Hanna Shayner’s research published in Academic Visions on July 18, 2026, details the integration of generative AI and large language models (LLMs) into legal education for junior law students. The study advocates for shifting from traditional knowledge-based learning to activity-based models by leveraging AI to handle routine tasks such as searching regulatory frameworks and analyzing large datasets. By utilizing case studies of US and Chinese judicial precedents, the author demonstrates how AI acts as an adaptive contextual tutor for English for Specific Purposes (ESP). This approach transforms passive language learning into active communication via role-play simulations and instant feedback.

The author proposes a dualistic modular framework for AI competence, split into an invariant module covering universal prompt engineering and academic autonomy, and a variable module focused on specialized legal software. The research highlights significant pedagogical risks including neural network hallucinations (generated content that is factually incorrect), AI-driven plagiarism, and the risk of clip-based thinking (reduced attention spans due to fragmented media consumption). Students are tasked with serving as critical supervisors of these digital tools, requiring advanced verification against official legislative registries.

To address these challenges, the study recommends institutional regulation of AI usage and a restructuring of State Final Examination (SFE) formats to emphasize oral examinations and practice-oriented case studies. These findings serve as a foundation for updating the "Law" specialty (081) curricula and provide actionable guidance for teacher training programs in professional pre-higher education institutions.

Hanna Shayner’s research published in Academic Visions on July 18, 2026, details the integration of generative AI and large language models (LLMs) into legal education for junior law students. The study advocates for shifting from traditional knowledge-based learning to activity-based models by leveraging AI to handle routine tasks such as searching regulatory frameworks and analyzing large datasets. By utilizing case studies of US and Chinese judicial precedents, the author demonstrates how AI acts as an adaptive contextual tutor for English for Specific Purposes (ESP). This approach transforms passive language learning into active communication via role-play simulations and instant feedback.

The author proposes a dualistic modular framework for AI competence, split into an invariant module covering universal prompt engineering and academic autonomy, and a variable module focused on specialized legal software. The research highlights significant pedagogical risks including neural network hallucinations (generated content that is factually incorrect), AI-driven plagiarism, and the risk of clip-based thinking (reduced attention spans due to fragmented media consumption). Students are tasked with serving as critical supervisors of these digital tools, requiring advanced verification against official legislative registries.

To address these challenges, the study recommends institutional regulation of AI usage and a restructuring of State Final Examination (SFE) formats to emphasize oral examinations and practice-oriented case studies. These findings serve as a foundation for updating the "Law" specialty (081) curricula and provide actionable guidance for teacher training programs in professional pre-higher education institutions.