AITutor-EvalKit: Exploring the Capabilities of AI Tutors
- URL: http://arxiv.org/abs/2512.03688v1
- Date: Wed, 03 Dec 2025 11:27:50 GMT
- Title: AITutor-EvalKit: Exploring the Capabilities of AI Tutors
- Abstract summary: We present AITutor-EvalKit, an application that uses language technology to evaluate the pedagogical quality of AI tutors.<n>It provides software for demonstration and evaluation, as well as model inspection and data visualization.
- Score: 15.444428064780363
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: We present AITutor-EvalKit, an application that uses language technology to evaluate the pedagogical quality of AI tutors, provides software for demonstration and evaluation, as well as model inspection and data visualization. This tool is aimed at education stakeholders as well as *ACL community at large, as it supports learning and can also be used to collect user feedback and annotations.
Related papers
- Usability Evaluation and Improvement of a Tool for Self-Service Learning Analytics [0.0]
Self-Service Learning Analytics (SSLA) tools aim to support educational stakeholders in creating learning analytics indicators without requiring technical expertise.<n>This paper presents a usability evaluation and improvement of the Indicator Editor, a no-code, exploratory SSLA tool.
arXiv Detail & Related papers (2026-03-25T14:01:53Z) - Draw2Learn: A Human-AI Collaborative Tool for Drawing-Based Science Learning [0.0]
Drawing supports learning by externalizing mental models, but providing timely feedback at scale remains challenging.<n>We present Draw2Learn, a system that explores how AI can act as a supportive teammate during drawing-based learning.
arXiv Detail & Related papers (2026-02-02T00:06:08Z) - LecEval: An Automated Metric for Multimodal Knowledge Acquisition in Multimedia Learning [58.98865450345401]
We introduce LecEval, an automated metric grounded in Mayer's Cognitive Theory of Multimedia Learning.<n>LecEval assesses effectiveness using four rubrics: Content Relevance (CR), Expressive Clarity (EC), Logical Structure (LS) and Audience Engagement (AE)<n>We curate a large-scale dataset of over 2,000 slides from more than 50 online course videos, annotated with fine-grained human ratings.
arXiv Detail & Related papers (2025-05-04T12:06:47Z) - MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors [82.91830877219822]
We present MathTutorBench, an open-source benchmark for holistic tutoring model evaluation.<n>MathTutorBench contains datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialog-based teaching.<n>We evaluate a wide set of closed- and open-weight models and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.
arXiv Detail & Related papers (2025-02-26T08:43:47Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - Large Language Models Meet User Interfaces: The Case of Provisioning Feedback [6.626949691937476]
We present a framework for incorporating GenAI into educational tools and demonstrate its application in our tool, Feedback Copilot.
This work charts a course for the future of GenAI in education.
arXiv Detail & Related papers (2024-04-17T05:05:05Z) - Improving the Validity of Automatically Generated Feedback via Reinforcement Learning [46.667783153759636]
We propose a framework for feedback generation that optimize both correctness and alignment using reinforcement learning (RL)<n>Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO)
arXiv Detail & Related papers (2024-03-02T20:25:50Z) - A Toolbox for Modelling Engagement with Educational Videos [21.639063299289607]
This work presents the PEEKC dataset and the TrueLearn Python library, which contains a dataset and a series of online learner state models.
The dataset contains a large amount of AI-related educational videos, which are of interest for building and validating AI-specific educational recommenders.
arXiv Detail & Related papers (2023-12-30T21:10:55Z) - Democratize with Care: The need for fairness specific features in
user-interface based open source AutoML tools [0.0]
Automated Machine Learning (AutoML) streamlines the machine learning model development process.
This democratization allows more users (including non-experts) to access and utilize state-of-the-art machine-learning expertise.
However, AutoML tools may also propagate bias in the way these tools handle the data, model choices, and optimization approaches adopted.
arXiv Detail & Related papers (2023-12-16T19:54:00Z) - VisIT-Bench: A Benchmark for Vision-Language Instruction Following
Inspired by Real-World Use [49.574651930395305]
VisIT-Bench is a benchmark for evaluation of instruction-following vision-language models.
Our dataset comprises 592 test queries, each with a human-authored instruction-conditioned caption.
We quantify quality gaps between models and references using both human and automatic evaluations.
arXiv Detail & Related papers (2023-08-12T15:27:51Z) - Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
This paper presents an approach to deriving a learner model directly from an assessment rubric.
We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
arXiv Detail & Related papers (2022-09-07T10:09:12Z) - Explainable Predictive Process Monitoring: A User Evaluation [62.41400549499849]
Explainability is motivated by the lack of transparency of black-box Machine Learning approaches.
We carry on a user evaluation on explanation approaches for Predictive Process Monitoring.
arXiv Detail & Related papers (2022-02-15T22:24:21Z) - AI Explainability 360: Impact and Design [120.95633114160688]
In 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods.
This paper examines the impact of the toolkit with several case studies, statistics, and community feedback.
The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
arXiv Detail & Related papers (2021-09-24T19:17:09Z) - Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool [0.9899017174990579]
We present a new Real-time Educational AI-powered Classroom Tool that employs EDM techniques for supporting the decision-making process of educators.
ReACT is a data-driven tool with a user-friendly graphical interface.
It analyzes students' performance data and provides context-based alerts as well as recommendations to educators for course planning.
arXiv Detail & Related papers (2021-07-30T03:09:59Z) - Automated Personalized Feedback Improves Learning Gains in an
Intelligent Tutoring System [34.19909376464836]
We investigate how automated, data-driven, personalized feedback in a large-scale intelligent tutoring system (ITS) improves student learning outcomes.
We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account.
We utilize state-of-the-art machine learning and natural language processing techniques to provide the students with personalized hints, Wikipedia-based explanations, and mathematical hints.
arXiv Detail & Related papers (2020-05-05T18:30:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.