AI Ed Researcher

Authors: Rahul R. Divekar, Sophia Guerra, Lisette Gonzalez, Natasha Boos

Abstract: While Large Language Models (LLMs) have transformed the user interface for learning, moving from keyword search to natural language dialogue, their impact on educational outcomes remains unclear. We present a controlled study (N=20) that directly compares the learning interaction and outcomes between LLM and search-based interfaces. We found that although LLMs elicit richer and nuanced interactions from a learner, they do not produce broadly better learning outcomes. In this paper, we explore this the “Interaction-Outcome Paradox.” To explain this, we discuss the concept of a cognitive shift: the locus of student effort moves from finding and synthesizing disparate sources (search) to a more self-aware identification and articulation of their knowledge gaps and strategies to bridge those gaps (LLMs). This insight provides a new lens for evaluating educational technologies, suggesting that the future of learning tools lies not in simply enriching interaction, but in designing systems that scaffold productive cognitive work by leveraging this student expressiveness.

Link: https://arxiv.org/abs/2511.09458

Authors: Amol Harsh, Brainerd Prince, Siddharth Siddharth, Deepan Raj Prabakar Muthirayan, Kabir S Bhalla, Esraaj Sarkar Gupta, Siddharth Sahu

Abstract: This research-to-practice full paper was inspired by the persistent challenge in effective communication among engineering students. Public speaking is a necessary skill for future engineers as they have to communicate technical knowledge with diverse stakeholders. While universities offer courses or workshops, they are unable to offer sustained and personalized training to students. Providing comprehensive feedback on both verbal and non-verbal aspects of public speaking is time-intensive, making consistent and individualized assessment impractical. This study integrates research on verbal and non-verbal cues in public speaking to develop an AI-driven assessment model for engineering students. Our approach combines speech analysis, computer vision, and sentiment detection into a multi-modal AI system that provides assessment and feedback. The model evaluates (1) verbal communication (pitch, loudness, pacing, intonation), (2) non-verbal communication (facial expressions, gestures, posture), and (3) expressive coherence, a novel integration ensuring alignment between speech and body language. Unlike previous systems that assess these aspects separately, our model fuses multiple modalities to deliver personalized, scalable feedback. Preliminary testing demonstrated that our AI-generated feedback was moderately aligned with expert evaluations. Among the state-of-the-art AI models evaluated, all of which were Large Language Models (LLMs), including Gemini and OpenAI models, Gemini Pro emerged as the best-performing, showing the strongest agreement with human annotators. By eliminating reliance on human evaluators, this AI-driven public speaking trainer enables repeated practice, helping students naturally align their speech with body language and emotion, crucial for impactful and professional communication.

Link: https://arxiv.org/abs/2511.04995

Authors: Sabina Elkins, Ekaterina Kochmar, Jackie C.K. Cheung, Iulian Serban

Abstract: Question generation (QG) is a natural language processing task with an abundance of potential benefits and use cases in the educational domain. In order for this potential to be realized, QG systems must be designed and validated with pedagogical needs in mind. However, little research has assessed or designed QG approaches with the input from real teachers or students. This paper applies a large language model-based QG approach where questions are generated with learning goals derived from Bloom’s taxonomy. The automatically generated questions are used in multiple experiments designed to assess how teachers use them in practice. The results demonstrate that teachers prefer to write quizzes with automatically generated questions, and that such quizzes have no loss in quality compared to handwritten versions. Further, several metrics indicate that automatically generated questions can even improve the quality of the quizzes created, showing the promise for large scale use of QG in the classroom setting.

Link: https://arxiv.org/abs/2401.05914

Authors: Haowei Hua(1), Hong Jiao(2), Xinyi Wang(3) ((1) Princeton University, (2) University of Maryland, College Park, (3) University of Maryland, College Park & Beijing Normal University)

Abstract: BERT and its variants are extensively explored for automated scoring. However, a limit of 512 tokens for these encoder-based models showed the deficiency in automated scoring of long essays. Thus, this research explores generative language models for automated scoring of long essays via summarization and prompting. The results revealed great improvement of scoring accuracy with QWK increased from 0.822 to 0.8878 for the Learning Agency Lab Automated Essay Scoring 2.0 dataset.

Link: https://arxiv.org/abs/2510.22830

Authors: Hamed Mahdavi(Pennsylvania State University), Pouria Mahdavinia(Pennsylvania State University), Alireza Farhadi(Amirkabir University of Technology), Pegah Mohammadipour(Pennsylvania State University), Samira Malek(Pennsylvania State University), Majid Daliri(New York University), Pedram Mohammadipour(Amirkabir University of Technology), Alireza Hashemi(City University of New York), Amir Khasahmadi(Autodesk), Vasant Honavar(Pennsylvania State University)

Abstract: State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 of 6 problems. Given this progress, we assess how well these models can grade proofs: detecting errors, judging their severity, and assigning fair scores beyond binary correctness. We study proof-analysis capabilities using a corpus of 90 Gemini 2.5 Pro-generated solutions that we grade on a 1-4 scale with detailed error annotations, and on MathArena solution sets for IMO/USAMO 2025 scored on a 0-7 scale. Our analysis shows that models can reliably flag incorrect (including subtly incorrect) solutions but exhibit calibration gaps in how partial credit is assigned. To address this, we introduce agentic workflows that extract and analyze reference solutions and automatically derive problem-specific rubrics for a multi-step grading process. We instantiate and compare different design choices for the grading workflows, and evaluate their trade-offs. Across our annotated corpus and MathArena, our proposed workflows achieve higher agreement with human grades and more consistent handling of partial credit across metrics. We release all code, data, and prompts/logs to facilitate future research.

Link: https://arxiv.org/abs/2510.27094