Evaluating Gemini in an arena for learning
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Abstract (arXiv)
Artificial intelligence (AI) is poised to transform education, but the research community lacks a robust, general benchmark to evaluate AI models for learning. To assess state-of-the-art support for educational use cases, we ran an "arena for learning" where educators and pedagogy experts conduct blind, head-to-head, multi-turn comparisons of leading AI models. In particular, N = 189 educators drew from their experience to role-play realistic learning use cases, interacting with two models sequentially, after which N = 206 experts judged which model better supported the user's learning goals. The arena evaluated a slate of state-of-the-art models: Gemini 2.5 Pro, Claude 3.7 Sonnet, GPT-4o, and OpenAI o3. Excluding ties, experts preferred Gemini 2.5 Pro in 73.2% of these match-ups -- ranking it first overall in the arena. Gemini 2.5 Pro also demonstrated markedly higher performance across key principles of good pedagogy. Altogether, these results position Gemini 2.5 Pro as a leading model for learning.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 13 of its 54 references that are in the map. First posted on arXiv 30 May 2025.
Builds on
13 of 54 references.
References · 13
- 2021Training Verifiers to Solve Math Word Problemscited by 10,109
- 2020Measuring Massive Multitask Language Understandingcited by 7,963
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
- 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
- 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
- 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,080
- 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
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- 2024LearnLM: Improving Gemini for Learningcited by 25
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Citing papers per year: 2025: 6; 2026: 5.
11 papers.
- 20253D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understandingcited by 47
- 2025TeachLM: Post-Training LLMs for Education Using Authentic Learning Datacited by 9
- 2025Benchmarking the Pedagogical Knowledge of Large Language Modelscited by 5
- 2025AI tutoring can safely and effectively support students: An exploratory RCT in UK classroomscited by 3
- 2026Architecting Trust in Artificial Epistemic Agentscited by 3
- 2025MRD: Multi-resolution Retrieval-Detection Fusion for High-Resolution Image Understandingcited by 1
- 2026PEARL: Training Socratic Tutors with Pedagogically Aligned Reinforcement Learningcited by 1
- 2026Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wildcited by 1
- 2026EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learnerscited by 1
- 2025Developing a General Personal Tutor for Educationcited by 0
- 2026Ceci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systemscited by 0
- 2026EduClaw-Bench: A Long-Horizon Benchmark for Pedagogical LLM Agents with Simulated Learners4 Aug 2026
- 2026Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wild10 Jun 2026
- 2026PEARL: Training Socratic Tutors with Pedagogically Aligned Reinforcement Learning28 May 2026
- 2026Ceci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systems28 Apr 2026
- 2026Architecting Trust in Artificial Epistemic Agents3 Mar 2026
- 2025AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms29 Dec 2025
- 2025Developing a General Personal Tutor for Education4 Dec 2025
- 2025MRD: Multi-resolution Retrieval-Detection Fusion for High-Resolution Image Understanding2 Dec 2025
- 2025TeachLM: Post-Training LLMs for Education Using Authentic Learning Data6 Oct 2025
- 20253D-R1: Enhancing Reasoning in 3D VLMs for Unified Scene Understanding31 Jul 2025
- 2025Benchmarking the Pedagogical Knowledge of Large Language Models23 Jun 2025