Tootfinder

T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation
Nirmal Gelal, Chloe Snow, Ambyr Rios, Hande K\"u\c{c}\"uk McGinty
https://arxiv.org/abs/2506.12075

T-TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation
The implementation of transformational pedagogy in secondary education classrooms requires a broad multiliteracy approach. Due to limited planning time and resources, high school English Literature teachers often struggle to curate diverse, thematically aligned literature text sets. This study addresses the critical need for a tool that provides scaffolds for novice educators in selecting literature texts that are diverse -- in terms of genre, theme, subtheme, and author -- yet similar in conte…

Designing Effective LLM-Assisted Interfaces for Curriculum Development
Abdolali Faraji, Mohammadreza Tavakoli, Mohammad Moein, Mohammadreza Molavi, G\'abor Kismih\'ok
https://arxiv.org/abs/2506.11767

Designing Effective LLM-Assisted Interfaces for Curriculum Development
Large Language Models (LLMs) have the potential to transform the way a dynamic curriculum can be delivered. However, educators face significant challenges in interacting with these models, particularly due to complex prompt engineering and usability issues, which increase workload. Additionally, inaccuracies in LLM outputs can raise issues around output quality and ethical concerns in educational content delivery. Addressing these issues requires careful oversight, best achieved through coopera…

Hard to find a single summarizing quote from the post, but it keeps coming back to two closely related ideas:
(1) the tendency of humans to blame themselves for poor tool performance (“oh I should have prompted in •that• way instead, my bad”), and
(2) what we educators call the “hidden curriculum:” people are unaware of learning they have done / habitual effort they are expending, and thus they see their own learning / ongoing effort as zero-cost, obvious, nonexistent, innate personal virtue, etc.
The existence of (2) sets people up for (1); recognizing (2) helps cure (1).
4/

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting
Sankalan Pal Chowdhury, Terry Jingchen Zhang, Donya Rooein, Dirk Hovy, Tanja K\"aser, Mrinmaya Sachan
https://arxiv.org/abs/2506.08702

Educators' Perceptions of Large Language Models as Tutors: Comparing Human and AI Tutors in a Blind Text-only Setting
The rapid development of Large Language Models (LLMs) opens up the possibility of using them as personal tutors. This has led to the development of several intelligent tutoring systems and learning assistants that use LLMs as back-ends with various degrees of engineering. In this study, we seek to compare human tutors with LLM tutors in terms of engagement, empathy, scaffolding, and conciseness. We ask human tutors to annotate and compare the performance of an LLM tutor with that of a human tut…

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Stefan Dascalescu, Adrian Marius Dumitran, Mihai Alexandru Vasiluta
https://arxiv.org/abs/2506.05990

Leveraging Generative AI for Enhancing Automated Assessment in Programming Education Contests
Competitive programming contests play a crucial role in cultivating computational thinking and algorithmic skills among learners. However, generating comprehensive test cases to effectively assess programming solutions remains resource-intensive and challenging for educators. This paper introduces an innovative NLP-driven method leveraging generative AI (large language models) to automate the creation of high-quality test cases for competitive programming assessments. We extensively evaluated o…

Statisticians Training STEM Educators in Statistics Methods and Pedagogy: A Case Study of Instructor Training in Bayesian Methods
Mine Dogucu, Jingchen Hu, Amy H Herring
https://arxiv.org/abs/2505.02298

Statisticians Training STEM Educators in Statistics Methods and Pedagogy: A Case Study of Instructor Training in Bayesian Methods
Educating the next generation of scientists in statistical methodology is an important task. Educating their instructors in statistical content knowledge and pedagogical knowledge is as important and provides an indirect impact of students' learning. Statisticians are in a place to lead train-the-trainer (TTT) programs in different methods. We present our instructor training program in Bayesian methods as an effective case study of a TTT model. In addition to describing the details of the struc…

🗳️ A new virtual summit is in the works—uniting educators, activists, artists & citizens to resist authoritarianism defend democracy. 🛠️ Help shape the vision: take the planning survey − bit.ly/4jhX8tR
#Resist #Democracy

This https://arxiv.org/abs/2505.17371 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

