Artificial intelligence could be used as a powerful and innovative tool in mathematics education. It is poised to transform the way of learning and teaching this subject.The main objective of our study is to provide a more complete and thorough understanding of the role and impact of using AI in mathematics education by determining the trends, the AI methods, the technological applications and the opportunities for utilizing AI by teachers and students. The potential benefits and threats caused by the use of AI are also discussed.

Manual and automatic verification of the trustworthiness of information is an important task. Knowing whether the author of a statement was an eyewitness to the reported event(s) is a useful clue. In linguistics, such information is expressed through “evidentiality”. Evidentials are especially important in Bulgarian, as Bulgarian journalists often use a specific type of evidential (“renarrative”) to report events that they did not directly observe, nor verify. Unfortunately, there are no automatic tools to detect Bulgarian renarrative. This article presents the first two automatic solutions for this task. Specifically – a fine-tuned BERT classifier (renarrative BERT detector, BGRenBERT), achieving 0.98 Accuracy on the test split, and a renarrative rule-based detector (BGRenRules), created with regular expressions, matching a parser’s output. Both solutions detect Bulgarian texts containing the most frequently encountered forms of renarrative. Additionally, we compare the results of the two detectors with the manual annotation of subsets of two Bulgarian fake text datasets. BGRenRules obtains substantially higher results than BGRenBERT. The error analysis shows that the errors from BGRenRules most frequently correspond to cases in which humans also have doubts. The training dataset (BgRenData), the annotated dataset subsets, and the two detectors are made publicly accessible on Zenodo, GitHub, and HuggingFace. We expect that these new resources will be of invaluable assistance to 1) Bulgarian-language researchers, 2) researchers of other languages with similar phenomena, especially those working on verifying information.

This study analyzes electricity consumption in a poultry farm using hourly measurements aggregated into seasonal and annual profiles. We develop sinusoidal models to capture the diurnal cyclical behavior and quantify model performance via the coefficient of determination ($\mathrm{R}^{2}$). The results show strong seasonal dependence in both daily energy use and hourly load shapes, with summer exhibiting the highest demand (cooling and ventilation) and spring the lowest. The fitted sinusoidal models provide an accurate representation of the cyclic pattern across seasons and the annual profile, enabling improved forecasting and operational planning.

Accurate forecasting of photovoltaic (PV) energy production is essential for effective planning, grid integration, and sustainable energy management. This study focuses on predicting the energy output of several PV plants located in different regions, employing Prophet and NeuralProphet models, including configurations with autoregressive components. Historical data on energy production and related environmental parameters are analyzed to build forecasting models that capture both short- and long-term trends. The performance of the models is evaluated using multiple statistical error metrics, ensuring the reliability of the generated forecasts. The results demonstrate that while Prophet outperforms baseline NeuralProphet in most cases, the inclusion of autoregressive lags in NeuralProphet reduces forecast errors by an order of magnitude, highlighting the importance of historical production patterns. These findings emphasize the potential of advanced data-driven forecasting techniques to support decision-making processes in renewable energy and enhance the integration of PV systems into energy networks.

As of early 2026, Artificial Intelligence (AI) has transitioned from an experimental auxiliary tool to a foundational pillar of modern mathematical pedagogy. This paper evaluates the rapid evolution of AI-driven mathematics tools over the academic year 2025–2026, emphasizing the shift toward adaptive, Socratic tutoring systems. We analyze key platforms and tools such as Khanmigo, Mathos AI, and Tutor CoPilot, assessing their impact on student mastery and teacher efficiency. Empirical studies from recent years suggest that AI-assisted human tutoring can lead to measurable improvements in student topic mastery. However, the integration of AI in mathematics education faces ongoing challenges regarding cognitive over-reliance and data privacy. This review synthesizes contemporary research to provide a framework for balanced AI integration that prioritizes conceptual understanding over automated problem-solving.