Intelligent Tutoring Systems for French Language Learning in Police Training: A Big Data-Driven AI Approach

Hue Nguyen Thi

doi:10.48161/qaj.v6n1a2256

Authors

Hue Nguyen Thi Foreign language Department, Vietnam People’s Police Academy, Hanoi 100000, Vietnam. https://orcid.org/0009-0001-6670-8269

DOI:

https://doi.org/10.48161/qaj.v6n1a2256

Keywords:

intelligent tutoring system (ITS), Grench, language learning, police training, light gradient-boosting machine (LightGBM).

Abstract

Communication challenges are frequently encountered by police due to need to interact with French-speaking communities. Law enforcement duties often occur at a fast pace and do not fall under typical language acquisition programs. The proposed research aims to provide law enforcement agencies, through the development of an Intelligent Tutoring System (ITS) based on Big Data, a means for French language acquisition for police-related uses. The ITS will consist of real-time interactive language learning tasks, proficiency assessments and simulated feedback based on the learner's performance, to develop an adaptive and goal-based learning program. Enhanced pre-processing techniques, including cleaning, tokenization or lemmatization, will ensure that any data entered into the ITS is of high quality. The ITS will include a Light Gradient Boosting Machine (LightGBM) that utilizes the predicted performance of learners and categorizes language-learning errors. The ITS will also include a Deep Q-Network (DQN) to automatically change the content of language lessons based on learners' engagement and progress. This represents a new ITS design model for the police training environment that integrates language with operational needs, a consideration that has thus far not been addressed in existing models. Assessment data of the ITS indicate substantial improvements in learner attention, rate of error correction and speed of learning. This research offers insight into the establishment of infrastructure as well as the implementation of strategies surrounding real-time, adaptively learned systems in the high-stakes context. In addition, it presents an opportunity through a modular design for police and multilingual communities to better communicate with one another, thereby enhancing the exchange of trust and increased citizen participation, through training opportunities for law enforcement personnel, in a manner that allows them to become better prepared for a more positive outcome.

Downloads

Download data is not yet available.

References

Ayuningsih, H. D. (2024). The importance of open communication in police organizations. IJIRCS: International Journal of Islamic Religion and Culture Studies, 2(2), 23–33.

Fielding, N. G. (2023). Police communications and social media. European Journal of Criminology, 20(1), 316–334.

Ndhlovu, F., Mulrooney, K., Mutongwizo, T., & Harkness, A. (2022). Mapping intercultural communication imperatives of police–public interactions in rural spaces. International Journal of Rural Criminology, 7(1), 134–147.

Dekusar, G., & Lahun, K. (2022). The role of motivation in learning a foreign language by future law officers. Journal of Legal and Law Enforcement Studies, 5, 336–342.

Frank, P. (2023). Community policing: The challenges of implementation in the United States. Academic Press.

Wu, S., Cao, Y., Cui, J., Li, R., Qian, H., Jiang, B., & Zhang, W. (2024). A comprehensive exploration of personalized learning in smart education: From student modeling to personalized recommendations. arXiv Preprint.

Maghsudi, S., Lan, A., Xu, J., & van der Schaar, M. (2021). Personalized education in the artificial intelligence era: What to expect next. IEEE Signal Processing Magazine, 38(3), 37–50.

Maity, S., & Deroy, A. (2024). Generative AI and its impact on personalized intelligent tutoring systems. arXiv Preprint.

Marouf, A., Al-Dahdooh, R., Ghali, M. J. A., Mahdi, A. O., Abunasser, B. S., & Abu-Naser, S. S. (2024). Enhancing education with artificial intelligence: The role of intelligent tutoring systems. International Journal of Advanced Educational Technologies, 6(1), 1–12.

Ahmed, F. Y., Masli, A. A., Khassawneh, B., Yousif, J. H., & Zebari, D. A. (2023). Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network. Computers, 12(9), 179.

Mahmoud, C. F., & Sørensen, J. T. (2024). Artificial intelligence in personalized learning with a focus on current developments and future prospects. Research and Advances in Education, 3(8), 25–31.

Ralph, L., & Robinson, P. (2023). Assessing police social media practices through a democratic policing lens. International Journal of Police Science & Management, 25(3), 237–249.

Lutfiani, N., & Meria, L. (2022). Utilization of big data in educational technology research. International Transactions on Education Technology, 1(1), 73–83.

An, Z., Wang, C., Li, S., Gan, Z., & Li, H. (2021). Technology-assisted self-regulated English language learning: Associations with English language self-efficacy, English enjoyment, and learning outcomes. Frontiers in Psychology, 11, 558466.

Murtinger, M., Uhl, J. C., Atzmüller, L. M., Regal, G., & Roither, M. (2024). Sound of the police—Virtual reality training for police communication for high-stress operations. Multimodal Technologies and Interaction, 8(6), 46.

Tripathi, I. (2021). Artificial intelligence-driven teaching methods for English language and literature in big data environments. Critical Reference to Pastoral Elements in English Literature, 28, 55–62.

Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2022). Automated data-driven generation of personalized pedagogical interventions in intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 32(2), 323–349.

Krasnopolskyi, V., Khairulina, N., Tyshakova, L., Dubova, H., Huliak, T., & Pinkovska, M. (2023). Formation of foreign language competence of future lawyers and law enforcement officers: Contemporary challenges. Multidisciplinary Science Journal, 5, 1–12.

Simon, G. Ü., Borszéki, J., Barnucz, N., & Uricska, E. (2022). The role of languages for law enforcement purposes in higher education: Introduction of a new foreign language strategy. Journal of Law Enforcement Education, 4(1), 15–28.

Levashov, O. S., & Orlova, O. O. (2021). Ways to improve foreign language teaching in police officers’ training. Law & Safety, 49, 65–72.

Li, Z., Yang, J., Wang, J., Shi, L., & Stein, S. (2024). Integrating LSTM and BERT for long-sequence data analysis in intelligent tutoring systems. arXiv Preprint.

Yamijala, S. M. S., Chodisetty, R. M., Chakravorty, C., & Sai, K. P. (2025). AI-powered learning revolutionizing smart education with personalized learning styles. In Internet of behavior-based computational intelligence for smart education systems (pp. 191–212). IGI Global.

Karunarathna, K. M. G. S., & Rupasingha, R. A. H. M. (2022). Learning to use normalization techniques for preprocessing and classification of text documents. International Journal of Multidisciplinary Studies, 9(2), 69-81.