Hasil untuk "Education"

S. Singer, Natalie R. Nielsen, H. Schweingruber

Ivette Motola, L. Devine, H. Chung et al.

A. D. de Boer, S. Pijl, A. Minnaert

Heather Cook, H. Kohl

Ágnes Zsóka, Zsuzsanna Szerényi, Anna Széchy et al.

C. Kelley, Anthony T. Milanowski

M. Hout

Ronald M. Epstein

R. Connell

M. Moore, G. Kearsley

D. Cohen

D. Bligh

G. Glass, K. Hopkins

R. Bringle, Julie A. Hatcher

G. A. Ferguson

Paul C. Helmreich

A. Fayolle

Jarosław A. Chudziak, Adam Kostka

The growing ubiquity of artificial intelligence (AI), in particular large language models (LLMs), has profoundly altered the way in which learners gain knowledge and interact with learning material, with many claiming that AI positively influences their learning achievements. Despite this advancement, current AI tutoring systems face limitations associated with their reactive nature, often providing direct answers without encouraging deep reflection or incorporating structured pedagogical tools and strategies. This limitation is most apparent in the field of mathematics, in which AI tutoring systems remain underdeveloped. This research addresses the question: How can AI tutoring systems move beyond providing reactive assistance to enable structured, individualized, and tool-assisted learning experiences? We introduce a novel multi-agent AI tutoring platform that combines adaptive and personalized feedback, structured course generation, and textbook knowledge retrieval to enable modular, tool-assisted learning processes. This system allows students to learn new topics while identifying and targeting their weaknesses, revise for exams effectively, and practice on an unlimited number of personalized exercises. This article contributes to the field of artificial intelligence in education by introducing a novel platform that brings together pedagogical agents and AI-driven components, augmenting the field with modular and effective systems for teaching mathematics.

Alexia De Costa

This study investigates the educational potential of Flappy, a low-cost, bioinspired robotic blimp platform modeled after the motion of manta rays, as a hands-on STEM learning tool for middle school students. Building on prior research emphasizing the role of social and bioinspired robotics in education, a one-day workshop was developed to introduce ten students to fundamental concepts in physics, engineering, and computer science. Participants constructed and programmed their own robotic blimps while engaging with a custom curriculum that incorporated visuals and collaborative activities. Quantitative analysis using pre- and post-assessments revealed significant learning gains, supported by a Wilcoxon Signed-Rank Test (p = 0.00195). Qualitative observations showed high levels of engagement, teamwork, and increased confidence with technical vocabulary and tools. The results suggest that affordable, bioinspired robotics platforms like Flappy can effectively enhance STEM comprehension and enthusiasm among younger learners, particularly when paired with structured, interactive instruction.

Kaitlin Gili, Kyle Heuton, Astha Shah et al.

Advances in machine learning (ML) offer new possibilities for science education research. We report on early progress in the design of an ML-based tool to analyze students' mechanistic sensemaking, working from a coding scheme that is aligned with previous work in physics education research (PER) and amenable to recently developed ML classification strategies using language encoders. We describe pilot tests of the tool, in three versions with different language encoders, to analyze sensemaking evident in college students' written responses to brief conceptual questions. The results show, first, that the tool's measurements of sensemaking can achieve useful agreement with a human coder, and, second, that encoder design choices entail a tradeoff between accuracy and computational expense. We discuss the promise and limitations of this approach, providing examples as to how this measurement scheme may serve PER in the future. We conclude with reflections on the use of ML to support PER research, with cautious optimism for strategies of co-design between PER and ML.