Integrating Artificial Intelligence in Higher Education
Software survey
| Citation velocity | historical |
|---|---|
| Highly cited | No |