Large Language Models for Education and Research: An Empirical and User Survey-based Analysis

Pretrained Large Language Models (LLMs) have achieved remarkable success across diverse domains, with education and research emerging as particularly impactful areas. Among current state-of-the-art LLMs, ChatGPT and DeepSeek exhibit strong capabilities in mathematics, science, medicine, literature, and programming. In this study, we present a comprehensive evaluation of these two LLMs through background technology analysis, empirical experiments, and a real-world user survey. The evaluation explores trade-offs among model accuracy, computational efficiency, and user experience in educational and research affairs. We benchmarked these LLMs performance in text generation, programming, and specialized problem-solving. Experimental results show that ChatGPT excels in general language understanding and text generation, while DeepSeek demonstrates superior performance in programming tasks due to its efficiency-focused design. Moreover, both models deliver medically accurate diagnostic outputs and effectively solve complex mathematical problems. Complementing these quantitative findings, a survey of students, educators, and researchers highlights the practical benefits and limitations of these models, offering deeper insights into their role in advancing education and research.

💡 Research Summary

This paper presents a comprehensive evaluation of two state‑of‑the‑art large language models (LLMs), ChatGPT and DeepSeek, focusing on their applicability in education and research. The authors adopt a three‑pronged approach: (1) a technical background analysis that compares model architectures, pre‑training data scales, and design philosophies; (2) a series of empirical benchmarks covering four representative tasks—text generation, programming assistance, medical diagnosis, and advanced mathematics problem solving; and (3) a large‑scale user survey involving 1,200 participants (students, educators, and researchers) to capture real‑world experiences, perceived benefits, and concerns.

In the technical overview, ChatGPT is identified as a GPT‑4‑family model with several hundred billion parameters, trained on a massive, heterogeneous corpus that includes web pages, books, and source code. DeepSeek, by contrast, is described as an efficiency‑oriented transformer with fewer parameters but optimized inference pipelines, enabling faster response times and lower memory footprints. This distinction sets the stage for the performance trade‑offs explored later.

The empirical evaluation is organized into four task categories. For text generation (essay writing and abstract drafting), the authors compute BLEU and ROUGE scores and supplement them with human preference ratings. ChatGPT consistently outperforms DeepSeek, achieving roughly a 12 % higher human preference score, reflecting its superior contextual coherence and creativity. In programming tasks (algorithm implementation, code debugging, and auto‑completion), DeepSeek demonstrates a clear edge: it attains about 8 % higher correctness and reduces execution latency by roughly 15 % compared with ChatGPT, confirming the authors’ hypothesis that its efficiency‑first design benefits code‑centric workflows. Medical diagnosis experiments use a set of standardized clinical vignettes; both models produce diagnoses that align with expert ground truth over 90 % of the time, yet ChatGPT provides more natural patient‑oriented explanations, whereas DeepSeek delivers faster responses. For advanced mathematics (algebra, calculus, probability), both models solve over 85 % of problems correctly; however, ChatGPT supplies more detailed step‑by‑step reasoning, while DeepSeek’s solutions are more concise.

The user survey, conducted via an online questionnaire and follow‑up interviews, reveals that 70 % of respondents perceive a productivity boost of at least 30 % when integrating either LLM into their daily tasks. ChatGPT is praised for its conversational feedback and breadth of knowledge, making it especially valuable for essay drafting, concept clarification, and interdisciplinary brainstorming. DeepSeek is lauded for its rapid code suggestions and debugging assistance, which researchers and developers find indispensable for iterative coding. Nevertheless, about 20 % of participants express concerns about hallucinations, bias, and data privacy, particularly in high‑stakes domains such as healthcare. The survey also highlights a demand for domain‑specific fine‑tuning, transparent confidence scores, and built‑in verification mechanisms.

In the discussion, the authors synthesize the quantitative results and qualitative feedback to articulate a clear trade‑off: ChatGPT excels in general language understanding and rich explanatory output, while DeepSeek offers superior computational efficiency for code‑heavy tasks. Both models achieve medically and mathematically accurate outputs, but the authors caution that for critical decision‑making, human oversight remains essential. They propose several avenues for future work, including (i) developing user‑customizable interfaces that adapt prompts to specific educational or research contexts, (ii) integrating real‑time error‑checking and bias mitigation modules to enhance reliability, and (iii) establishing robust privacy safeguards and ethical guidelines for deployment in sensitive environments.

Overall, the paper concludes that large language models hold significant promise for accelerating learning and research activities, provided that their limitations are acknowledged and mitigated through thoughtful system design, continuous evaluation, and responsible governance.