v273: Proceedings of iRAISE 2025

2026-06-04 · Source: Proceedings of Machine Learning Research · Field: Education & Learning — Educational Technology (EdTech), Online Learning & Digital Education, Academic Research & Higher Education · Depth: Expert, short

Summary

Volume 273 of the "Proceedings of the Innovation and Responsibility in AI-Supported Education Workshop", held on March 03, 2025, in Philadelphia, Pennsylvania, USA, compiles research on the evolving role of artificial intelligence in educational settings. Edited by Zichao Wang, Simon Woodhead, Muktha Ananda, Debshila Basu Mallick, James Sharpnack, and Jill Burstein, the volume features a keynote on AI and handwriting analysis for pathological challenges. Spotlight papers explore AI mentors for student projects, improving LLM-based automatic essay scoring, rethinking math benchmarks, and evaluating classroom observations with large language models. Further contributions detail a human-centered framework for AI instructional design, hyper-personalized stories for social emotional learning, and assessing LLMs for automated programming feedback. The collection also addresses fairness in AI-assisted remote proctoring, compares GPT-4 with BERT for open-response assessment, and introduces a system for multimodal learning analytics dashboards. Poster presentations cover topics from representational alignment in teaching to personalized AI coaches and multilingual LLM assessment.

Key takeaway

For AI Scientists and Research Scientists developing educational technologies, this volume highlights critical areas for innovation and responsible deployment. You should consider integrating LLMs for tasks like automated feedback, essay scoring, and personalized coaching, while rigorously evaluating fairness in systems such as remote proctoring. Focus on human-centered design frameworks like ARCHED to ensure transparency and collaboration in AI-assisted instructional design, addressing both technical efficacy and ethical implications in your work.

Key insights

AI, particularly LLMs, is transforming education through diverse applications, necessitating responsible innovation.

Principles

• AI integration in education requires balancing innovation with responsibility.
• Large Language Models offer versatile capabilities for educational support.
• Fairness and transparency are crucial considerations for AI in education.

Method

ARCHED provides a human-centered framework for transparent, responsible, and collaborative AI-assisted instructional design.

In practice

• Implement AI mentors to spot early issues in student proposals.
• Utilize LLMs for automatic essay scoring with linguistic features.
• Develop AI coaches for personalized self-directed learning assistance.

Topics

• AI in Education
• Large Language Models
• Automated Assessment
• Personalized Learning
• AI Ethics
• Instructional Design

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Student

Related on AIssential

• The greatest risk of AI in higher education isn’t cheating – it’s the erosion of learning itself — AI's pervasive integration in higher education risks eroding fundamental learning processes and mentorship structures.
• SCRIPT: Implementing an Intelligent Tutoring System for Programming in a German University Context — SCRIPT is a Python ITS designed for advanced learners, integrating LLMs and strict EU data privacy.
• I built an app for a teacher to save her time grading. — AI tools can effectively augment human expertise by proposing drafts for validation, significantly reducing repetitive tasks.
• AI and CS Teaching — AI's impact on CS education necessitates curriculum reform and effective AI tutor integration over anti-cheating focus.
• 95% of UK students now use AI and their experiences couldn't be more divided — Widespread AI adoption among UK students creates a critical gap in university support and raises concerns about skill development.
• Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance — Modularizing educational LLM agents fosters responsible learning assistance by integrating pedagogical guidance.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Proceedings of Machine Learning Research.