In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.

The integration of advanced generative AI has profoundly influenced higher education, especially in language learning. In Japan’s exam-focused and teacher-centered English education system, students have few opportunities to use generative AI to enhance critical skills like analysis, evaluation, and creativity. Learning often remains passive, with an emphasis on memorization and testing rather than on higher-order thinking skills (HOTS). To address these issues, the authors propose a new AI-driven approach to language teaching that combines HOTS with lower-order thinking skills (LOTS), based on Bloom’s revised taxonomy. This method aims to promote deeper engagement, critical thinking, and active learning through AI-assisted activities such as information synthesis and problem-solving. The approach utilizes large language models, like LLaMA 3, to offer personalized learning experiences tailored to students’ English abilities and individual interests. Pre-prompts guide learning toward specific objectives such as vocabulary, grammar, and pronunciation. Furthermore, the plan includes integrating the system with an AI assistant robot to increase interactivity and immersion. While concerns about diminished human interaction, privacy, algorithmic bias, and accessibility persist, AI-powered language learning has the potential to bring significant advantages to Japan’s higher education system. By providing a supportive, non-judgmental environment, generative AI can help overcome the challenge of communication in English, boost student confidence, and support the digital transformation of education, ultimately aiding students in preparing to live in a global society.