Evaluating the Effectiveness of AI Tools in Mathematical Modelling of Various Life Phenomena: A Proposed Approach

Hadeel N. Abosaooda · Syaiba Balqish Ariffin · Osamah Mohammed Alyasiri · Ameen A. Noor

Advances in artificial intelligence (AI) are transforming the landscape of mathematical modelling in areas including physics, biology, and chemistry. Research suggests that ChatGPT, Gemini, and other AI tools can change the way researchers use simulation and modeling for complex phenomena by helping to produce models faster with less computational complexity and real-time insights. Here, we introduce a novel framework for building mathematical models of life sciences using AI tools for applications in disease dynamics and ecological systems. The approach integrates AI tools into the process for a hybrid model that combines initial model formulations based on AI-assisted discussions and refinements based on expert validation of AI-generated output. To give an example, if we are interested in modelling disease outbreaks, AI platforms such as ChatGPT or Gemini can instantly build a simple susceptible-infectious-recovered (SIR) model. This also helps with high dataset processing and making parameter suggestions based on real-time data, which in turn helps in the dynamic adaptation of models to changing data (e.g. transmission rates or intervention strategies). Likewise, in ecological modelling, AI tools can aid in the generation of predator-prey models that consider these complex interactions, such as habitat fragmentation or reserved zones and then suggest parameter sensitivities based on observed trends. These abilities make the future of AI-based mathematical modelling especially exciting, as they will further decrease the time that is traditionally spent by researchers on manually defining models and allow them to focus on result interpretation and strategic decision-making. With the rapidly changing advances in AI tools, incorporating some new capabilities and developments in the mathematical modelling procedure may allow for unprecedented improvements in predictive performance, model flexibility and interdisciplinary investigations. Further research and real-world efforts with this approach are needed to determine if AI tools can improve the cost-effectiveness and affordability of mathematical modelling in many fields of science.

Evaluating AI Language Models in News Retrieval: A Comparative Study Of ChatGPT-Plus and DeepSeek (R1)

Omar Al-Janabi · Osamah Mohammed Alyasiri · Elaf Ayyed Jebur · Shahad Mohgoob Nafl

The increasing complexity of how humans interact with and process information has demonstrated significant advancements in Natural Language Processing (NLP), transitioning from task-specific architectures to generalized frameworks applicable across multiple tasks. Despite their success, challenges persist in specialized domains such as translation, where instruction tuning may prioritize fluency over accuracy. Against this backdrop, the present study conducts a comparative evaluation of ChatGPT-Plus and DeepSeek (R1) on a high-fidelity bilingual retrieval-and-translation task. A single standardize prompt directs each model to access the Arabic-language news section of the College of Medicine, University of Baghdad, retrieve the three most recent articles, and translate them into English. ChatGPT-Plus fulfilled the prompt successfully, extracting authentic Arabic content and delivering fluent, semantically accurate English translations. DeepSeek (R1), by contrast, failed to retrieve the requested articles and instead produced only generic procedural advice – evidence of its lack of real-time web access and a retrieval-augmented generation (RAG) mechanism.