Artificial Intelligence-Based Learning Objective-Oriented Formative Feedback for Students in Higher Education Based on the Instructional Design

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

In recent years, the integration of artificial intelligence (AI) into Learning Management Systems (LMS) has expanded, though many tools lack alignment with learning environment competencies, causing cognitive overload and diminishing learner motivation. This paper explores integrating an AI-based formative feedback system within an LMS, designed to enhance learner competencies and align with curriculum and learning objectives. Utilizing a self-trained large language model, the system processes predefined teaching materials to deliver efficient formative feedback, minimizing cognitive overload. Our study, involving 450 students from the University of Leipzig, assesses the impact of AI-generated feedback during lectures on learning performance, validated against specified learning objectives and competencies. Results emphasize the importance of incorporating sound pedagogical practices in developing AI tools for adaptive learning environments, demonstrating that AI-driven formative feedback can significantly enhance the efficacy of these environments and achieve educational goals.

Details

OriginalspracheEnglisch
TitelFutureproofing Engineering Education for Global Responsibility
Redakteure/-innenMichael E. Auer, Tiia Rüütmann
Herausgeber (Verlag)Springer Science and Business Media B.V.
Seiten504-513
Seitenumfang10
Band1
ISBN (elektronisch)978-3-031-85652-5
ISBN (Print)978-3-031-85651-8
PublikationsstatusVeröffentlicht - 2025
Peer-Review-StatusJa

Publikationsreihe

ReiheLecture Notes in Networks and Systems
Band1260 LNNS
ISSN2367-3370

Konferenz

Titel27th International Conference on Interactive Collaborative Learning & 53rd IGIP International Conference on Engineering Pedagogy
UntertitelFutureproofing Engineering Education for Global Responsibility
KurztitelICL 2024
Dauer24 - 27 September 2024
Webseite
OrtTallinn University of Technology
StadtTallinn
LandEstland

Schlagworte

Schlagwörter

  • Adaptive, AI, Competence Model, Feedback, Instruction, Recommender