AI in education is a topic that has been researched for the last 70 years. However, the last two years have seen very significant changes. These changes relate to the introduction of OpenAI's ChatGPT chatbot in November 2022. The GPT (Generative Pre-trained Transformer) language model has dramatically influenced how the public approaches artificial intelligence. For many, generative language models have become synonymous with AI and have come uncritically viewed as a universal source of answers to most questions. However, it soon became apparent that even generative language models had their limits. Among the main problems that emerged was hallucination (providing answers containing false or misleading information), which is expected in all language models. The main problem of hallucination is that this information is difficult to distinguish from other information, and AI language models are very persuasive in presenting it. The risks of this phenomenon are much more substantial when using language modules to support learning, where the learner cannot distinguish correct information from incorrect information. The proposed paper focuses on the area of AI hallucination in mathematics education. It will first show how AI chatbots hallucinate in mathematics and then present one possible solution to counter this hallucination. The presented solution was created for the AI chatbot Edu-AI and designed to tutor students in mathematics. Usually, the problem is approached so that the system verifies the correctness of the output offered by the chatbot. Within the Edu-AI, checking responses is not implemented, but checking inputs is. If an input containing a factual query is recorded, it is redirected, and the answer is traced to authorised knowledge sources and study materials. If a relevant answer cannot be traced in these sources, a redirect to a natural person who will address the question is offered. In addition to describing the technical solution, the article includes concrete examples of how the system works. This solution has been developed for the educational domain but applies to all domains where users must be provided with relevant information.

This review study focuses on the teaching of photosynthesis, and it builds on previous review studies. The aim of this study is to present the findings to readers and thus contribute to improving the training of future teachers and student education. This study conducted a critical review of the topic of photosynthesis education, by examining 80 systematically selected articles through quantitative and qualitative approaches. The quantitative analysis highlighted the increasing number of studies in recent years and helped identify the main issues, namely teaching methods and misconceptions. In contrast, the qualitative conceptualization showed that photosynthesis education is a key topic for the development of science literacy and serves as a model for how teachers can educate students on difficult and complex issues. An alarming issue is that misconceptions adopted in childhood are powerful and often persist into adulthood (including teachers). Professional education on photosynthesis is not possible without pedagogical content knowledge, the identification of student concepts, activating methods of learning, and methodical work with scientific language and thinking.

Aim. The aim of this paper is to present the results of a pilot validation of an artificial intelligence chatbot for mathematics tutoring. The subject of the analysis is the behaviour of the students in the course, in particular their approach to solving problems without external motivation through scoring or other assessment. Methods. The analysis of student behaviour was carried out on the basis of records in the application database. The subject of the analysis was not only the pupils' responses, but also the time taken to produce them and the overall trajectory through the course. Results. The results of the analysis show that students who choose to join the course, work intensively in the lessons. Most pupils solve more than one basic task. A significant proportion of pupils also use the prepared help and instructional videos. The analysis of the solution time allowed identification of a group of pupils who do not even read the problems and only confirm the results. Conclusion. The pilot validation confirmed that the chat-bot format is close to the learners, and they are able to use the courses created in this environment. Solution time analysis suggests that this parameter can be used to analyse learner behaviour and system control. Further analyses in this area are needed.

Acquiring the language of the host country is an important factor that influences the process of integration of immigrants into society. For the children of immigrants, proficiency in the official language of their new country is crucial for their success and their smooth integration into the education system and the wider community. This article analyses the Czech language proficiency of immigrant children enrolled in lower secondary schools in the Czech Republic and seeks to examine how their language skills develop over time and what factors impact the process of Czech language acquisition. The authors created a special diagnostic instrument to test students' communicative competence in a pilot study they carried out on a sample of immigrant children enrolled in selected lower secondary schools in Prague. The findings of this pilot study confirmed that the linguistic distance between Czech and the students' mother tongue had a significant influence on how and how quickly they mastered the Czech language.

The paper presents one of the possible perspectives on what can be called “good” questions in teaching. This perspective view is based on the definition of “good” questions in Sullivan and Lilburn (2010) and extends it to natural sciences and problems based on interdisciplinary relations. The presented activities are anchored in the Theory of Didactic Situations (Brousseau, 1997) and in Inquiry Based Teaching (e.g. Hodson, 2009; Chapman, 2010)