Imagine a huge classroom where different versions of Google’s Gemini sit side‑by‑side answering the same homework and exam questions. A panel of judges then scores which Gemini answers are most helpful for students. This paper is about building that classroom arena and seeing how good Gemini really is as a learning assistant.
Institutions and edtech companies don’t know how reliable and pedagogically effective a general‑purpose LLM like Gemini actually is for real learning tasks (explanations, feedback, step‑by‑step help). This work creates a controlled ‘arena’ to systematically evaluate Gemini on educational tasks so decision‑makers can judge whether, where, and how to safely use it for instruction and assessment support.
If productized, the moat would come from (a) a rigorously curated evaluation benchmark of real educational tasks and rubrics, and (b) a standardized arena framework for comparing AI tutors across models, which could become a de‑facto standard for universities and edtech vendors.
Frontier Wrapper (GPT-4)
Context Window Stuffing
Medium (Integration logic)
Evaluation cost and throughput if human raters are involved; prompt/context length limits for complex multi-step learning tasks.
Early Adopters
Focuses specifically on rigorous, arena-style evaluation of Gemini’s educational usefulness rather than generic benchmark scores, providing a more decision-relevant view for learning environments.