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.

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.

Peacekeeping Missions

English is widely acknowledged as the lingua franca of diplomacy, multinational collaboration, and United

Nations Peacekeeping Missions. For Pakistan, one of the largest troop-contributing countries, effective English

communication is critical for operational success. However, persistent challenges remain, as troops often rely

on general English training that does not fully address the mission-specific communicative demands of

for tailored ESMP and the potential of AI by examining the perceptions of both officers and troops to propose

an AI-ESMP adaptive framework for future implementation.

Artificial intelligence (AI) offers significant advantages in education, notably by supporting adaptive learning

pathways, delivering immediate feedback, and boosting student motivation. Specifically, AI-driven

personalization enhances learner autonomy (Woo & Choi, 2021), while Generative AI can effectively simulate

authentic scenarios (Ejaz & Jamil, 2024. However, alongside these benefits, researchers like Bannister et al.

(2023) emphasize the necessity of considering ethical and infrastructural challenges, including system

reliability and data governance. Some of these aspects of concern can be cybersecurity failures, risk of leaking

on cognitive intelligence, communication weaknesses, and directly compromising tactical secrecy. Likewise,

high-stakes communicative demands inherent in that context. Second, Artificial Intelligence (AI) in education

provides the pedagogical affordances necessary to meet these needs, including personalization, real-time

feedback, and authentic simulation. Third, defense studies contextualize this solution by highlighting AI’s

emerging role in multinational coordination and communication in high-stakes military environments.

Together, these perspectives provide a robust foundation for the study and the conceptualization of the AI-

ESMP Adaptive Framework as a bridge between the peacekeeping communicative needs of the Pakistan Army

and the pedagogical affordances of AI technology.

adequate representation of both strata: 30 officers (teachers) and 30 troops (learners). In the quantitative phase,

Likert-scale questionnaires captured digital literacy, ESMP literacy, AI suitability, readiness, and motivation.

Descriptive statistics were used to summarize the results. In the qualitative phase, semi-structured interviews

explored participants’ perceptions of AI’s potential, barriers, and institutional implications. Data integration

followed a side-by-side explanatory approach.

The quantitative findings highlighted notable differences between officers and troops. Officers reported strong

digital literacy (M=4.53) and full endorsement of institutional adoption (M=5.00), but low personal readiness

(M=3.43). Troops, in contrast, demonstrated moderate digital literacy (M=3.37) but significantly higher

motivation (M=4.23) and support for compulsory AI-ESMP training (M=4.30).

The joint display matrix integrated quantitative and qualitative results, revealing a clear divergence in

perspectives. Officers emphasized policy alignment, security, and infrastructural readiness, while troops valued

AI’s flexibility, feedback, and practical mission relevance.

Barriers

The following bar chart visually illustrates the comparison between officers’ and troops’ perceptions of key

variables.

The results indicate a complementary readiness profile between officers and troops. Officers, with their digital

competence and institutional authority, are positioned as facilitators of policy and infrastructure. Their low

language needs of officers and troops, while recent ESMP scholarship situates these needs within

peacekeeping contexts. AI learning theories support the observed preference for adaptive, scenario-based

learning, while defense research aligns with officers’ recognition of institutional modernization. The

convergence of theory and results thus confirms the value of an AI-ESMP Adaptive Framework for UN

peacekeeping.

However, one of the factors behind the cautious approach of the officers had been that by incorporating the AI-

ESMP Adaptive Framework for the Pakistan Military, as cautioned by Bannister et al. (2023), its application

This study concludes that an AI-ESMP Adaptive Framework is both feasible and desirable for the Pakistan

Army. By embedding mission-driven communicative tasks within AI-enabled environments, the framework

balances officer-level endorsement with troop-level motivation. To implement such a framework, the study

non-financial influences.

The following have been the contributions of the Authors:

 Unaiza Khudai collected data, analyzed, and compiled

 Dr Shanti Chandran supervised and guided the design and conduct of the research

 Dr. Marsha proofread the composition and checked for technical detailing of the paper, and helped in

revising the final manuscript.

The authors would like to express their sincere gratitude to Major Rab Nawaz, Retd, for facilitating the

conduct of research and collection of data for the study.

19(4), 55–73.