Artificial Intelligence and Digital Competencies
An Importance–Performance Analysis of Future Mathematics Teachers’ Perceptions
Bibliographic Data
| ID | 22044532 |
|---|---|
| Authors | Pilar Gómez‐Rey (0000-0002-1883-4279, Universidad de Sevilla), Salvador Angosto Sánchez (0000-0001-7281-794X, Universidad de Sevilla), Ari Alamäki (0000-0003-4525-8243, Haaga-Helia University of Applied Sciences), Stephan Schlögl (0000-0001-7469-4381, Management Center Innsbruck) |
| Year | 2026 |
| Volume | 16 |
| Issue | 7 |
| Pages | 1024 |
| Publication date | 2026-06-28 |
| 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/educsci16071024 |
| OpenAlex | W7166468055 |
| Language | EN |
| References cited | 68 |
This study examines how future mathematics teachers perceive the importance of AI-related and digital competencies and their self-reported performance in these areas. The study was conducted in a Mathematics Education course in Spain with 198 Primary Education students. Using an Importance–Performance Map Analysis (IPMA) framework, the questionnaire assessed six dimensions: AI awareness, AI usage, AI evaluation, AI ethics, AI trust, and digital skills, with items adapted from previous studies. The results showed that students assigned higher importance to all competencies than the level of performance they reported. AI evaluation, AI trust, and digital skills received the highest importance scores, whereas AI awareness obtained the lowest scores. The IPMA identified AI usage as the main priority for improvement, as students considered it relevant but reported comparatively lower performance. Differences by academic year and self-reported AI knowledge level suggest that students’ stage of training and perceived AI knowledge influenced their perceptions. These findings reveal a gap between the importance future teachers assign to AI-related competencies and their perceived level of development. The study highlights the need for more specific and pedagogically grounded AI training in Mathematics Education and offers practical implications for teacher education curricula in response to the demands of 21st-century classrooms
Applications of artificial intelligence · Curriculum · Higher education · Knowledge level · Perception · Digital literacy in education · Mathematics Education and Teaching Techniques · Teaching and Learning Programming
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| Citation velocity | historical |
|---|---|
| Highly cited | No |