Artificial intelligence (AI), particularly generative technologies, and large language models are transforming modern education. On the one hand, these tools can automate certain aspects of teaching, personalize educational materials, and improve learning efficiency. However, concerns are emerging regarding the impact of AI on the quality of education, the mental health of students, and fundamental academic values. This article reviews the scientific literature on the applications of artificial intelligence in education, with a special emphasis on its impact on student mental health. Based on an analysis of 120 scientific articles, the study automatically extracted data on AI implementation's potential opportunities and threats. A novel aspect of this work is identifying factors that can be considered both opportunities and threats depending on context. In addition, a frequency analysis of keywords and phrases uncovered many opportunities and challenges. All identified aspects are characterized in the article. The article also highlights key barriers to using large language models (LLMs) for detecting student mental health issues, such as underestimating suicide risk, difficulties with interpreting subtle language, biases in training data, lack of cultural sensitivity, and unresolved ethical concerns. These challenges illustrate why generative AI is not yet reliable for supporting student mental health, especially in high-risk situations. One of the key conclusions is that the use of generative AI to support student mental health is seldom addressed in existing review articles, likely due to the current unreliability of this technology.
Marcin Jukiewicz (2025) studied this question.