Evaluating Generative AI Tools for Student Learning: An Analytic Hierarchy Process Approach
Abstract: This study evaluates seven generative AI platforms for student learning using the Analytic Hierarchy Process. Seven criteria were considered: accuracy, explanation quality, mathematical ability, research assistance, coding support, ease of use, and reliability. Pairwise-comparison data were collected from 500 students and analyzed to derive criterion and alternative priorities. The reported model assigns the greatest importance to research assistance and explanation quality. The composite evaluation ranks ChatGPT first, followed by Gemini, Qwen, Perplexity Claude, Microsoft Copilot, and DeepSeek. The comparison indicates that platform suitability varies across academic tasks and that overall rankings should be interpreted according to the criteria and judgments used in the AHP model. The findings provide a structured framework for selecting generative AI tools for different student-learning requirements.
Keywords: Analytic Hierarchy Process (AHP), ChatGPT, Gemini, Claude, Perplexity, Microsoft Copilot, DeepSeek, and Qwen, Generative artificial intelligence, Multi-criteria decision-making, and Educational technology
How to Cite?: Nikith Nannapaneni, Dr. A.V.S. Prasad, "Evaluating Generative AI Tools for Student Learning: An Analytic Hierarchy Process Approach", Volume 15 Issue 10, October 2026, International Journal of Science and Research (IJSR), Pages: 346-352, https://www.ijsr.net/getabstract.php?paperid=SR261004213026, DOI: https://dx.doi.org/10.21275/SR261004213026