Gender, age, and emotion-aware strategies in AI-enhanced education: insights from future educators and algorithmic thinking in primary schools
Introduction: Artificial Intelligence (AI) is rapidly transforming educational landscapes. Understanding how educators and students perceive and engage with AI is crucial for effective integration.Objectives: This study aims to (1) analyze the attitudes of future educators toward AI in teaching, considering variables such as gender and age, and (2) evaluate the effectiveness of emotion-aware, ICT-based instructional strategies in enhancing algorithmic thinking among primary school pupils.Methods: A mixed-methods approach was employed. Quantitative data on pre-service teachers’ perceptions were collected via surveys from participants at state-managed universities in the Zamboanga Peninsula, Philippines. Additionally, an experimental pedagogical intervention using emotion-sensitive strategies was implemented in selected primary schools to assess changes in students' algorithmic thinking skills.Results: Statistical analyses revealed significant differences in AI-related perceptions based on gender and age among pre-service teachers. Furthermore, primary school pupils who participated in the emotion-aware instructional activities showed notable improvements in algorithmic thinking competencies compared to control groups.Conclusions: The findings underscore the importance of aligning AI integration with the emotional and sociocultural dynamics of both teachers and learners. Emotion-sensitive strategies not only foster computational competencies in young students but also create inclusive teaching environments. The study advocates for thoughtful, context-aware implementation of AI in education to maximize its transformative potential.
Authors
- Khadija Achtaich (ORCID: https://orcid.org/0000-0001-7883-7714)
- Naceur Achtaich (ORCID: https://orcid.org/0000-0001-8640-8338)
- Fatimazahra Ouahouda
Institutions
- University of Hassan II Casablanca (MA)
Publication Details
- Journal
- EthAIca
- Published
- 2025-08-09
- DOI
- https://doi.org/10.56294/ai2025406
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69