An End-to-End Approach for Child Reading Assessment in the Xhosa Language
Child literacy is a strong predictor of life outcomes at the subsequent stages of an individual's life. This points to a need for targeted interventions in vulnerable low and middle income populations to help bridge the gap between literacy levels in these regions and high income ones. In this effort, reading assessments provide an important tool to measure the effectiveness of these programs and AI can be a reliable and economical tool to support educators with this task. Developing accurate a…

This https://arxiv.org/abs/2504.13883 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signals in Educational Game Playing
This study estimates cognitive effort (CE) based on functional near-infrared spectroscopy (fNIRS) data and performance scores using a hybrid deep learning model. The estimation of CE enables educators to modify material to enhance learning effectiveness and student engagement. Relative neural efficiency (RNE) and relative neural involvement (RNI) are two metrics that have been used to represent CE. To estimate RNE and RNI we need hemodynamic response in the brain and the performance score of a …

Evaluating Gemini in an arena for learning
LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek, Andrea Huber, Ankit Anand, Avishkar Bhoopchand, Brett Wiltshire, Daniel Gillick, Daniel Kasenberg, Eleni Sgouritsa, Gal Elidan, Hengrui Liu, Holger Winnemoeller, Irina Jurenka, James Cohan, Jennifer She, Julia Wilkowski, Kaiz Alarakyia, Kevin R. McKee, Komal Singh, Lisa Wang, Markus Kunesch, Miruna P\^islar, Niv Efron, Parsa Mahmoudieh, Pierre-Alexandre Kamienny, Sara Wiltb…

Evaluating Gemini in an arena for learning
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 se…

Please join me on July 7th for next month's MediaEd Club webinar for the Media Education Lab: "Privacy, Power, and Platforms"
https://mediaeducationlab.com/index.php/events/privacy-power-and-platforms
If you missed today's / this month…

Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG
Zarreen Reza, Alexander Mazur, Michael T. Dugdale, Robin Ray-Chaudhuri
https://arxiv.org/abs/2506.05925

Small Models, Big Support: A Local LLM Framework for Teacher-Centric Content Creation and Assessment using RAG and CAG
While Large Language Models (LLMs) are increasingly utilized as student-facing educational aids, their potential to directly support educators, particularly through locally deployable and customizable open-source solutions, remains significantly underexplored. Many existing educational solutions rely on cloud-based infrastructure or proprietary tools, which are costly and may raise privacy concerns. Regulated industries with limited budgets require affordable, self-hosted solutions. We introduc…

Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications
Vahid Garousi, Zafar Jafarov, Aytan Movsumova, Atif Namazov, Huseyn Mirzayev
https://arxiv.org/abs/2506.00682

Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications
Context: As generative AI (GenAI) tools such as ChatGPT and GitHub Copilot become pervasive in education, concerns are rising about students using them to complete rather than learn from coursework-risking overreliance, reduced critical thinking, and long-term skill deficits. Objective: This paper proposes and empirically applies a causal model to help educators scaffold responsible GenAI use in Software Engineering (SE) education. The model identifies how professor actions, student factors, …

Designing the Future of Entrepreneurship Education: Exploring an AI-Empowered Scaffold System for Business Plan Development
Junhua Zhu, Lan Luo
https://arxiv.org/abs/2505.23326

Designing the Future of Entrepreneurship Education: Exploring an AI-Empowered Scaffold System for Business Plan Development
Entrepreneurship education equips students to transform innovative ideas into actionable entrepreneurship plans, yet traditional approaches often struggle to provide the personalized guidance and practical alignment needed for success. Focusing on the business plan as a key learning tool and evaluation method, this study investigates the design needs for an AI-empowered scaffold system to address these challenges. Based on qualitative insights from educators and students, the findings highlight…

Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education
Yiwei Sun
https://arxiv.org/abs/2506.00057 https://arx…

Hierarchical Bayesian Knowledge Tracing in Undergraduate Engineering Education
Educators teaching entry-level university engineering modules face the challenge of identifying which topics students find most difficult and how to support diverse student needs effectively. This study demonstrates a rigorous yet interpretable statistical approach -- hierarchical Bayesian modeling -- that leverages detailed student response data to quantify both skill difficulty and individual student abilities. Using a large-scale dataset from an undergraduate Statics course, we identified cl…