Author
Listed:
- Unaiza Khudai
(Language Academy, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia)
- Shanti Chandran Sandaran
(Language Academy, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia)
- Marsha Lavania Manivannan
(Language Academy, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia)
- M. Rab Nawaz Shad
(Army Education Corps, Pakistan)
Abstract
This study investigates the integration of artificial intelligence (AI) into English for specific military purposes (ESMP) training for Pakistan army personnel who are preparing for United Nations peacekeeping missions. Using an explanatory sequential mixed methods design with stratified random samples of officers (n = 30) and troops (n = 30), the research examined perceptions of AI’s suitability for mission-oriented English training. Quantitative results revealed that officers reported strong digital literacy (M = 4.53) and institutional endorsement (M = 5.00), but low personal readiness (M = 3.43). In contrast, troops demonstrated moderate digital literacy (M = 3.37) but higher motivation (M = 4.23) and strong support for compulsory AI-ESMP training (M = 4.30). The qualitative findings reinforced these patterns: officers emphasized institutional policy, infrastructural requirements, and security concerns, while troops regarded AI as flexible, motivational, and practically useful. These findings confirm the feasibility of developing an AI-ESMP Adaptive Framework to enhance communication, operational readiness, and multinational collaboration in peacekeeping environments.
Suggested Citation
Unaiza Khudai & Shanti Chandran Sandaran & Marsha Lavania Manivannan & M. Rab Nawaz Shad, 2025.
"Artificial Intelligence for English for Specific Military Purposes: An Adaptive Framework for UN Peacekeeping Missions,"
International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(25), pages 39-43, November.
Handle:
RePEc:bcp:journl:v:9:y:2025:i:25:p:39-43
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