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Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S. et al. (2023). Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. JMIR Medical Education, 9, e48291.
https://doi.org/10.2196/48291

has been cited by the following article:

• TITLE: An Empirical Study on the “High Criticality, Low Strategizing” Profile: College Students’ Human-AI Interaction Competence and Implications for Foreign Language Education

  AUTHORS: Xiaoping Mai

  KEYWORDS: Human-AI Interactive Negotiation Competence (HAINC), Foreign Language Education, Empirical Study, Pedagogical Implications

  JOURNAL NAME: Open Journal of Modern Linguistics, Vol.15 No.6, November 14, 2025

  ABSTRACT: This study investigates the current state of college students’ Human-AI Interactive Negotiation Competence (HAINC) and explores its pedagogical implications for foreign language education. Based on a theoretical framework comprising five core components, a 16-item questionnaire was developed and administered to 290 undergraduate students. Results reveal a distinct “high criticality, low strategizing” profile: while students demonstrate strong critical evaluation and iterative optimization skills (Adjusting Strategies, M = 3.83; Analyzing Feedback, M = 3.79), they exhibit significant weaknesses in strategic command, particularly in task decomposition (M = 3.70) and role-playing (M = 3.60). The study proposes a structured integration of HAINC training into foreign language curricula through a practice-oriented, incremental, and feedback-driven approach to transform students from passive AI users into strategic “conductors”, thereby mitigating the risks of over-reliance and enhancing cognitive engagement.