#AI evaluation
6 論文
Norah Almousa, Shayan Peyghambari Oskoui, Raquel Coelho, Gayle Rogers, Xiang Lorraine Li, Diane Litman
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
FeedTypeは、大学の文章作成コース、6つの言語モデル、3種類のプロンプト戦略でフィードバックの焦点を比較した。モデルは多くの分類を網羅したが、分布や調整の仕方は教師と完全には一致しなかった。フィードバックを検討するための基準であり、学生の文章力向上を実証した研究ではない。
Tanya Nazaretsky, Hagit Gabbay, Tanja Käser
Computers and Education: Artificial Intelligence
Researchers compared human and AI feedback on 979 responses from six university STEM courses, then examined ratings from 472 students. Expert analysis found broadly similar pedagogical quality, while students’ judgments were associated with their views of the source’s credibility. The study did not measure feedback uptake or learning gains. For educators, it raises a practical question: how can students justify an evaluation of advice before deciding whether to use it?
Abubakir Siedahmed, Jaclyn Ocumpaugh, Zelda Ferris, Dinesh Kodwani, Neil Heffernan, Eamon Worden
Proceedings of the 18th International Conference on Educational Data Mining
A study of 394 authentic mathematics responses examined how GPT-4o scores language associated with African American Vernacular English. Changing one targeted dialect feature did not significantly alter scores, while changing the grading prompt did. The latter contrast also changed rubric wording, so it cannot cleanly establish that a dialect instruction removed bias. The study makes a useful case for testing content scoring carefully, with language variation and mathematical reasoning evaluated separately.
Manooshree Patel, Rayna Bhattacharyya, Thomas Lu, Arnav Mehta, Niels Voss, Narges Norouzi, Gireeja Ranade
Proceedings of the AAAI Conference on Artificial Intelligence
LeanTutor connects a language model to the Lean theorem prover to support mathematical proof feedback. Its technical evaluation shows promising hints on a small selected benchmark, but also exposes a crucial boundary: a verifier can check the formal proof only after the learner's words have been translated correctly. The study did not test classroom learning, and feedback evaluation excluded failed translations. It is an early system design with explicit limitations, not a validated replacement for mathematics instruction.
Anthropic
AI Product and Learning Report
Anthropic released Claude Sonnet 5.5 on September 28, positioning it as a faster, lower-cost complement to Opus 5.5 for well-defined work. The company reports improvements in coding, documents, and communication, with efficiency gains dependent on the task. OpenAI's recently released GPT-6 Sol and Luna and Gemini's course-connected support provide context. Educational teams should evaluate the work they actually need, including the review required before a draft becomes usable.
Anthropic
AI Product and Learning Report
Anthropic released Claude Opus 5.5 on September 22, emphasizing stronger coding and knowledge work, clearer communication, and lower operating costs. The same day's GPT-6 Sol and Luna launch broadened the choice of affordable models, while Gemini's established Guided Learning tools offered a contrasting educational purpose. For universities, the opportunity is to compare research workflows carefully while keeping model performance, completed artifacts, and student learning as separate outcomes.